Detecting endometrial cancer

Abstract

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Description

CROSS-REFERENCE TO RELATED APPLICATION This application is a divisional application of Australian Patent Application No. 2020211461, filed on 24 January 2020, and is related to International Patent Application No. PCT / US2020 / 015059, filed on 24 January 2020, and claims priority to U.S. Provisional Patent Application No. 62 / 796,384, filed on 24 January 2019, the disclosure of each of which is incorporated herein by reference in their entirety. SEQUENCE LISTING Preceding applications contained a sequence listing which was originally submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy is named 37456-601_ST25 and is 118,783 bytes in size. The instant application contains a sequence listing which has been submitted electronically as an XML document in the ST.26 format and is hereby incorporated by reference in its entirety. Said XML copy, created on 16 June 2026, is named “P0035581AUD1 Sequence Listing ST26” and is 532,180 bytes in size. FIELD OF INVENTION Provided herein is technology for endometrial cancer (EC) screening and particularly, but not exclusively, to methods, compositions, and related uses for detecting the presence of endometrial cancer and various subtypes of endometrial cancer. BACKGROUND Early detection approaches for endometrial cancer (EC) are lacking, despite the fact that EC is the most common gynecologic malignancy in the United States and in many other developed countries (see, Siegel, R.L., et al., Cancer statistics, 2016. CA Cancer J Clin, 2016. 66(1): p. 7-30; Parkin, D., et al., Global cancer statistics, 2002. CA Cancer J Clin. , 2005. 55(2): p. 74-108). While low-risk, early stage EC has an excellent prognosis with 5-year overall survival (OS) >95%, 5-year OS when diagnosed at stage III or IV is sobering at 68% and 17%, respectively (see, Fridley, B.L., et al., PLoS ONE, 2010. 5(9): p. e12693). Most EC are low-grade endometrioid histology and preceded by hyperplasia precursors; however, the 2026204791   22 Jun 2026 more aggressive grade 3 endometrioid, serous, clear cell, and carcinosarcoma histologies comprise 10-15% of newly diagnosed EC and can be highly lethal (see, Felix, A.S., et al., Cancer Causes Control, 2010. 21(11): p. 1851-6; Moore, K.N. and A.N. Fader, Clin Obstet Gynecol, 2011. 54(2): p. 278-91; Cancer Genome Atlas Research, N., et al., Nature, 2013. 497(7447): p. 67-73; Hussein, Y.R., et al., Int J Gynecol Pathol, 2016. 35(1): p. 16-24). Early detection increases the chance of cure (see, Mariani, A., et al., Gynecologic Oncology, 2008. 109(1): p. 11-18). Improved methods for detecting EC and various subtypes of EC are needed. Any discussion of the prior art throughout the specification should not be considered as an express or implied admission that such prior art is widely known or forms part of the common general knowledge in the field. SUMMARY Methylated DNA has been studied as a potential class of biomarkers in the tissues of most tumor types. In many instances, DNA methyltransferases add a methyl group to DNA at [TEXT CONTINUES ON PAGE 2] 2026204791   22 Jun 2026 cytosine-phosphate-guanine (CpG) island sites as an epigenetic control of gene expression. In a biologically attractive mechanism, acquired methylation events in promoter regions of tumor suppressor genes are thought to silence expression, thus contributing to oncogenesis. DNA methylation may be a more chemically and biologically stable diagnostic tool than RNA or protein expression (Laird (2010) Nat Rev Genet 11: 191-203). Furthermore, in other cancers like sporadic colon cancer, methylation markers offer excellent specificity and are more broadly informative and sensitive than are individual DNA mutations (Zou et al (2007) Cancer Epidemiol Biomarkers Prev 16: 2686-96). Analysis of CpG islands has yielded important findings when applied to animal models and human cell lines. For example, Zhang and colleagues found that amplicons from different parts of the same CpG island may have different levels of methylation (Zhang et al. (2009) PLoS Genet 5: el000438). Further, methylation levels were distributed bi-modally between highly methylated and unmethylated sequences, further supporting the binary switch-like pattern of DNA methyltransferase activity (Zhang et al. (2009) PLoS Genet 5: el 000438). Analysis of murine tissues in vivo and cell lines in vitro demonstrated that only about 0.3% of high CpG density promoters (HCP, defined as having >7% CpG sequence within a 300 base pair region) were methylated, whereas areas of low CpG density (LCP, defined as having <5% CpG sequence within a 300 base pair region) tended to be frequently methylated in a dynamic tissue-specific pattern (Meissner et al. (2008) Nature 454: 766-70). HCPs include promoters for ubiquitous housekeeping genes and highly regulated developmental genes. Among the HCP sites methylated at >50% were several established markers such as Wnt 2, NDRG2, SFRP2, and BMP3 (Meissner et al. (2008) Nature 454: 766-70). Epigenetic methylation of DNA at cytosine-phosphate-guanine (CpG) island sites by DNA methyltransferases has been studied as a potential class of biomarkers in the tissues of most tumor types. In a biologically attractive mechanism, acquired methylation events in promotor regions of tumor suppressor genes are thought to silence expression, contributing to oncogenesis. DNA methylation may be a more chemically and biologically stable diagnostic tool than RNA or protein expression. Furthermore, in other cancers like sporadic colon cancer, aberrant methylation markers are more broadly informative and sensitive than are individual DNA mutations and offer excellent specificity. Several methods are available to search for novel methylation markers. While microarray based interrogation of CpG methylation is a reasonable, high-throughput approach, this 2026204791   22 Jun 2026 strategy is biased towards known regions of interest, mainly established tumor suppressor promotors. Alternative methods for genome-wide analysis of DNA methylation have been developed in the last decade. There are three basic approaches. The first employs digestion of DNA by restriction enzymes which recognize specific methylated sites, followed by several possible analytic techniques which provide methylation data limited to the enzyme recognition site or the primers used to amplify the DNA in quantification steps (such as methylation-specific PCR; MSP). A second approach enriches methylated fractions of genomic DNA using anti-bodies directed to methyl-cytosine or other methylation-specific binding domains followed by microarray analysis or sequencing to map the fragment to a reference genome. This approach does not provide single nucleotide resolution of all methylated sites within the fragment. A third approach begins with bisulfite treatment of the DNA to convert all unmethylated cytosines to uracil, followed by restriction enzyme digestion and complete sequencing of all fragments after coupling to an adapter ligand. The choice of restriction enzymes can enrich the fragments for CpG dense regions, reducing the number of redundant sequences which may map to multiple gene positions during analysis. RRBS yields CpG methylation status data at single nucleotide resolution of 80-90% of all CpG islands and a majority of tumor suppressor promoters at medium to high read coverage. In cancer case - control studies, analysis of these reads results in the identification of differentially methylated regions (DMRs). In previous RRBS analysis of pancreatic cancer specimens, hundreds of DMRs were uncovered, many of which had never been associated with carcinogenesis and many of which were unannotated. Further validation studies on independent tissue samples sets confirmed marker CpGs which were 100% sensitive and specific in terms of performance. EC spontaneously sheds tumor cells (see, Chin, A.B., et al., American Journal of Obstetrics and Gynecology, 2000. 182(6): p. 1278-1282) and detection of EC biomarkers via minimally invasive methods is a promising approach (see, Kinde, I., et al., Science Translational Medicine, 2013. 5(167): p. 167ra4; Bakkum-Gamez, J.N., et al., Gynecologic Oncology, 2015. 137(1): p. 14-22; Wentzensen, N., et al., International Journal of Cancer, 2014. 135(8): p. 1860-1868; Fiegl H, G.C., et al., Cancer Epidemiol Biomarkers Prev, 2004. 13(5): p. 882-8); however, optimization of markers, standardization of collection methods, and improvement in specificity are needed. DNA methylation is an early event in EC carcinogenesis (see,Tao, M.H. and J.L. Freudenheim, Epigenetics, 2010. 5(6): p. 491-8); RASSF1 is methylated in morphologically normal appearing endometrium adjacent to ECs 2026204791   22 Jun 2026 (see, Fiegl H, G.C., et al., Cancer Epidemiol Biomarkers Prev, 2004. 13(5): p. 882-8; Pijnenborg, J., et al., Annals of Oncology, 2007. 18(3): p. 491-497; Suehiro, Y., et al., Clinical Cancer Research, 2008. 14(11): p. 3354-3361; Arafa, M., et al., Histopathology, 2008. 53(5): p. 525-532); MLH1 methylation occurs in atypical hyperplasia (see, Suehiro, Y., et al., Clinical Cancer Research, 2008. 14(11): p. 3354-3361; Horowitz, N., et al., Gynecologic Oncology, 2002. 86(1): p. 62-68; Xiong, Y., et al., Gynecologic Oncology, 2006. 103(1): p. 321-328; Banno K, Y.M., et al., Oncol Rep, 2006. 16(6): p. 1189-96; Zighelboim, I., et al., Clinical Cancer Research, 2007. 13(10): p. 2882-2889; Guida M, S.F., et al., Eur J Gynaecol Oncol., 2009. 30(3): p. 267-70). These and other genes are established as methylated in EC (see, Fiegl H, G.C., et al., Cancer Epidemiol Biomarkers Prev, 2004. 13(5): p. 882-8; Suehiro, Y., et al., Clinical Cancer Research, 2008. 14(11): p. 3354-3361; Zighelboim, I., et al., Clinical Cancer Research, 2007. 13(10): p. 2882-2889; Wentzensen, N., et al., International Journal of Cancer, 2014: p. [Epub ahead of print]; Tao MH, F.J., DNA methylation in EC. Epigenetics, 2010. 5(6): p. 491-8; Integrated genomic characterization of endometrial carcinoma. Nature, 2013. 497(7447): p. 67-73; Huang, Y.-W., et al., Gynecologic Oncology, 2010. 117(2): p. 239-247; Xiong, Y., et al., Gynecologic Oncology, 2005. 99(1): p. 135-141; Sasaki, M., et al., Cancer Research, 2001. 61(1): p. 97-102; Sasaki, M., et al., Molecular and Cellular Endocrinology, 2003. 202(1-2): p. 201-207) and cell-free methylated DNA released from necrotic tumor cells is an attractive target and has been detected in a variety of biological fluids, including sputum, plasma, peritoneal fluid, stool, nipple aspirates, urine, pancreatic juice, and vaginal fluid (see, Bakkum-Gamez, J.N., et al., Gynecologic Oncology, 2015. 137(1): p. 14-22; Fiegl H, G.C., et al., Cancer Epidemiol Biomarkers Prev, 2004. 13(5): p. 882-8; Duffy MJ, N.R., et al., Eur J Cancer, 2009. 45(3): p. 335-46; Ahlquist, D.A., et al., Gastroenterology, 2012. 142(2): p. 248-256; Duffy, M.J., et al., Eur J Cancer, 2009. 45(3): p. 335-46; Kisiel, J.B., et al., Clinical Cancer Research, 2015. 21(19): p. 4473-4481). Provided herein is technology for EC screening and particularly, but not exclusively, to methods, compositions, and related uses for detecting the presence of EC and various subtypes of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC). Indeed, as described in Examples I, II and III, experiments conducted during the course for identifying embodiments for the present invention identified a novel set of differentially methylated regions (DMRs) for discriminating cancer of the endometrium derived DNA from non-neoplastic control DNA. 2026204791   22 Jun 2026 Such experiments list and describe 499 novel DNA methylation markers distinguishing EC tissue (and various subtypes of EC tissue) from benign endometrial tissue (see, Tables 1, 8, and 21, Examples 1, 2 and 3). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers capable of distinguishing EC tissue from benign endometrial tissue: • AFF3, AIM1A, AMIGO3_A, BMP4_B, C17orfl07_A, Clorf70_B, C5orf52, CLDN7, DIDO 1 A, EEF1A2, EMX2OS, FEV, FKBP11A, GDF6, GDF7 A, JSRP1A, KCTD15 A, KLHL21, LRRC8D A, NBPF8, MAX.chrl0.130339363-130339534, MAX.chrl0.22624479-22624553, MAX.chrl4.103021656-103021718, MAX.chr8.145103829-145103992, MAX.chr8.145104263-145104422, MDFI B, MIAT A, MMP23B, NDRG2, OBSCNA, PCOLCE, PYCARD, SEPT9B, SLC6A3A, SLC8A3B, SQSTM1, VILL, ZNF302, ZNF323_A, ZNF506, and ZNF90 (see, Table 2, Example 1); • EMX2OS, CYTH2, C17orfl07_A, DIDO1A, GDF6, NBPF8, MAX.chrl4.103021656-103021718, JSRP1_A, GATA2B, and SFMBT2B (see, Table 3, Example 1); • SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO1A, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ A (see, Table 15, Example 1); and • EMX2OS, CYTH2, NBPF8, MAX.chrl0.22624479-22624553 (see, Table 20, Example 1). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers for detecting EC in blood samples (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples): • ANKRD35, ARL5C, ARRB1, BCL2L11A, BCL2L11B, BCL2L11C, BZRAP1, C16orf54, C17orfl01, C6orfl32, CACNA2D4, DEDD2, EPS15L1, FAIM2, FAM125B, FAM189B, FAM78A, FOXP4, GYPC A, GYPC B, IFFO1 A, IFFO1B, ITPKA, KLF16, LIMD2, LOC389333, LOC440925_A, LOC646278, LYL1, LYPLAL1, MAX.chrl 1.32355226-32355251, MAX.chrl4.102172621-102172686, MAX.chrl4.105512122-105512239, MAX.chrl5.95128144-95128248, MAX.chrl6.11327016-11327312, 2026204791   22 Jun 2026 MAX.chr3.187676577-187676668, MAX. chr4.174430676-174430847, MAX.chr8.145900783-145900914, MAX.chr8.80804237-80804301, N4BP3, NCOR2, NFATC1_A, NFATC1B, NKX2-6, NR2F6, OSM, PALLD C, PIK3CD, PRKAR1B, RAD52, STX16 A, SUCLG2, TNFRSF1B, TNFRSF4, ZDHHC18, and ZNF671A (see, Table 9, Example 1). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers capable of distinguishing clear cell EC tissue from benign endometrial tissue: • DIDO1A, NDRG4, MAX.chrl4.103021656-103021718, MMP23B, EMX2OS, SEPT9B, NBPF8, EEF1A2, AIM1A, BMP4 B, MAX.chr8.145103829-145103992, OBSCN, PYCARD, GDF6, MDFI B, MIAT A, SCL8A3, ZNF323 A, SQSTM1, AFF3, Clorf70, GDF7 A, JSRP1A, LRRC8D A, FEV, and MAX.chr8.145104263-145104422 (see, Table 4, Example 1); • ZNF323_A, MAX.chr7.104624356-104624730, NDRG2, DIDO1_A, MDFI B, MAX.chrl4.103021656-103021718, MMP23B, SEPT9_B, and STX16_A (see, Table 11, Example 1); • SFMBT2B, SQSTM1, ZNF323_A, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO 1 A, MDFI B, GDF7 A, MAX.chrl0.22624479-22624553, JSRP1_A, MAX.chrl4.103021656-103021718, EMX2OS, LRRC34, NBPF8, SEPT9 B, EEF1A2, LRRC41C, VILL, and MPZ A (see, Table 16, Example 1); and • MAX.chr7:104624386-104624529, EMX2OS, DIDO1_B, and OBSCN_B (see, Table 24, Example 3). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers for detecting clear cell EC in blood samples (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples): • SFMBT2 B, SQSTM1, ZNF323 A, ZNF506, ZNF90, CLDN7, LRRC41B, MAX. chr7.104624356-104624730, NDRG2, CYP11A1, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN A, DIDO 1 A, GDF6, MAX.chrlO.130339363-130339534, MDFI B, DLL4, GDF7 A, MIAT A, PYCARD, BMP4 B, JSRP1A, 2026204791   22 Jun 2026 MAX. chrl4.103021656-103021718, EMX2, MMP23B, EMX2OS, MAX. chrl7.73073716-73073814, NBPF8, SEPT9_B, LOC440925_A, STX16_A, ITPKA, EEF1A2, FEV, LRRC41C, and NFIC. From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers capable of distinguishing carcinosarcoma EC tissue from benign endometrial tissue: • EMX2OS, DIDO 1 A, SBNO2, AMIGO3A, PCOLCE, CLDN7, CYTH2, OBSCN A, AHSA2, DLL4, EMX2, MAX.chrl4.74100620-74100870, LRRC4, PPP2R5CA, SQSTM1, MAX.chrl7.73073716-73073814, CYP11A1, ACOXL A, and AIM1B (see, Table 5, Example 1); • EMX2OS, and LRRC34 (see, Table 13, Example 1); • ZNF506, ZNF90, MAX. chr8.145103 829-145103 992, LRRC8D A, OBSCN_A, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and VILL (see, Table 18, Example 1); and • TRH, MAX.chr7:104624386-104624529, EMX2OS, DIDO1_B, and ST3GAL2B (see, Table 24, Example 3). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers for detecting carcinosarcoma EC in blood samples (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples): • SFMBT2 B, SMTN, ZNF506, ZNF90, CLDN7, LRRC41B, CYP11 Al, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2 B, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, DLL4, MAX.chrl0.22624479-22624553, PYCARD, BMP4_B, JSRP1_A, MAX.chrl4.103021656-103021718, MIAT B, EMX2OS, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, and VILL (see, Table 13, Example 1). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers capable of distinguishing serous EC tissue from benign endometrial tissue: 2026204791   22 Jun 2026 • EMX2OS, KANKI, Clorf70_B, AMIGO3A, DIDO 1 A, LRRC41C, NFIC, FKBP11A, C17orfl07_A, SMTN, LRRC41B, LRRC8DA, OBSCNA, MAX.chr7.104624356-104624730, MIATB (see, Table 7, Example 1); • MAX.chr7.104624356-104624730, EMX2OS, and LRRC41_C (see, Table 12, Example 1); • MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN_A, DIDO1A, EMX2OS, LRRC41C, and VILL (see, Table 17, Example 1); and • EMX2OS, and LRRC41D (see, Table 24, Example 3). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers for detecting serous EC in blood samples (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples): • SFMBT2B, SMTN, SQSTM1, ZNF90, CLDN7, LRRC41B, MAX.chr7.104624356-104624730, CYP11A1, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, GDGF6, DLL4, PYCARD, BMP4 B, JSRP1A, MIAT B, KANKI, EMX2OS, NBPF8, LOC440925_A, ITPKA, EEF1A2, FEV, LRRC41C, NFIC, VILL, MPZ_A (see, Table 12, Example 1). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers capable of distinguishing endometrioid EC tissue from benign endometrial tissue: • MAX.chrlO.130339363-130339534, SFMBT2_C, CYTH2, SLC6A3, VILL, EMX2OS, MAX.chrlO.22624479-22624553, GDF6, ZNF90, ZNF506, JSRP1_A, c5orf52, SFMBT2 B, NBPF8, RHBDL1A, DIDO 1 A, KANKI, and GATA2 B (see, Table 6, Example 1); • MAX.chr8.145103829-145103992, CYTH2, DIDO1_A, MAX.chrl0.22624479-22624553, JSRP1_A, SBNO2, NBPF8, and VILL (see, Table 14, Example 1); and • SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, MAX.chr8.145104263-145104422, OBSCN_A, MAX.chrlO.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ_A (see, Table 19, Example 1). 2026204791   22 Jun 2026 From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers for detecting endometrioid EC in blood samples (e.g., plasma samples, whole blood samples, leukocyte samples, serum samples): • SFMBT2B, SMTN, SQSTM1, ZNF506, ZNF90, CLDN7, LRRC41B, FKBP11A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2B, LRRC8DA, MAX.chr8.145104263-145104422, DIDO1_A, GDF6, MAX.chrlO. 130339363-130339534, DLL4, MAX.chrl 0.22624479-22624553, MIAT A, PYCARD, BMP4_B, JSRP1_A, MAX.chrl4.103021656-103021718, MIAT B, KANKI, SBNO2, c5orf52, EMX2O6, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, VILL, and MPZ_A (see, Table 14, Example 1). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers capable of distinguishing endometrioid EC Grade 1 tissue from benign endometrial tissue: • TSPYL5, TRH, JAM3, FAM19A5, PTGDR, SFMBT2 E, JSRP1B, and ARL5C (see, Table 25, Example 3). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers capable of distinguishing endometrioid EC Grade 2 tissue from benign endometrial tissue: • TSPYL5, MPZ_B, TRH, CNTN4, FAM19A5, GLT1D1, RYR2F, PTGDR, EMX2OS, MAX.chrl0:22624470-22624553, SPDYA B, SFMBT2 E, and JSRP1B (see, Table 25, Example 3). From these 499 novel DNA methylation markers, further experiments identified the following markers and / or panels of markers capable of distinguishing endometrioid EC Grade 3 tissue from benign endometrial tissue: • TSPYL5, MPZ B, TRH, and PTGDR (see, Table 25, Example 3). As described herein, the technology provides a number of methylated DNA markers and subsets thereof (e.g., sets of 2, 3, 4, 5, 6, 7, or 8 markers) with high discrimination for EC overall and various types of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, 2026204791   22 Jun 2026 serous EC). Experiments applied a selection filter to candidate markers to identify markers that provide a high signal to noise ratio and a low background level to provide high specificity for purposes of EC screening or diagnosis. In some embodiments, the technology is related to assessing the presence of and methylation state of one or more of the markers identified herein in a biological sample (e.g., endometrial tissue sample, blood sample). These markers comprise one or more differentially methylated regions (DMR) as discussed herein, e.g., as provided in Tables 1, 8 and 21. Methylation state is assessed in embodiments of the technology. As such, the technology provided herein is not restricted in the method by which a gene’s methylation state is measured. For example, in some embodiments the methylation state is measured by a genome scanning method. For example, one method involves restriction landmark genomic scanning (Kawai et al. (1994) Mol. Cell. Biol. 14: 7421-7427) and another example involves methylation-sensitive arbitrarily primed PCR (Gonzalgo et al. (1997) Cancer Res. 57: 594599). In some embodiments, changes in methylation patterns at specific CpG sites are monitored by digestion of genomic DNA with methylation-sensitive restriction enzymes followed by Southern analysis of the regions of interest (digestion-Southern method). In some embodiments, analyzing changes in methylation patterns involves a PCR-based process that involves digestion of genomic DNA with methylation-sensitive restriction enzymes or methylation-dependent restriction enzymes prior to PCR amplification (Singer-Sam et al. (1990) Nucl. Acids Res. 18: 687). In addition, other techniques have been reported that utilize bisulfite treatment of DNA as a starting point for methylation analysis. These include methylation-specific PCR (MSP) (Herman et al. (1992) Proc. Natl. Acad. Sci. USA 93: 98219826) and restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA (Sadri and Hornsby (1996) Nucl. Acids Res. 24: 5058-5059; and Xiong and Laird (1997) Nucl. Acids Res. 25: 2532-2534). PCR techniques have been developed for detection of gene mutations (Kuppuswamy et al. (1991) Proc. Natl. Acad. Sci. USA 88: 1143-1147) and quantification of allelic-specific expression (Szabo and Mann (1995) Genes Dev. 9: 3097-3108; and Singer-Sam et al. (1992) PCR Methods Appl. 1: 160-163). Such techniques use internal primers, which anneal to a PCR-generated template and terminate immediately 5' of the single nucleotide to be assayed. Methods using a “quantitative Ms-SNuPE assay” as described in U.S. Pat. No. 7,037,650 are used in some embodiments. Upon evaluating a methylation state, the methylation state is often expressed as the fraction or percentage of individual strands of DNA that is methylated at a particular site 2026204791   22 Jun 2026 (e.g., at a single nucleotide, at a particular region or locus, at a longer sequence of interest, e.g., up to a ~100-bp, 200-bp, 500-bp, 1000-bp subsequence of a DNA or longer) relative to the total population of DNA in the sample comprising that particular site. Traditionally, the amount of the unmethylated nucleic acid is determined by PCR using calibrators. Then, a known amount of DNA is bisulfite treated and the resulting methylation-specific sequence is determined using either a real-time PCR or other exponential amplification, e.g., a QuARTS assay (e.g., as provided by U.S. Pat. No. 8,361,720; and U.S. Pat. Appl. Pub. Nos. 2012 / 0122088 and 2012 / 0122106, incorporated herein by reference). For example, in some embodiments methods comprise generating a standard curve for the unmethylated target by using external standards. The standard curve is constructed from at least two points and relates the real-time Ct value for unmethylated DNA to known quantitative standards. Then, a second standard curve for the methylated target is constructed from at least two points and external standards. This second standard curve relates the Ct for methylated DNA to known quantitative standards. Next, the test sample Ct values are determined for the methylated and unmethylated populations and the genomic equivalents of DNA are calculated from the standard curves produced by the first two steps. The percentage of methylation at the site of interest is calculated from the amount of methylated DNAs relative to the total amount of DNAs in the population, e.g., (number of methylated DNAs) / (the number of methylated DNAs + number of unmethylated DNAs) * 100. Also provided herein are compositions and kits for practicing the methods. For example, in some embodiments, reagents (e.g., primers, probes) specific for one or more markers are provided alone or in sets (e.g., sets of primers pairs for amplifying a plurality of markers). Additional reagents for conducting a detection assay may also be provided (e.g., enzymes, buffers, positive and negative controls for conducting QuARTS, PCR, sequencing, bisulfite, or other assays). In some embodiments, the kits contain a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent). In some embodiments, the kits containing one or more reagent necessary, sufficient, or useful for conducting a method are provided. Also provided are reactions mixtures containing the reagents. Further provided are master mix reagent sets containing a plurality of reagents that may be added to each other and / or to a test sample to complete a reaction mixture. In some embodiments, the technology described herein is associated with a programmable machine designed to perform a sequence of arithmetic or logical operations as 2026204791   22 Jun 2026 provided by the methods described herein. For example, some embodiments of the technology are associated with (e.g., implemented in) computer software and / or computer hardware. In one aspect, the technology relates to a computer comprising a form of memory, an element for performing arithmetic and logical operations, and a processing element (e.g., a microprocessor) for executing a series of instructions (e.g., a method as provided herein) to read, manipulate, and store data. In some embodiments, a microprocessor is part of a system for determining a methylation state (e.g., of one or more DMR, e.g., DMR 1-499 as provided in Tables 1, 8 and 21); comparing methylation states (e.g., of one or more DMR, e.g., DMR 1-499 as provided in Tables 1, 8 and 21); generating standard curves; determining a Ct value; calculating a fraction, frequency, or percentage of methylation (e.g., of one or more DMR, e.g., DMR 1-499 as provided in Tables 1, 8 and 21); identifying a CpG island; determining a specificity and / or sensitivity of an assay or marker; calculating an ROC curve and an associated AUC; sequence analysis; all as described herein or is known in the art. In some embodiments, a microprocessor or computer uses methylation state data in an algorithm to predict a site of a cancer. In some embodiments, a software or hardware component receives the results of multiple assays and determines a single value result to report to a user that indicates a cancer risk based on the results of the multiple assays (e.g., determining the methylation state of multiple DMR, e.g., as provided in Tables 2, 18 and 26). Related embodiments calculate a risk factor based on a mathematical combination (e.g., a weighted combination, a linear combination) of the results from multiple assays, e.g., determining the methylation states of multiple markers (such as multiple DMR, e.g., as provided in Tables 1, 8 and 21). In some embodiments, the methylation state of a DMR defines a dimension and may have values in a multidimensional space and the coordinate defined by the methylation states of multiple DMR is a result, e.g., to report to a user, e.g., related to a cancer risk. Some embodiments comprise a storage medium and memory components. Memory components (e.g., volatile and / or nonvolatile memory) find use in storing instructions (e.g., an embodiment of a process as provided herein) and / or data (e.g., a work piece such as methylation measurements, sequences, and statistical descriptions associated therewith). Some embodiments relate to systems also comprising one or more of a CPU, a graphics card, and a user interface (e.g., comprising an output device such as display and an input device such as a keyboard). 2026204791   22 Jun 2026 Programmable machines associated with the technology comprise conventional extant technologies and technologies in development or yet to be developed (e.g., a quantum computer, a chemical computer, a DNA computer, an optical computer, a spintronics based computer, etc.). In some embodiments, the technology comprises a wired (e.g., metallic cable, fiber optic) or wireless transmission medium for transmitting data. For example, some embodiments relate to data transmission over a network (e.g., a local area network (LAN), a wide area network (WAN), an ad-hoc network, the internet, etc.). In some embodiments, programmable machines are present on such a network as peers and in some embodiments the programmable machines have a client / server relationship. In some embodiments, data are stored on a computer-readable storage medium such as a hard disk, flash memory, optical media, a floppy disk, etc. In some embodiments, the technology provided herein is associated with a plurality of programmable devices that operate in concert to perform a method as described herein. For example, in some embodiments, a plurality of computers (e.g., connected by a network) may work in parallel to collect and process data, e.g., in an implementation of cluster computing or grid computing or some other distributed computer architecture that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a network (private, public, or the internet) by a conventional network interface, such as Ethernet, fiber optic, or by a wireless network technology. For example, some embodiments provide a computer that includes a computer-readable medium. The embodiment includes a random access memory (RAM) coupled to a processor. The processor executes computer-executable program instructions stored in memory. Such processors may include a microprocessor, an ASIC, a state machine, or other processor, and can be any of a number of computer processors, such as processors from Intel Corporation of Santa Clara, California and Motorola Corporation of Schaumburg, Illinois. Such processors include, or may be in communication with, media, for example computer-readable media, which stores instructions that, when executed by the processor, cause the processor to perform the steps described herein. Embodiments of computer-readable media include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor with computer-readable instructions. Other examples of suitable media include, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, 2026204791   22 Jun 2026 an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. The instructions may comprise code from any suitable computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, and JavaScript. Computers are connected in some embodiments to a network. Computers may also include a number of external or internal devices such as a mouse, a CD-ROM, DVD, a keyboard, a display, or other input or output devices. Examples of computers are personal computers, digital assistants, personal digital assistants, cellular phones, mobile phones, smart phones, pagers, digital tablets, laptop computers, internet appliances, and other processor-based devices. In general, the computers related to aspects of the technology provided herein may be any type of processor-based platform that operates on any operating system, such as Microsoft Windows, Linux, UNIX, Mac OS X, etc., capable of supporting one or more programs comprising the technology provided herein. Some embodiments comprise a personal computer executing other application programs (e.g., applications). The applications can be contained in memory and can include, for example, a word processing application, a spreadsheet application, an email application, an instant messenger application, a presentation application, an Internet browser application, a calendar / organizer application, and any other application capable of being executed by a client device. All such components, computers, and systems described herein as associated with the technology may be logical or virtual. Accordingly, provided herein is technology related to a method of screening for EC and / or various forms of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC) in a sample obtained from a subject, the method comprising assaying a methylation state of a marker in a sample obtained from a subject (e.g., endometrial tissue) (e.g., a blood sample) and identifying the subject as having EC and / or a specific form of EC when the methylation state of the marker is different than a methylation state of the marker assayed in a subject that does not have EC, wherein the marker comprises a base in a differentially methylated region (DMR) selected from a group consisting of DMR 1-499 as provided in Tables 1, 8 and 21. 2026204791   22 Jun 2026 In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has EC: AFF3, AIM1_A, AMIGO3_A, BMP4_B, C17orfl07_A, Clorf70_B, C5orf52, CLDN7, DIDO 1 A, EEF1A2, EMX2OS, FEV, FKBP11A, GDF6, GDF7A, JSRP1A, KCTD15A, KLHL21, LRRC8DA, NBPF8, MAX.chrl0.130339363-130339534, MAX.chrl0.22624479-22624553, MAX.chrl4.103021656-103021718, MAX. chr8.145103829-145103992, MAX.chr8.145104263-145104422, MDFI B, MIAT A, MMP23B, NDRG2, OBSCNA, PCOLCE, PYCARD, SEPT9B, SLC6A3A, SLC8A3_B, SQSTM1, VILL, ZNF302, ZNF323_A, ZNF506, and ZNF90 (see, Table 2, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has EC: EMX2OS, CYTH2, C17orfl07_A, DIDO 1 A, GDF6, NBPF8, MAX.chrl4.103021656-103021718, JSRP1_A, GATA2B, and SFMBT2B (see, Table 3, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has EC: SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO 1 A, MAX.chrl0.22624479-22624553, JSRP1A, EMX2OS, NBPF8, and MPZ A (see, Table 15, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has EC: EMX2OS, CYTH2, NBPF8, MAX.chrl0.22624479-22624553 (see, Table 20, Example 1). In some embodiments wherein the sample obtained from the subject is a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample) and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has EC: ANKRD35, ARL5C, ARRB1, BCL2L11A, BCL2L11B, BCL2L11C, BZRAP1, C16orf54, C17orfl01, C6orfl32, CACNA2D4, DEDD2, EPS15L1, FAIM2, FAM125B, 2026204791   22 Jun 2026 FAM189B, FAM78A, FOXP4, GYPCA, GYPC B, IFFO1A, IFFO1B, ITPKA, KLF16, LIMD2, LOC389333, LOC440925_A, LOC646278, LYL1, LYPLAL1, MAX.chrl 1.32355226-32355251, MAX.chrl4.102172621-102172686, MAX.chrl4.105512122-105512239, MAX.chrl5.95128144-95128248, MAXchrl6.11327016-11327312, MAX.chr3.187676577-187676668, MAX.chr4.174430676-174430847, MAX.chr8.145900783-145900914, MAX. chr8.80804237-80804301, N4BP3, NCOR2, NFATC1A, NFATC1B, NKX2-6, NR2F6, OSM, PALLD C, PIK3CD, PRKAR1B, RAD52, STX16 A, SUCLG2, TNFRSF1B, TNFRSF4, ZDHHC18, and ZNF671A (see, Table 9, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has clear cell EC: DIDO1_A, NDRG4, MAX.chrl4.103021656-103021718, MMP23B, EMX2OS, SEPT9 B, NBPF8, EEF1A2, AIM1A, BMP4 B, MAX.chr8.145103829-145103992, OBSCN, PYCARD, GDF6, MDFI B, MIAT A, SCL8A3, ZNF323 A, SQSTM1, AFF3, Clorf70, GDF7 A, JSRP1A, LRRC8D A, FEV, and MAX.chr8.145104263-145104422 (see, Table 4, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has clear cell EC: ZNF323_A, MAX.chr7.104624356-104624730, NDRG2, DIDO 1 A, MDFI B, MAX.chrl4.103021656-103021718, MMP23B, SEPT9 B, and STX16 A (see, Table 11, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has clear cell EC: SFMBT2B, SQSTM1, ZNF323_A, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO1A, MDFI B, GDF7 A, MAX.chrl0.22624479-22624553, JSRP1_A, MAX.chr!4.103021656-103021718, EMX2OS, LRRC34, NBPF8, SEPT9 B, EEF1A2, LRRC41C, VILL, and MPZ A (see, Table 16, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a 2026204791   22 Jun 2026 methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has clear cell EC: MAX. chr7:104624386-104624529, EMX2OS, DIDO1B, and OBSCN B (see, Table 24, Example 3). In some embodiments wherein the sample obtained from the subject is a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample) and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has clear cell EC: SFMBT2B, SQSTM1, ZNF323_A, ZNF506, ZNF90, CLDN7, LRRC41B, MAX. chr7.104624356-104624730, NDRG2, CYP11A1, MAX. chr8.145103 829-145103 992, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCNA, DIDO1_A, GDF6, MAX.chrlO. 130339363-130339534, MDFI B, DLL4, GDF7 A, MIAT A, PYCARD, BMP4 B, JSRP1A, MAX.chrl4.103021656-103021718, EMX2, MMP23B, EMX2OS, MAX.chrl7.73073716-73073814, NBPF8, SEPT9B, LOC440925_A, STX16 A, ITPKA, EEF1A2, FEV, LRRC41C, andNFIC. In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has carcinosarcoma EC: EMX2OS, DIDO1A, SBNO2, AMIGO3 A, PCOLCE, CLDN7, CYTH2, OBSCN A, AHSA2, DLL4, EMX2, MAX.chr14.74100620-74100870, LRRC4, PPP2R5C_A, SQSTM1, MAX.chrl7.73073716-73073814, CYP11A1, ACOXL A, and AIM1B (see, Table 5, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has carcinosarcoma EC: EMX2OS, and LRRC34 (see, Table 13, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has carcinosarcoma EC: ZNF506, ZNF90, MAX.chr8.145103829-145103992, LRRC8D A, OBSCN_A, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and VILL (see, Table 18, Example 1). 2026204791   22 Jun 2026 In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has carcinosarcoma EC: TRH, MAX. chr7:104624386-104624529, EMX2OS, DIDO I B, and ST3GAL2B (see, Table 24, Example 3). In some embodiments wherein the sample obtained from the subject is a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample) and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has carcinosarcoma EC: SFMBT2B, SMTN, ZNF506, ZNF90, CLDN7, LRRC41_B, CYP11A1, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2B, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, DLL4, MAXchrl0.22624479-22624553, PYCARD, BMP4_B, JSRP1_A, MAX.chr!4.103021656-103021718, MIAT B, EMX2OS, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, and VILL (see, Table 13, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has serous EC: EMX2OS, KANKI, Clorf70_B, AMIGO3_A, DIDO 1 A, LRRC41C, NFIC, FKBP11A, C17orfl07_A, SMTN, LRRC41B, LRRC8D A, OBSCNA, MAX.chr7.104624356-104624730, MIAT B (see, Table 7, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has serous EC: MAX. chr7.104624356-104624730, EMX2OS, and LRRC41C (see, Table 12, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has serous EC: MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO 1 A, EMX2OS, LRRC41C, and VILL (see, Table 17, Example 1). 2026204791   22 Jun 2026 In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has serous EC: EMX2OS, and LRRC41D (see, Table 24, Example 3). In some embodiments wherein the sample obtained from the subject is a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample) and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has serous EC: SFMBT2B, SMTN, SQSTM1, ZNF90, CLDN7, LRRC41B, MAX.chr7.104624356-104624730, CYP11A1, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, GDGF6, DLL4, PYCARD, BMP4B, JSRP1A, MIATB, KANKI, EMX2OS, NBPF8, LOC440925_A, ITPKA, EEF1A2, FEV, LRRC41C, NFIC, VILL, MPZ_A (see, Table 12, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has endometrioid EC: MAX.chrlO.130339363-130339534, SFMBT2_C, CYTH2, SLC6A3, VILL, EMX2OS, MAX.chrl0.22624479-22624553, GDF6, ZNF90, ZNF506, JSRP1A, c5orf52, SFMBT2 B, NBPF8, RHBDL1A, DIDO 1 A, KANKI, and GATA2 B (see, Table 6, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has endometrioid EC: MAX.chr8.145103829-145103992, CYTH2, DIDO1A, MAX.chrl0.22624479-22624553, JSRP1_A, SBNO2, NBPF8, and VILL (see, Table 14, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has endometrioid EC: SFMBT2_B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, MAX.chr8.145104263-145104422, OBSCN_A, 2026204791   22 Jun 2026 MAX. chrl 0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ_A (see, Table 19, Example 1). In some embodiments wherein the sample obtained from the subject is a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample) and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has endometrioid EC: SFMBT2B, SMTN, SQSTM1, ZNF506, ZNF90, CLDN7, LRRC41B, FKBP11A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2 B, LRRC8D A, MAX.chr8.145104263-145104422, DIDO1_A, GDF6, MAXchrl0.130339363-130339534, DLL4, MAX.chrl0.22624479-22624553, MIAT A, PYCARD, BMP4 B, JSRP1_A, MAX.chrl4.103021656-103021718, MIAT B, KANKI, SBNO2, c5orf52, EMX2O6, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, VILL, and MPZ A (see, Table 14, Example 1). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has endometrioid Grade 1 EC: TSPYL5, TRH, JAM3, FAM19A5, PTGDR, SFMBT2E, JSRP1B, and ARL5C (see, Table 25, Example 3). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has endometrioid Grade 2 EC: TSPYL5, MPZ B, TRH, CNTN4, FAM19A5, GLT1D1, RYR2 F, PTGDR, EMX2OS, MAX.chrl0:22624470-22624553, SPDYAB, SFMBT2E, and JSRP1_B (see, Table 25, Example 3). In some embodiments wherein the sample obtained from the subject is endometrial tissue and the methylation state of one or more of the following markers is different than a methylation state of the one or more markers assayed in a subject that does not have EC indicates the subject has endometrioid Grade 3 EC: TSPYL5, MPZ B, TRH, and PTGDR (see, Table 25, Example 3). The technology is related to identifying and discriminating EC and / or various forms of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC). Some embodiments provide methods comprising assaying a plurality of markers, e.g., comprising assaying 2 to 11 to 100 or 120 or 499 markers. 2026204791   22 Jun 2026 The technology is not limited in the methylation state assessed. In some embodiments assessing the methylation state of the marker in the sample comprises determining the methylation state of one base. In some embodiments, assaying the methylation state of the marker in the sample comprises determining the extent of methylation at a plurality of bases. Moreover, in some embodiments the methylation state of the marker comprises an increased methylation of the marker relative to a normal methylation state of the marker. In some embodiments, the methylation state of the marker comprises a decreased methylation of the marker relative to a normal methylation state of the marker. In some embodiments the methylation state of the marker comprises a different pattern of methylation of the marker relative to a normal methylation state of the marker. Furthermore, in some embodiments the marker is a region of 100 or fewer bases, the marker is a region of 500 or fewer bases, the marker is a region of 1000 or fewer bases, the marker is a region of 5000 or fewer bases, or, in some embodiments, the marker is one base. In some embodiments the marker is in a high CpG density promoter. The technology is not limited by sample type. For example, in some embodiments the sample is a stool sample, a tissue sample (e.g., endometrial tissue sample), a blood sample (e.g., plasma, leukocyte, serum, whole blood), an excretion, or a urine sample. Furthermore, the technology is not limited in the method used to determine methylation state. In some embodiments the assaying comprises using methylation specific polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation specific nuclease, mass-based separation, or target capture. In some embodiments, the assaying comprises use of a methylation specific oligonucleotide. In some embodiments, the technology uses massively parallel sequencing (e.g., next-generation sequencing) to determine methylation state, e.g., sequencing-by-synthesis, real-time (e.g., single-molecule) sequencing, bead emulsion sequencing, nanopore sequencing, etc. The technology provides reagents for detecting a DMR, e.g., in some embodiments are provided a set of oligonucleotides comprising the sequences provided by SEQ ID NO: 1499 (see, Tables 1, 8 and 21). In some embodiments are provided an oligonucleotide comprising a sequence complementary to a chromosomal region having a base in a DMR, e.g., an oligonucleotide sensitive to methylation state of a DMR. The technology provides various panels of markers use for identifying EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is AFF3, AIM1A, AMIG03 A, BMP4 B, C17orfl07_A, Clorf70_B, C5orf52, CLDN7, 2026204791   22 Jun 2026 DIDO 1 A, EEF1A2, EMX2OS, FEV, FKBP11A, GDF6, GDF7A, JSRP1A, KCTD15A, KLHL21, LRRC8DA, NBPF8, MAX.chrlO. 130339363-130339534, MAX.chrl0.22624479-22624553, MAX.chrl4.103021656-103021718, MAX. chr8.145103829-145103992, MAX.chr8.145104263-145104422, MDFIB, MIAT A, MMP23B, NDRG2, OBSCN A, PCOLCE, PYCARD, SEPT9 B, SLC6A3 A, SLC8A3_B, SQSTM1, VILL, ZNF302, ZNF323_A, ZNF506, and ZNF90 (see, Table 2, Example 1). The technology provides various panels of markers use for identifying EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is EMX2OS, CYTH2, C17orfl07_A, DIDO1_A, GDF6, NBPF8, MAX.chrl4.103021656-103021718, JSRP1A, GATA2 B, and SFMBT2 B (see, Table 3, Example 1). The technology provides various panels of markers use for identifying EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN_A, DIDO1A, MAX.chrlO.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ_A (see, Table 15, Example 1) The technology provides various panels of markers use for identifying EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is EMX2OS, CYTH2, NBPF8, MAX.chrl0.22624479-22624553 (see, Table 20, Example 1). The technology provides various panels of markers use for identifying EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is ANKRD35, ARL5C, ARRB1, BCL2L11A, BCL2L11B, BCL2L11C, BZRAP1, C16orf54, C17orfl01, C6orfl32, CACNA2D4, DEDD2, EPS15L1, FAIM2, FAM125B, FAM189B, FAM78A, FOXP4, GYPC A, GYPC B, IFFO1A, IFFO1B, ITPKA, KLF16, LIMD2, LOC389333, LOC440925_A, LOC646278, LYL1, LYPLAL1, MAXchrl 1.32355226-32355251, MAX.chrl4.102172621-102172686, MAX.chrl4.105512122-105512239, MAX.chrl5.95128144-95128248, MAXchrl6.11327016-11327312, MAX.chr3.187676577-187676668, MAX. chr4.174430676-174430847, MAX.chr8.145900783-145900914, MAX.chr8.80804237-80804301, N4BP3, NCOR2, NFATC1A, NFATC1B, NKX2-6, NR2F6, OSM, PALLD C, PIK3CD, PRKAR1B, RAD52, STX16 A, SUCLG2, TNFRSF1B, TNFRSF4, ZDHHC18, and ZNF671A (see, Table 9, Example 1). The technology provides various panels of markers use for identifying clear cell EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation 2026204791   22 Jun 2026 that is DIDO1A, NDRG4, MAX. chrl4.103021656-103021718, MMP23B, EMX2OS, SEPT9B, NBPF8, EEF1A2, AIM1_A, BMP4_B, MAX.chr8.145103829-145103992, OBSCN, PYCARD, GDF6, MDFIB, MIATA, SCL8A3, ZNF323_A, SQSTM1, AFF3, Clorf70, GDF7A, JSRP1_A, LRRC8DA, FEV, and MAX.chr8.145104263-145104422 (see, Table 4, Example 1). The technology provides various panels of markers use for identifying clear cell EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is ZNF323_A, MAX.chr7.104624356-104624730, NDRG2, DIDO1_A, MDFI B, MAX.chrl4.103021656-103021718, MMP23B, SEPT9_B, and STX16_A (see, Table 11, Example 1). The technology provides various panels of markers use for identifying clear cell EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is SFMBT2B, SQSTM1, ZNF323_A, ZNF90, MAX. chr8.145103 829-145103 992, CYTH2, LRRC8D A, OBSCNA, DIDO 1 A, MDFI B, GDF7 A, MAX.chr10.22624479-22624553, JSRP1_A, MAX.chrl4.103021656-103021718, EMX2OS, LRRC34, NBPF8, SEPT9B, EEF1A2, LRRC41C, VILL, and MPZ_A (see, Table 16, Example 1). The technology provides various panels of markers use for identifying clear cell EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is MAX. chr7:104624386-104624529, EMX2OS, DIDO1_B, and OBSCN_B (see, Table 24, Example 3). The technology provides various panels of markers use for identifying clear cell EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is SFMBT2 B, SQSTM1, ZNF323 A, ZNF506, ZNF90, CLDN7, LRRC41B, MAX. chr7.104624356-104624730, NDRG2, CYP11A1, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN A, DIDO1A, GDF6, MAX.chrl0.130339363-130339534, MDFI B, DLL4, GDF7 A, MIAT A, PYCARD, BMP4 B, JSRP1A, MAX.chr!4.103021656-103021718, EMX2, MMP23B, EMX2OS, MAX.chrl7.73073716-73073814, NBPF8, SEPT9_B, LOC440925_A, STX16_A, ITPKA, EEF1A2, FEV, LRRC41C, andNFIC (see, Table 11, Example 1). The technology provides various panels of markers use for identifying carcinosarcoma EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is EMX2OS, DIDO 1 A, SBNO2, AMIGO3 A, PCOLCE, CLDN7, CYTH2, OBSCN A, AHSA2, DLL4, EMX2, MAX.chrl4.74100620-74100870, LRRC4, 2026204791   22 Jun 2026 PPP2R5CA, SQSTM1, MAX.chrl7.73073716-73073814, CYP11A1, ACOXL_A, and AIM1B (see, Table 5, Example 1). The technology provides various panels of markers use for identifying carcinosarcoma EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is EMX2OS, and LRRC34 (see, Table 13, Example 1). The technology provides various panels of markers use for identifying carcinosarcoma EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is ZNF506, ZNF90, MAX.chr8.145103829-145103992, LRRC8D A, OBSCN A, MAX.chrl0.22624479-22624553, JSRP1A, EMX2OS, NBPF8, and VILL (see, Table 18, Example 1). The technology provides various panels of markers use for identifying carcinosarcoma EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is TRH, MAX. chr7:1046243 86-104624529, EMX2OS, DIDO I B, and ST3GAL2B (see, Table 24, Example 3). The technology provides various panels of markers use for identifying carcinosarcoma EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is SFMBT2B, SMTN, ZNF506, ZNF90, CLDN7, LRRC41_B, CYP11A1, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2B, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, DLL4, MAXchrl0.22624479-22624553, PYCARD, BMP4_B, JSRP1_A, MAX.chrl4.103021656-103021718, MIAT B, EMX2OS, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, and VILL (see, Table 13, Example 1). The technology provides various panels of markers use for identifying serous EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is EMX2OS, KANKI, Clorf70_B, AMIGO3 A, DIDO 1 A, LRRC41C, NFIC, FKBP11A, C17orfl07_A, SMTN, LRRC41B, LRRC8D A, OBSCN A, MAX.chr7.104624356-104624730, MIAT B (see, Table 7, Example 1). The technology provides various panels of markers use for identifying serous EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is MAX.chr7.104624356-104624730, EMX2OS, and LRRC41_C (see, Table 12, Example 1). The technology provides various panels of markers use for identifying serous EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation 2026204791   22 Jun 2026 that is MAX.chr8.145103829-145103992, CYTH2, LRRC8DA, OBSCN_A, DIDO1_A, EMX2OS, LRRC41C, and VILL (see, Table 17, Example 1). The technology provides various panels of markers use for identifying serous EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is EMX2OS, and LRRC41D (see, Table 24, Example 3). The technology provides various panels of markers use for identifying serous EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is SFMBT2 B, SMTN, SQSTM1, ZNF90, CLDN7, LRRC41_B, MAX.chr7.104624356-104624730, CYP11A1, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCNA, GDGF6, DLL4, PYCARD, BMP4 B, JSRP1A, MIAT B, KANKI, EMX2OS, NBPF8, LOC440925_A, ITPKA, EEF1A2, FEV, LRRC41C, NFIC, VILL, MPZ_A (see, Table 12, Example 1). The technology provides various panels of markers use for identifying endometrioid EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is MAX.chrl0.130339363-130339534, SFMBT2_C, CYTH2, SLC6A3, VILL, EMX2OS, MAX.chrl0.22624479-22624553, GDF6, ZNF90, ZNF506, JSRP1_A, c5orf52, SFMBT2B, NBPF8, RHBDL1A, DIDO 1 A, KANKI, and GATA2 B (see, Table 6, Example 1). The technology provides various panels of markers use for identifying endometrioid EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is MAX.chr8.145103829-145103992, CYTH2, DIDO1_A, MAX.chrl0.22624479-22624553, JSRP1_A, SBNO2, NBPF8, and VILL (see, Table 14, Example 1). The technology provides various panels of markers use for identifying endometrioid EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, MAX.chr8.145104263-145104422, OBSCN_A, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ A (see, Table 19, Example 1). The technology provides various panels of markers use for identifying endometrioid EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is SFMBT2B, SMTN, SQSTM1, ZNF506, ZNF90, CLDN7, LRRC41_B, FKBP11A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2 B, LRRC8D A, MAX.chr8.145104263-145104422, DIDO1_A, GDF6, MAX.chrlO. 130339363-130339534, 2026204791   22 Jun 2026 DLL4, MAX. chrlO.22624479-22624553, MIATA, PYCARD, BMP4 B, JSRP1A, MAX.chrl4.103021656-103021718, MIATB, KANKI, SBNO2, c5orf52, EMX2O6, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, VILL, and MPZ_A (see, Table 14, Example 1). The technology provides various panels of markers use for identifying endometrioid Grade 1 EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is TSPYL5, TRH, JAM3, FAM19A5, PTGDR, SFMBT2E, JSRP1B, and ARL5C (see, Table 25, Example 3). The technology provides various panels of markers use for identifying endometrioid Grade 2 EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is TSPYL5, MPZ_B, TRH, CNTN4, FAM19A5, GLT1D1, RYR2 F, PTGDR, EMX2OS, MAX.chrl0:22624470-22624553, SPDYA B, SFMBT2E, and JSRP1B (see, Table 25, Example 3). The technology provides various panels of markers use for identifying endometrioid Grade 3 EC, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is TSPYL5, MPZ_B, TRH, and PTGDR (see, Table 25, Example 3). Kit embodiments are provided, e.g., a kit comprising a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent); and a control nucleic acid comprising a sequence from a DMR selected from a group consisting of DMR 1-499 (from Tables 1, 8 and 21) and having a methylation state associated with a subject who does not have EC. In some embodiments, kits comprise a bisulfite reagent and an oligonucleotide as described herein. In some embodiments, kits comprise a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent); and a control nucleic acid comprising a sequence from a DMR selected from a group consisting of of DMR 1-499 (from Tables 1, 8 and 21) and having a methylation state associated with a subject who has EC. Some kit embodiments comprise a sample collector for obtaining a sample from a subject (e.g., a stool sample; endometrial tissue sample; blood sample); a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent); and an oligonucleotide as described herein. 2026204791   22 Jun 2026 The technology is related to embodiments of compositions (e.g., reaction mixtures). In some embodiments are provided a composition comprising a nucleic acid comprising a DMR and a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent). Some embodiments provide a composition comprising a nucleic acid comprising a DMR and an oligonucleotide as described herein. Some embodiments provide a composition comprising a nucleic acid comprising a DMR and a methylation-sensitive restriction enzyme. Some embodiments provide a composition comprising a nucleic acid comprising a DMR and a polymerase. Additional related method embodiments are provided for screening for EC and / or various forms of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC) in a sample obtained from a subject (e.g., endometrial tissue sample; blood sample; stool sample), e.g., a method comprising determining a methylation state of a marker in the sample comprising a base in a DMR that is one or more of DMR 1-499 (from Tables 1, 8 and 21); comparing the methylation state of the marker from the subject sample to a methylation state of the marker from a normal control sample from a subject who does not have EC (e.g., EC, clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC); and determining a confidence interval and / or a p value of the difference in the methylation state of the subject sample and the normal control sample. In some embodiments, the confidence interval is 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% or 99.99% and the p value is 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, or 0.0001. Some embodiments of methods provide steps of reacting a nucleic acid comprising a DMR with a reagent capable of modifying nucleic acid in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylationdependent restriction enzyme, and a bisulfite reagent) to produce, for example, nucleic acid modified in a methylation-specific manner; sequencing the nucleic acid modified in a methylation-specific manner to provide a nucleotide sequence of the nucleic acid modified in a methylation-specific manner; comparing the nucleotide sequence of the nucleic acid modified in a methylation-specific manner with a nucleotide sequence of a nucleic acid comprising the DMR from a subject who does not have EC and / or a form of EC to identify differences in the two sequences; and identifying the subject as having EC (e.g., EC and / or a form of EC: clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC) when a difference is present. 2026204791   22 Jun 2026 Systems for screening for EC in a sample obtained from a subject are provided by the technology. Exemplary embodiments of systems include, e.g., a system for screening for EC and / or types of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC) in a sample obtained from a subject (e.g., endometrial tissue sample; plasma sample; stool sample), the system comprising an analysis component configured to determine the methylation state of a sample, a software component configured to compare the methylation state of the sample with a control sample or a reference sample methylation state recorded in a database, and an alert component configured to alert a user of a EC-associated methylation state. An alert is determined in some embodiments by a software component that receives the results from multiple assays (e.g., determining the methylation states of multiple markers, e.g., DMR, e.g., as provided in Tables 1, 8 and 21) and calculating a value or result to report based on the multiple results. Some embodiments provide a database of weighted parameters associated with each DMR provided herein for use in calculating a value or result and / or an alert to report to a user (e.g., such as a physician, nurse, clinician, etc.). In some embodiments all results from multiple assays are reported and in some embodiments one or more results are used to provide a score, value, or result based on a composite of one or more results from multiple assays that is indicative of a cancer risk in a subject. In some embodiments of systems, a sample comprises a nucleic acid comprising a DMR. In some embodiments the system further comprises a component for isolating a nucleic acid, a component for collecting a sample such as a component for collecting a stool sample. In some embodiments, the system comprises nucleic acid sequences comprising a DMR. In some embodiments the database comprises nucleic acid sequences from subjects who do not have EC and / or specific types of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC). Also provided are nucleic acids, e.g., a set of nucleic acids, each nucleic acid having a sequence comprising a DMR. In some embodiments the set of nucleic acids wherein each nucleic acid has a sequence from a subject who does not have EC and / or specific types of EC. Related system embodiments comprise a set of nucleic acids as described and a database of nucleic acid sequences associated with the set of nucleic acids. Some embodiments further comprise a reagent capable of modifying DNA in a methylationspecific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent). And, some embodiments further comprise a nucleic acid sequencer. 2026204791   22 Jun 2026 In certain embodiments, methods for characterizing a sample (e.g., endometrial tissue sample; blood sample; stool sample) from a human patient are provided. For example, in some embodiments such embodiments comprise obtaining DNA from a sample of a human patient; assaying a methylation state of a DNA methylation marker comprising a base in a differentially methylated region (DMR) selected from a group consisting of DMR 1-499 from Tables 1, 8 and 21; and comparing the assayed methylation state of the one or more DNA methylation markers with methylation level references for the one or more DNA methylation markers for human patients not having EC and / or specific types of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC). Such methods are not limited to a particular type of sample from a human patient. In some embodiments, the sample is an endometrial tissue sample. In some embodiments, the sample is a plasma sample. In some embodiments, the sample is a stool sample, a tissue sample, an endometrial tissue sample, a blood sample (e.g., leukocyte sample, plasma sample, whole blood sample, serum sample), or a urine sample. In some embodiments, such methods comprise assaying a plurality of DNA methylation markers. In some embodiments, such methods comprise assaying 2 to 11 DNA methylation markers. In some embodiments, such methods comprise assaying 12 to 120 DNA methylation markers. In some embodiments, such methods comprise assaying 2 to 499 DNA methylation markers. In some embodiments, such methods comprise assaying the methylation state of the one or more DNA methylation markers in the sample comprises determining the methylation state of one base. In some embodiments, such methods comprise assaying the methylation state of the one or more DNA methylation markers in the sample comprises determining the extent of methylation at a plurality of bases. In some embodiments, such methods comprise assaying a methylation state of a forward strand or assaying a methylation state of a reverse strand. In some embodiments, the DNA methylation marker is a region of 100 or fewer bases. In some embodiments, the DNA methylation marker is a region of 500 or fewer bases. In some embodiments, the DNA methylation marker is a region of 1000 or fewer bases. In some embodiments, the DNA methylation marker is a region of 5000 or fewer bases. In some embodiments, the DNA methylation marker is one base. In some embodiments, the DNA methylation marker is in a high CpG density promoter. 2026204791   22 Jun 2026 In some embodiments, the assaying comprises using methylation specific polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation specific nuclease, mass-based separation, or target capture. In some embodiments, the assaying comprises use of a methylation specific oligonucleotide. In some embodiments, the methylation specific oligonucleotide is selected from the group consisting of SEQ ID NO: 1-499 (Tables 1, 8 and 21). In some embodiments, a chromosomal region having an annotation selected from the group consisting of AFF3, AIM1A, AMIGO 3 A, BMP4 B, C17orfl07_A, Clorf70_B, C5orf52, CLDN7, DIDO 1 A, EEF1A2, EMX2OS, FEV, FKBP11A, GDF6, GDF7 A, JSRP1A, KCTD15 A, KLHL21, LRRC8D A, NBPF8, MAX.chrl0.130339363-130339534, MAX.chrl0.22624479-22624553, MAX.chrl4.103021656-103021718, MAX. chr8.145103829-145103992, MAX.chr8.145104263-145104422, MDFI B, MIAT A, MMP23B, NDRG2, OBSCNA, PCOLCE, PYCARD, SEPT9B, SLC6A3A, SLC8A3_B, SQSTM1, VILL, ZNF302, ZNF323_A, ZNF506, and ZNF90 (see, Table 2, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of EMX2OS, CYTH2, C17orfl07_A, DIDO 1 A, GDF6, NBPF8, MAX.chrl4.103021656-103021718, JSRP1_A, GATA2B, and SFMBT2B (see, Table 3, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO 1 A, MAX.chrlO.22624479-22624553, JSRP1A, EMX2OS, NBPF8, and MPZ_A (see, Table 15, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of EMX2OS, CYTH2, NBPF8, MAX.chrlO.22624479-22624553 (see, Table 20, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of ANKRD35, ARL5C, ARRB1, BCL2L11A, BCL2L11B, BCL2L11C, BZRAP1, C16orf54, C17orfl01, C6orfl32, CACNA2D4, DEDD2, EPS15L1, FAIM2, FAM125B, FAM189B, FAM78A, FOXP4, GYPC A, GYPC B, IFFO1A, IFFO1B, ITPKA, KLF16, LIMD2, LOC389333, LOC440925_A, LOC646278, LYL1, LYPLAL1, MAX.chrl 1.32355226-32355251, MAX.chrl4.102172621-102172686, 2026204791   22 Jun 2026 MAX. chrl4.105512122-105512239, MAX.chrl5.95128144-95128248, MAX.chrl6.11327016-11327312, MAX.chr3.187676577-187676668, MAX.chr4.174430676-174430847, MAX.chr8.145900783-145900914, MAX. chr8.80804237-80804301, N4BP3, NCOR2, NFATC1A, NFATC1B, NKX2-6, NR2F6, OSM, PALLD C, PIK3CD, PRKAR1B, RAD52, STX16 A, SUCLG2, TNFRSF1B, TNFRSF4, ZDHHC18, and ZNF671A (see, Table 9, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of DIDO 1 A, NDRG4, MAX.chrl4.103021656-103021718, MMP23B, EMX2OS, SEPT9B, NBPF8, EEF1A2, AIM1A, BMP4 B, MAX.chr8.145103829-145103992, OBSCN, PYCARD, GDF6, MDFI B, MIAT A, SCL8A3, ZNF323_A, SQSTM1, AFF3, Clorf70, GDF7 A, JSRP1A, LRRC8D A, FEV, and MAX. chr8.145104263-145104422 (see, Table 4, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting ofZNF323_A, MAX. chr7.104624356-104624730, NDRG2, DIDO1_A, MDFI B, MAX.chr!4.103021656-103021718, MMP23B, SEPT9_B, and STX16_A (see, Table 11, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of SFMBT2B, SQSTM1, ZNF323_A, ZNF90, MAX. chr8.145103 829145103992, CYTH2, LRRC8D A, OBSCNA, DIDO 1 A, MDFI B, GDF7 A, MAX.chrl0.22624479-22624553, JSRP1_A, MAX.chrl4.103021656-103021718, EMX2OS, LRRC34, NBPF8, SEPT9B, EEF1A2, LRRC41C, VILL, and MPZ_A (see, Table 16, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of MAX.chr7:104624386-104624529, EMX2OS, DIDO1_B, and OBSCNB (see, Table 24, Example 3) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of SFMBT2B, SQSTM1, ZNF323_A, ZNF506, ZNF90, CLDN7, LRRC41B, MAX. chr7.104624356-104624730, NDRG2, CYP11A1, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN A, DIDO1_A, GDF6, MAX.chrlO. 130339363-130339534, MDFI B, DLL4, GDF7 A, MIAT A, PYCARD, BMP4 B, JSRP1A, MAX.chrl4.103021656- 2026204791   22 Jun 2026 103021718, EMX2, MMP23B, EMX2OS, MAX.chrl7.73073716-73073814, NBPF8, SEPT9B, LOC440925_A, STX16_A, ITPKA, EEF1A2, FEV, LRRC41C, andNFIC (see, Table 11, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of EMX2OS, DIDO 1 A, SBNO2, AMIGO3 A, PCOLCE, CLDN7, CYTH2, OBSCNA, AHSA2, DLL4, EMX2, MAX.chrl4.74100620-74100870, LRRC4, PPP2R5CA, SQSTM1, MAX.chrl7.73073716-73073814, CYP11A1, ACOXL_A, and AIM1B (see, Table 5, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of EMX2OS, and LRRC34 (see, Table 13, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting ofZNF506, ZNF90, MAX.chr8.145103829-145103992, LRRC8DA, OBSCN A, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and VILL (see, Table 18, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of TRH, MAX. chr7:1046243 86-104624529, EMX2OS, DIDO1_B, and ST3GAL2B (see, Table 24, Example 3) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of SFMBT2 B, SMTN, ZNF506, ZNF90, CLDN7, LRRC41_B, CYP11A1, MAX. chr8.145103 829-145103 992, AHSA2, CYTH2, GATA2 B, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, DLL4, MAXchrl0.22624479-22624553, PYCARD, BMP4_B, JSRP1_A, MAX.chrl4.103021656-103021718, MIAT B, EMX2OS, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, and VILL (see, Table 13, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting EMX2OS, KANKI, Clorf70_B, AMIGO3 A, DIDO 1 A, LRRC41C, NFIC, FKBP11A, C17orfl07_A, SMTN, LRRC41B, LRRC8D A, OBSCN A, MAX. chr7.104624356-104624730, MIAT B (see, Table 7, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of MAX.chr7.104624356-104624730, EMX2OS, and LRRC41_C (see, Table 12, Example 1) comprises the DNA methylation marker. 2026204791   22 Jun 2026 In some embodiments, a chromosomal region having an annotation selected from the group consisting of MAX.chr8.145103829-145103992, CYTH2, LRRC8DA, OBSCN_A, DIDO1A, EMX2OS, LRRC41C, and VILL (see, Table 17, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of EMX2OS, and LRRC41D (see, Table 24, Example 3) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of SFMBT2B, SMTN, SQSTM1, ZNF90, CLDN7, LRRC41_B, MAX.chr7.104624356-104624730, CYP11A1, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, GDGF6, DLL4, PYCARD, BMP4B, JSRP1A, MIATB, KANKI, EMX2OS, NBPF8, LOC440925_A, ITPKA, EEF1A2, FEV, LRRC41C, NFIC, VILL, MPZ A (see, Table 12, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of MAX.chrlO. 130339363-130339534, SFMBT2_C, CYTH2, SLC6A3, VILL, EMX2OS, MAX.chrl0.22624479-22624553, GDF6, ZNF90, ZNF506, JSRP1_A, c5orf52, SFMBT2B, NBPF8, RHBDL1A, DIDO 1 A, KANKI, and GATA2 B (see, Table 6, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of MAX. chr8.145103 829-145103992, CYTH2, DIDO1_A, MAXchrl0.22624479-22624553, JSRP1_A, SBNO2, NBPF8, and VILL (see, Table 14, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, MAX.chr8.145104263-145104422, OBSCN_A, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ_A (see, Table 19, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of SFMBT2B, SMTN, SQSTM1, ZNF506, ZNF90, CLDN7, LRRC41_B, FKBP11A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2 B, LRRC8D A, MAX.chr8.145104263-145104422, DIDO1_A, GDF6, MAX.chrl0.130339363-130339534, DLL4, MAX.chrlO.22624479-22624553, MIAT A, PYCARD, BMP4 B, JSRP1A, 2026204791   22 Jun 2026 MAX.chrl4.103021656-103021718, MIATB, KANKI, SBNO2, c5orf52, EMX2O6, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, VILL, and MPZ_A (see, Table 14, Example 1) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of TSPYL5, TRH, JAM3, FAM19A5, PTGDR, SFMBT2 E, JSRP1B, and ARL5C (see, Table 25, Example 3) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of TSPYL5, MPZ_B, TRH, CNTN4, FAM19A5, GLT1D1, RYR2 F, PTGDR, EMX2OS, MAX.chrl0:22624470-22624553, SPDYA B, SFMBT2 E, and JSRP1B (see, Table 25, Example 3) comprises the DNA methylation marker. In some embodiments, a chromosomal region having an annotation selected from the group consisting of TSPYL5, MPZ B, TRH, and PTGDR (see, Table 25, Example 3) comprises the DNA methylation marker. In some embodiments, such methods comprise determining the methylation state of two DNA methylation markers. In some embodiments, such methods comprise determining the methylation state of a pair of DNA methylation markers provided in a row of Tables 1, 8 and / or 21. In certain embodiments, the technology provides methods for characterizing a sample (e.g., endometrial tissue sample; leukocyte sample; plasma sample; whole blood sample; serum sample; stool sample) obtained from a human patient. In some embodiments, such methods comprise determining a methylation state of a DNA methylation marker in the sample comprising a base in a DMR selected from a group consisting of DMR 1-499 from Tables 1, 8 or 21; comparing the methylation state of the DNA methylation marker from the patient sample to a methylation state of the DNA methylation marker from a normal control sample from a human subject who does not have a EC and / or a specific form of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC); and determining a confidence interval and / or a p value of the difference in the methylation state of the human patient and the normal control sample. In some embodiments, the confidence interval is 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% or 99.99% and thep value is 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, or 0.0001. In certain embodiments, the technology provides methods for characterizing a sample obtained from a human subject (e.g., endometrial tissue sample; leukocyte sample; plasma sample; whole blood sample; serum sample; stool sample), the method comprising reacting a 2026204791   22 Jun 2026 nucleic acid comprising a DMR with a reagent capable of modifying DNA in a methylationspecific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent) to produce nucleic acid modified in a methylationspecific manner; sequencing the nucleic acid modified in a methylation-specific manner to provide a nucleotide sequence of the nucleic acid modified in a methylation-specific manner; comparing the nucleotide sequence of the nucleic acid modified in a methylation-specific manner with a nucleotide sequence of a nucleic acid comprising the DMR from a subject who does not have EC to identify differences in the two sequences. In certain embodiments, the technology provides systems for characterizing a sample obtained from a human subject (e.g., endometrial tissue sample; plasma sample; stool sample), the system comprising an analysis component configured to determine the methylation state of a sample, a software component configured to compare the methylation state of the sample with a control sample or a reference sample methylation state recorded in a database, and an alert component configured to determine a single value based on a combination of methylation states and alert a user of a EC-associated methylation state. In some embodiments, the sample comprises a nucleic acid comprising a DMR. In some embodiments, such systems further comprise a component for isolating a nucleic acid. In some embodiments, such systems further comprise a component for collecting a sample. In some embodiments, the sample is a stool sample, a tissue sample, an endometrial tissue sample, a blood sample (e.g., plasma sample, leukocyte sample, whole blood sample, serum sample), or a urine sample. In some embodiments, the database comprises nucleic acid sequences comprising a DMR. In some embodiments, the database comprises nucleic acid sequences from subjects who do not have EC. Additional embodiments will be apparent to persons skilled in the relevant art based on the teachings contained herein. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1: A cross-validated 3-MDM panel was derived from rPART modeling (EMX2OS, NBPF8, SFMBT2) which discriminated overall EC from BE with 97% specificity and 97% sensitivity with an AUC of 0.98. The data was plotted in a heat matrix format which allowed complementarity visualization. 2026204791   22 Jun 2026 FIG. 2: Marker chromosomal regions used for the 61 methylation markers (e.g., methylated regions distinguishing EC tissue from normal endometrial tissue) and related primer and probe information. DEFINITIONS To facilitate an understanding of the present technology, a number of terms and phrases are defined below. Additional definitions are set forth throughout the detailed description. Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Furthermore, the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention. In addition, as used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and / or” unless the context clearly dictates otherwise. The term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a”, “an”, and “the” include plural references. The meaning of “in” includes “in” and “on.” The transitional phrase “consisting essentially of’ as used in claims in the present application limits the scope of a claim to the specified materials or steps “and those that do not materially affect the basic and novel characteristic(s)” of the claimed invention, as discussed in In re Herz, 537F.2d 549, 551-52, 190 USPQ 461, 463 (CCPA 1976). For example, a composition “consisting essentially of’ recited elements may contain an unrecited contaminant at a level such that, though present, the contaminant does not alter the function of the recited composition as compared to a pure composition, i.e., a composition “consisting of’ the recited components. As used herein, a “nucleic acid” or “nucleic acid molecule” generally refers to any ribonucleic acid or deoxyribonucleic acid, which may be unmodified or modified DNA or RNA. “Nucleic acids” include, without limitation, single- and double-stranded nucleic acids. As used herein, the term “nucleic acid” also includes DNA as described above that contains one or more modified bases. Thus, DNA with a backbone modified for stability or for other 2026204791   22 Jun 2026 reasons is a “nucleic acid”. The term “nucleic acid” as it is used herein embraces such chemically, enzymatically, or metabolically modified forms of nucleic acids, as well as the chemical forms of DNA characteristic of viruses and cells, including for example, simple and complex cells. The terms “oligonucleotide” or “polynucleotide” or “nucleotide” or “nucleic acid” refer to a molecule having two or more deoxyribonucleotides or ribonucleotides, preferably more than three, and usually more than ten. The exact size will depend on many factors, which in turn depends on the ultimate function or use of the oligonucleotide. The oligonucleotide may be generated in any manner, including chemical synthesis, DNA replication, reverse transcription, or a combination thereof. Typical deoxyribonucleotides for DNA are thymine, adenine, cytosine, and guanine. Typical ribonucleotides for RNA are uracil, adenine, cytosine, and guanine. As used herein, the terms “locus” or “region” of a nucleic acid refer to a subregion of a nucleic acid, e.g., a gene on a chromosome, a single nucleotide, a CpG island, etc. The terms “complementary” and “complementarity” refer to nucleotides (e.g., 1 nucleotide) or polynucleotides (e.g., a sequence of nucleotides) related by the base-pairing rules. For example, the sequence 5'-A-G-T-3' is complementary to the sequence 3'-T-C-A-5'. Complementarity may be “partial,” in which only some of the nucleic acids’ bases are matched according to the base pairing rules. Or, there may be “complete” or “total” complementarity between the nucleic acids. The degree of complementarity between nucleic acid strands effects the efficiency and strength of hybridization between nucleic acid strands. This is of particular importance in amplification reactions and in detection methods that depend upon binding between nucleic acids. The term “gene” refers to a nucleic acid (e.g., DNA or RNA) sequence that comprises coding sequences necessary for the production of an RNA, or of a polypeptide or its precursor. A functional polypeptide can be encoded by a full length coding sequence or by any portion of the coding sequence as long as the desired activity or functional properties (e.g., enzymatic activity, ligand binding, signal transduction, etc.) of the polypeptide are retained. The term “portion” when used in reference to a gene refers to fragments of that gene. The fragments may range in size from a few nucleotides to the entire gene sequence minus one nucleotide. Thus, “a nucleotide comprising at least a portion of a gene” may comprise fragments of the gene or the entire gene. 2026204791   22 Jun 2026 The term “gene” also encompasses the coding regions of a structural gene and includes sequences located adjacent to the coding region on both the 5' and 3' ends, e.g., for a distance of about 1 kb on either end, such that the gene corresponds to the length of the fulllength mRNA (e.g., comprising coding, regulatory, structural and other sequences). The sequences that are located 5' of the coding region and that are present on the mRNA are referred to as 5' non-translated or untranslated sequences. The sequences that are located 3' or downstream of the coding region and that are present on the mRNA are referred to as 3' nontranslated or 3' untranslated sequences. The term “gene” encompasses both cDNA and genomic forms of a gene. In some organisms (e.g., eukaryotes), a genomic form or clone of a gene contains the coding region interrupted with non-coding sequences termed “introns” or “intervening regions” or “intervening sequences.” Introns are segments of a gene that are transcribed into nuclear RNA (hnRNA); introns may contain regulatory elements such as enhancers. Introns are removed or “spliced out” from the nuclear or primary transcript; introns therefore are absent in the messenger RNA (mRNA) transcript. The mRNA functions during translation to specify the sequence or order of amino acids in a nascent polypeptide. In addition to containing introns, genomic forms of a gene may also include sequences located on both the 5' and 3' ends of the sequences that are present on the RNA transcript. These sequences are referred to as “flanking” sequences or regions (these flanking sequences are located 5' or 3' to the non-translated sequences present on the mRNA transcript). The 5' flanking region may contain regulatory sequences such as promoters and enhancers that control or influence the transcription of the gene. The 3' flanking region may contain sequences that direct the termination of transcription, posttranscriptional cleavage, and polyadenylation. The term “wild-type” when made in reference to a gene refers to a gene that has the characteristics of a gene isolated from a naturally occurring source. The term “wild-type” when made in reference to a gene product refers to a gene product that has the characteristics of a gene product isolated from a naturally occurring source. The term “naturally-occurring” as applied to an object refers to the fact that an object can be found in nature. For example, a polypeptide or polynucleotide sequence that is present in an organism (including viruses) that can be isolated from a source in nature and which has not been intentionally modified by the hand of a person in the laboratory is naturally-occurring. A wild-type gene is often that gene or allele that is most frequently observed in a population and is thus arbitrarily designated the “normal” or “wild-type” form of the gene. In contrast, the term “modified” or “mutant” when 2026204791   22 Jun 2026 made in reference to a gene or to a gene product refers, respectively, to a gene or to a gene product that displays modifications in sequence and / or functional properties (e.g., altered characteristics) when compared to the wild-type gene or gene product. It is noted that naturally-occurring mutants can be isolated; these are identified by the fact that they have altered characteristics when compared to the wild-type gene or gene product. The term “allele” refers to a variation of a gene; the variations include but are not limited to variants and mutants, polymorphic loci, and single nucleotide polymorphic loci, frameshift, and splice mutations. An allele may occur naturally in a population or it might arise during the lifetime of any particular individual of the population. Thus, the terms “variant” and “mutant” when used in reference to a nucleotide sequence refer to a nucleic acid sequence that differs by one or more nucleotides from another, usually related, nucleotide acid sequence. A “variation” is a difference between two different nucleotide sequences; typically, one sequence is a reference sequence. “Amplification” is a special case of nucleic acid replication involving template specificity. It is to be contrasted with non-specific template replication (e.g., replication that is template-dependent but not dependent on a specific template). Template specificity is here distinguished from fidelity of replication (e.g., synthesis of the proper polynucleotide sequence) and nucleotide (ribo- or deoxyribo-) specificity. Template specificity is frequently described in terms of “target” specificity. Target sequences are “targets” in the sense that they are sought to be sorted out from other nucleic acid. Amplification techniques have been designed primarily for this sorting out. The term “amplifying” or “amplification” in the context of nucleic acids refers to the production of multiple copies of a polynucleotide, or a portion of the polynucleotide, typically starting from a small amount of the polynucleotide (e.g., a single polynucleotide molecule), where the amplification products or amplicons are generally detectable. Amplification of polynucleotides encompasses a variety of chemical and enzymatic processes. The generation of multiple DNA copies from one or a few copies of a target or template DNA molecule during a polymerase chain reaction (PCR) or a ligase chain reaction (LCR; see, e.g., U.S. Patent No. 5,494,810; herein incorporated by reference in its entirety) are forms of amplification. Additional types of amplification include, but are not limited to, allele-specific PCR (see, e.g., U.S. Patent No. 5,639,611; herein incorporated by reference in its entirety), assembly PCR (see, e.g., U.S. Patent No. 5,965,408; herein incorporated by reference in its entirety), helicase-dependent amplification (see, e.g., U.S. Patent No. 2026204791   22 Jun 2026 7,662,594; herein incorporated by reference in its entirety), hot-start PCR (see, e.g., U.S. Patent Nos. 5,773,258 and 5,338,671; each herein incorporated by reference in their entireties), intersequence-specific PCR, inverse PCR (see, e.g., Triglia, et al.(\9^) Nucleic Acids Res., 16:8186; herein incorporated by reference in its entirety), ligation-mediated PCR (see, e.g., Guilfoyle, R. et al., Nucleic Acids Research, 25:1854-1858 (1997); U.S. Patent No. 5,508,169; each of which are herein incorporated by reference in their entireties), methylation-specific PCR (see, e.g., Herman, et al., (1996) PNAS 93(13) 9821-9826; herein incorporated by reference in its entirety), miniprimer PCR, multiplex ligation-dependent probe amplification (see, e.g., Schouten, et al., (2002) Nucleic Acids Research 30(12): e57; herein incorporated by reference in its entirety), multiplex PCR (see, e.g., Chamberlain, et al., (1988) Nucleic Acids Research 16(23) 11141-11156; Ballabio, et al., (1990) Human Genetics 84(6) 571-573; Hayden, etal., (2008) BMC Genetics 9:80; each of which are herein incorporated by reference in their entireties), nested PCR, overlap-extension PCR (see, e.g., Higuchi, et al., (1988) Nucleic Acids Research 16(15) 7351-7367; herein incorporated by reference in its entirety), real time PCR (see, e.g., Higuchi, et al., (1992) Biotechnology 10:413-417; Higuchi, et al., (1993) Biotechnology 11:1026-1030; each of which are herein incorporated by reference in their entireties), reverse transcription PCR (see, e.g., Bustin, S.A. (2000) J. Molecular Endocrinology 25:169-193; herein incorporated by reference in its entirety), solid phase PCR, thermal asymmetric interlaced PCR, and Touchdown PCR (see, e.g., Don, et al., Nucleic Acids Research (1991) 19(14) 4008; Roux, K. (1994) Biotechniques 16(5) 812-814; Hecker, et al., (1996) Biotechniques 20(3) 478-485; each of which are herein incorporated by reference in their entireties). Polynucleotide amplification also can be accomplished using digital PCR (see, e.g., Kalinina, et al., Nucleic Acids Research. 25; 19992004, (1997); Vogelstein and Kinzler, Proc Natl Acad Sci USA. 96; 9236-41, (1999); International Patent Publication No. WO05023091A2; US Patent Application Publication No. 20070202525; each of which are incorporated herein by reference in their entireties). The term “polymerase chain reaction” (“PCR”) refers to the method of K.B. Mullis U.S. PatentNos. 4,683,195, 4,683,202, and 4,965,188, that describe a method for increasing the concentration of a segment of a target sequence in a mixture of genomic or other DNA or RNA, without cloning or purification. This process for amplifying the target sequence consists of introducing a large excess of two oligonucleotide primers to the DNA mixture containing the desired target sequence, followed by a precise sequence of thermal cycling in the presence of a DNA polymerase. The two primers are complementary to their respective 2026204791   22 Jun 2026 strands of the double stranded target sequence. To effect amplification, the mixture is denatured and the primers then annealed to their complementary sequences within the target molecule. Following annealing, the primers are extended with a polymerase so as to form a new pair of complementary strands. The steps of denaturation, primer annealing, and polymerase extension can be repeated many times (i.e., denaturation, annealing and extension constitute one “cycle”; there can be numerous “cycles”) to obtain a high concentration of an amplified segment of the desired target sequence. The length of the amplified segment of the desired target sequence is determined by the relative positions of the primers with respect to each other, and therefore, this length is a controllable parameter. By virtue of the repeating aspect of the process, the method is referred to as the “polymerase chain reaction” (“PCR”). Because the desired amplified segments of the target sequence become the predominant sequences (in terms of concentration) in the mixture, they are said to be “PCR amplified” and are “PCR products” or “amplicons.” Those of skill in the art will understand the term “PCR” encompasses many variants of the originally described method using, e.g., real time PCR, nested PCR, reverse transcription PCR (RT-PCR), single primer and arbitrarily primed PCR, etc. Template specificity is achieved in most amplification techniques by the choice of enzyme. Amplification enzymes are enzymes that, under conditions they are used, will process only specific sequences of nucleic acid in a heterogeneous mixture of nucleic acid. For example, in the case of Q-beta replicase, MDV-1 RNA is the specific template for the replicase (Kacian et al., Proc. Natl. Acad. Sci. USA, 69:3038

[1972] ). Other nucleic acid will not be replicated by this amplification enzyme. Similarly, in the case of T7 RNA polymerase, this amplification enzyme has a stringent specificity for its own promoters (Chamberlin et al, Nature, 228:227

[1970] ). In the case of T4 DNA ligase, the enzyme will not ligate the two oligonucleotides or polynucleotides, where there is a mismatch between the oligonucleotide or polynucleotide substrate and the template at the ligation junction (Wu and Wallace (1989) Genomics 4:560). Finally, thermostable template-dependant DNA polymerases (e.g., Taq and Pfu DNA polymerases), by virtue of their ability to function at high temperature, are found to display high specificity for the sequences bounded and thus defined by the primers; the high temperature results in thermodynamic conditions that favor primer hybridization with the target sequences and not hybridization with non-target sequences (H. A. Erlich (ed.), PCR Technology, Stockton Press

[1989] ). 2026204791   22 Jun 2026 As used herein, the term “nucleic acid detection assay” refers to any method of determining the nucleotide composition of a nucleic acid of interest. Nucleic acid detection assay include but are not limited to, DNA sequencing methods, probe hybridization methods, structure specific cleavage assays (e.g., the INVADER assay, (Hologic, Inc.) and are described, e.g., in U.S. Patent Nos. 5,846,717, 5,985,557, 5,994,069, 6,001,567, 6,090,543, and 6,872,816; Lyamichev et al., Nat. Biotech., 17:292 (1999), Hall et al., PNAS, USA, 97:8272 (2000), and US Pat. No. 9,096,893, each of which is herein incorporated by reference in its entirety for all purposes); enzyme mismatch cleavage methods (e.g, Variagenics, U.S. Pat. Nos. 6,110,684, 5,958,692, 5,851,770, herein incorporated by reference in their entireties); polymerase chain reaction (PCR), described above; branched hybridization methods (e.g., Chiron, U.S. Pat. Nos. 5,849,481, 5,710,264, 5,124,246, and 5,624,802, herein incorporated by reference in their entireties); rolling circle replication (e.g, U.S. Pat. Nos. 6,210,884, 6,183,960 and 6,235,502, herein incorporated by reference in their entireties); NASBA (e.g, U.S. Pat. No. 5,409,818, herein incorporated by reference in its entirety); molecular beacon technology (e.g, U.S. Pat. No. 6,150,097, herein incorporated by reference in its entirety); E-sensor technology (Motorola, U.S. Pat. Nos. 6,248,229, 6,221,583, 6,013,170, and 6,063,573, herein incorporated by reference in their entireties); cycling probe technology (e.g, U.S. Pat. Nos. 5,403,711, 5,011,769, and 5,660,988, herein incorporated by reference in their entireties); Dade Behring signal amplification methods (e.g, U.S. Pat. Nos. 6,121,001, 6,110,677, 5,914,230, 5,882,867, and 5,792,614, herein incorporated by reference in their entireties); ligase chain reaction (e.g., Baranay Proc. Natl. Acad. Sci USA 88, 189-93 (1991)); and sandwich hybridization methods (e.g, U.S. Pat. No. 5,288,609, herein incorporated by reference in its entirety). The term “amplifiable nucleic acid” refers to a nucleic acid that may be amplified by any amplification method. It is contemplated that “amplifiable nucleic acid” will usually comprise “sample template.” The term “sample template” refers to nucleic acid originating from a sample that is analyzed for the presence of “target” (defined below). In contrast, “background template” is used in reference to nucleic acid other than sample template that may or may not be present in a sample. Background template is most often inadvertent. It may be the result of carryover or it may be due to the presence of nucleic acid contaminants sought to be purified away from the sample. For example, nucleic acids from organisms other than those to be detected may be present as background in a test sample. 2026204791   22 Jun 2026 The term “primer” refers to an oligonucleotide, whether occurring naturally as, e.g., a nucleic acid fragment from a restriction digest, or produced synthetically, that is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product that is complementary to a nucleic acid template strand is induced, (e.g., in the presence of nucleotides and an inducing agent such as a DNA polymerase, and at a suitable temperature and pH). The primer is preferably single stranded for maximum efficiency in amplification, but may alternatively be double stranded. If double stranded, the primer is first treated to separate its strands before being used to prepare extension products. Preferably, the primer is an oligodeoxyribonucleotide. The primer must be sufficiently long to prime the synthesis of extension products in the presence of the inducing agent. The exact lengths of the primers will depend on many factors, including temperature, source of primer, and the use of the method. The term “probe” refers to an oligonucleotide (e.g, a sequence of nucleotides), whether occurring naturally as in a purified restriction digest or produced synthetically, recombinantly, or by PCR amplification, that is capable of hybridizing to another oligonucleotide of interest. A probe may be single-stranded or double-stranded. Probes are useful in the detection, identification, and isolation of particular gene sequences (e.g, a “capture probe”). It is contemplated that any probe used in the present invention may, in some embodiments, be labeled with any “reporter molecule,” so that is detectable in any detection system, including, but not limited to enzyme (e.g., ELISA, as well as enzyme-based histochemical assays), fluorescent, radioactive, and luminescent systems. It is not intended that the present invention be limited to any particular detection system or label. The term “target,” as used herein refers to a nucleic acid sought to be sorted out from other nucleic acids, e.g., by probe binding, amplification, isolation, capture, etc. For example, when used in reference to the polymerase chain reaction, “target” refers to the region of nucleic acid bounded by the primers used for polymerase chain reaction, while when used in an assay in which target DNA is not amplified, e.g., in some embodiments of an invasive cleavage assay, a target comprises the site at which a probe and invasive oligonucleotides (e.g, INVADER oligonucleotide) bind to form an invasive cleavage structure, such that the presence of the target nucleic acid can be detected. A “segment” is defined as a region of nucleic acid within the target sequence. As used herein, “methylation” refers to cytosine methylation at positions C5 or N4 of cytosine, the N6 position of adenine, or other types of nucleic acid methylation. In vitro 2026204791   22 Jun 2026 amplified DNA is usually unmethylated because typical in vitro DNA amplification methods do not retain the methylation pattern of the amplification template. However, “unmethylated DNA” or “methylated DNA” can also refer to amplified DNA whose original template was unmethylated or methylated, respectively. Accordingly, as used herein a “methylated nucleotide” or a “methylated nucleotide base” refers to the presence of a methyl moiety on a nucleotide base, where the methyl moiety is not present in a recognized typical nucleotide base. For example, cytosine does not contain a methyl moiety on its pyrimidine ring, but 5-methylcytosine contains a methyl moiety at position 5 of its pyrimidine ring. Therefore, cytosine is not a methylated nucleotide and 5-methylcytosine is a methylated nucleotide. In another example, thymine contains a methyl moiety at position 5 of its pyrimidine ring; however, for purposes herein, thymine is not considered a methylated nucleotide when present in DNA since thymine is a typical nucleotide base of DNA. As used herein, a “methylated nucleic acid molecule” refers to a nucleic acid molecule that contains one or more methylated nucleotides. As used herein, a “methylation state”, “methylation profile”, and “methylation status” of a nucleic acid molecule refers to the presence of absence of one or more methylated nucleotide bases in the nucleic acid molecule. For example, a nucleic acid molecule containing a methylated cytosine is considered methylated (e.g., the methylation state of the nucleic acid molecule is methylated). A nucleic acid molecule that does not contain any methylated nucleotides is considered unmethylated. The methylation state of a particular nucleic acid sequence (e.g., a gene marker or DNA region as described herein) can indicate the methylation state of every base in the sequence or can indicate the methylation state of a subset of the bases (e.g., of one or more cytosines) within the sequence, or can indicate information regarding regional methylation density within the sequence with or without providing precise information of the locations within the sequence the methylation occurs. The methylation state of a nucleotide locus in a nucleic acid molecule refers to the presence or absence of a methylated nucleotide at a particular locus in the nucleic acid molecule. For example, the methylation state of a cytosine at the 7th nucleotide in a nucleic acid molecule is methylated when the nucleotide present at the 7th nucleotide in the nucleic acid molecule is 5-methylcytosine. Similarly, the methylation state of a cytosine at the 7th 2026204791   22 Jun 2026 nucleotide in a nucleic acid molecule is unmethylated when the nucleotide present at the 7th nucleotide in the nucleic acid molecule is cytosine (and not 5-methylcytosine). The methylation status can optionally be represented or indicated by a “methylation value” (e.g., representing a methylation frequency, fraction, ratio, percent, etc.) A methylation value can be generated, for example, by quantifying the amount of intact nucleic acid present following restriction digestion with a methylation dependent restriction enzyme or by comparing amplification profiles after bisulfite reaction or by comparing sequences of bisulfite-treated and untreated nucleic acids. Accordingly, a value, e.g., a methylation value, represents the methylation status and can thus be used as a quantitative indicator of methylation status across multiple copies of a locus. This is of particular use when it is desirable to compare the methylation status of a sequence in a sample to a threshold or reference value. As used herein, “methylation frequency” or “methylation percent (%)” refer to the number of instances in which a molecule or locus is methylated relative to the number of instances the molecule or locus is unmethylated. As such, the methylation state describes the state of methylation of a nucleic acid (e.g., a genomic sequence). In addition, the methylation state refers to the characteristics of a nucleic acid segment at a particular genomic locus relevant to methylation. Such characteristics include, but are not limited to, whether any of the cytosine (C) residues within this DNA sequence are methylated, the location of methylated C residue(s), the frequency or percentage of methylated C throughout any particular region of a nucleic acid, and allelic differences in methylation due to, e.g., difference in the origin of the alleles. The terms “methylation state”, “methylation profile”, and “methylation status” also refer to the relative concentration, absolute concentration, or pattern of methylated C or unmethylated C throughout any particular region of a nucleic acid in a biological sample. For example, if the cytosine (C) residue(s) within a nucleic acid sequence are methylated it may be referred to as “hypermethylated” or having “increased methylation”, whereas if the cytosine (C) residue(s) within a DNA sequence are not methylated it may be referred to as “hypomethylated” or having “decreased methylation”. Likewise, if the cytosine (C) residue(s) within a nucleic acid sequence are methylated as compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.) that sequence is considered hypermethylated or having increased methylation compared to the other nucleic acid sequence. Alternatively, if the cytosine (C) residue(s) within a DNA sequence are not methylated as compared to 2026204791   22 Jun 2026 another nucleic acid sequence (e.g., from a different region or from a different individual, etc.) that sequence is considered hypomethylated or having decreased methylation compared to the other nucleic acid sequence. Additionally, the term “methylation pattern” as used herein refers to the collective sites of methylated and unmethylated nucleotides over a region of a nucleic acid. Two nucleic acids may have the same or similar methylation frequency or methylation percent but have different methylation patterns when the number of methylated and unmethylated nucleotides are the same or similar throughout the region but the locations of methylated and unmethylated nucleotides are different. Sequences are said to be “differentially methylated” or as having a “difference in methylation” or having a “different methylation state” when they differ in the extent (e.g., one has increased or decreased methylation relative to the other), frequency, or pattern of methylation. The term “differential methylation” refers to a difference in the level or pattern of nucleic acid methylation in a cancer positive sample as compared with the level or pattern of nucleic acid methylation in a cancer negative sample. It may also refer to the difference in levels or patterns between patients that have recurrence of cancer after surgery versus patients who not have recurrence. Differential methylation and specific levels or patterns of DNA methylation are prognostic and predictive biomarkers, e.g., once the correct cut-off or predictive characteristics have been defined. Methylation state frequency can be used to describe a population of individuals or a sample from a single individual. For example, a nucleotide locus having a methylation state frequency of 50% is methylated in 50% of instances and unmethylated in 50% of instances. Such a frequency can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a population of individuals or a collection of nucleic acids. Thus, when methylation in a first population or pool of nucleic acid molecules is different from methylation in a second population or pool of nucleic acid molecules, the methylation state frequency of the first population or pool will be different from the methylation state frequency of the second population or pool. Such a frequency also can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a single individual. For example, such a frequency can be used to describe the degree to which a group of cells from a tissue sample are methylated or unmethylated at a nucleotide locus or nucleic acid region. 2026204791   22 Jun 2026 As used herein a “nucleotide locus” refers to the location of a nucleotide in a nucleic acid molecule. A nucleotide locus of a methylated nucleotide refers to the location of a methylated nucleotide in a nucleic acid molecule. Typically, methylation of human DNA occurs on a dinucleotide sequence including an adjacent guanine and cytosine where the cytosine is located 5' of the guanine (also termed CpG dinucleotide sequences). Most cytosines within the CpG dinucleotides are methylated in the human genome, however some remain unmethylated in specific CpG dinucleotide rich genomic regions, known as CpG islands (see, e.g, Antequera et al. (1990) Cell 62: 503-514). As used herein, a “CpG island” refers to a G:C-rich region of genomic DNA containing an increased number of CpG dinucleotides relative to total genomic DNA. A CpG island can be at least 100, 200, or more base pairs in length, where the G:C content of the region is at least 50% and the ratio of observed CpG frequency over expected frequency is 0.6; in some instances, a CpG island can be at least 500 base pairs in length, where the G:C content of the region is at least 55%) and the ratio of observed CpG frequency over expected frequency is 0.65. The observed CpG frequency over expected frequency can be calculated according to the method provided in Gardiner-Garden et al (1987) J. Mol. Biol. 196: 261281. For example, the observed CpG frequency over expected frequency can be calculated according to the formula R = (A * B) / (C * D), where R is the ratio of observed CpG frequency over expected frequency, A is the number of CpG dinucleotides in an analyzed sequence, B is the total number of nucleotides in the analyzed sequence, C is the total number of C nucleotides in the analyzed sequence, and D is the total number of G nucleotides in the analyzed sequence. Methylation state is typically determined in CpG islands, e.g., at promoter regions. It will be appreciated though that other sequences in the human genome are prone to DNA methylation such as CpA and CpT (see Ramsahoye (2000) Proc. Natl. Acad. Sci. USA 97: 5237-5242; Salmon and Kaye (1970) Biochim. Biophys. Acta. 204: 340-351; Grafstrom (1985) Nucleic Acids Res. 13: 2827-2842; Nyce (1986) Nucleic Acids Res. 14: 4353-4367; Woodcock (1987) Biochem. Biophys. Res. Commun. 145: 888-894). As used herein, a “methylation-specific reagent” refers to a reagent that modifies a nucleotide of the nucleic acid molecule as a function of the methylation state of the nucleic acid molecule, or a methylation-specific reagent, refers to a compound or composition or other agent that can change the nucleotide sequence of a nucleic acid molecule in a manner that reflects the methylation state of the nucleic acid molecule. Methods of treating a nucleic acid molecule with such a reagent can include contacting the nucleic acid molecule with the 2026204791   22 Jun 2026 reagent, coupled with additional steps, if desired, to accomplish the desired change of nucleotide sequence. Such methods can be applied in a manner in which unmethylated nucleotides (e.g, each unmethylated cytosine) is modified to a different nucleotide. For example, in some embodiments, such a reagent can deaminate unmethylated cytosine nucleotides to produce deoxy uracil residues. Examples of such reagents include, but are not limited to, a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent. A change in the nucleic acid nucleotide sequence by a methylation -specific reagent can also result in a nucleic acid molecule in which each methylated nucleotide is modified to a different nucleotide. The term “methylation assay” refers to any assay for determining the methylation state of one or more CpG dinucleotide sequences within a sequence of a nucleic acid. The term “MS AP-PCR” (Methylation-Sensitive Arbitrarily-Primed Polymerase Chain Reaction) refers to the art-recognized technology that allows for a global scan of the genome using CG-rich primers to focus on the regions most likely to contain CpG dinucleotides, and described by Gonzalgo et al. (1997) Cancer Research 57: 594-599. The term “MethyLight™” refers to the art-recognized fluorescence-based real-time PCR technique described by Eads et al. (1999) Cancer Res. 59: 2302-2306. The term “HeavyMethyl™” refers to an assay wherein methylation specific blocking probes (also referred to herein as blockers) covering CpG positions between, or covered by, the amplification primers enable methylation-specific selective amplification of a nucleic acid sample. The term “HeavyMethyl™ MethyLight™” assay refers to a HeavyMethyl™ MethyLight™ assay, which is a variation of the MethyLight™ assay, wherein the MethyLight™ assay is combined with methylation specific blocking probes covering CpG positions between the amplification primers. The term “Ms-SNuPE” (Methylation-sensitive Single Nucleotide Primer Extension) refers to the art-recognized assay described by Gonzalgo & Jones (1997) Nucleic Acids Res. 25: 2529-2531. The term “MSP” (Methylation-specific PCR) refers to the art-recognized methylation assay described by Herman et al. (1996) Proc. Natl. Acad. Sci. USA 93: 9821-9826, and by U.S. Pat. No. 5,786,146. 2026204791   22 Jun 2026 The term “COBRA” (Combined Bisulfite Restriction Analysis) refers to the art-recognized methylation assay described by Xiong & Laird (1997) Nucleic Acids Res. 25: 2532-2534. The term “MCA” (Methylated CpG Island Amplification) refers to the methylation assay described by Toyota et al. (1999) Cancer Res. 59: 2307-12, and in WO 00 / 26401Al. As used herein, a “selected nucleotide” refers to one nucleotide of the four typically occurring nucleotides in a nucleic acid molecule (C, G, T, and A for DNA and C, G, U, and A for RNA), and can include methylated derivatives of the typically occurring nucleotides (e.g., when C is the selected nucleotide, both methylated and unmethylated C are included within the meaning of a selected nucleotide), whereas a methylated selected nucleotide refers specifically to a methylated typically occurring nucleotide and an unmethylated selected nucleotides refers specifically to an unmethylated typically occurring nucleotide. The term “methylation-specific restriction enzyme” refers to a restriction enzyme that selectively digests a nucleic acid dependent on the methylation state of its recognition site. In the case of a restriction enzyme that specifically cuts if the recognition site is not methylated or is hemi-methylated (a methylation-sensitive enzyme), the cut will not take place (or will take place with a significantly reduced efficiency) if the recognition site is methylated on one or both strands. In the case of a restriction enzyme that specifically cuts only if the recognition site is methylated (a methylation-dependent enzyme), the cut will not take place (or will take place with a significantly reduced efficiency) if the recognition site is not methylated. Preferred are methylation-specific restriction enzymes, the recognition sequence of which contains a CG dinucleotide (for instance a recognition sequence such as CGCG or CCCGGG). Further preferred for some embodiments are restriction enzymes that do not cut if the cytosine in this dinucleotide is methylated at the carbon atom C5. As used herein, a “different nucleotide” refers to a nucleotide that is chemically different from a selected nucleotide, typically such that the different nucleotide has Watson-Crick base-pairing properties that differ from the selected nucleotide, whereby the typically occurring nucleotide that is complementary to the selected nucleotide is not the same as the typically occurring nucleotide that is complementary to the different nucleotide. For example, when C is the selected nucleotide, U or T can be the different nucleotide, which is exemplified by the complementarity of C to G and the complementarity of U or T to A. As used herein, a nucleotide that is complementary to the selected nucleotide or that is complementary to the different nucleotide refers to a nucleotide that base-pairs, under high 2026204791   22 Jun 2026 stringency conditions, with the selected nucleotide or different nucleotide with higher affinity than the complementary nucleotide's base-paring with three of the four typically occurring nucleotides. An example of complementarity is Watson-Crick base pairing in DNA (e.g., A-T and C-G) and RNA (e.g., A-U and C-G). Thus, for example, G base-pairs, under high stringency conditions, with higher affinity to C than G base-pairs to G, A, or T and, therefore, when C is the selected nucleotide, G is a nucleotide complementary to the selected nucleotide. As used herein, the “sensitivity” of a given marker (or set of markers used together) refers to the percentage of samples that report a DNA methylation value above a threshold value that distinguishes between neoplastic and non-neoplastic samples. In some embodiments, a positive is defined as a histology-confirmed neoplasia that reports a DNA methylation value above a threshold value (e.g., the range associated with disease), and a false negative is defined as a histology-confirmed neoplasia that reports a DNA methylation value below the threshold value (e.g, the range associated with no disease). The value of sensitivity, therefore, reflects the probability that a DNA methylation measurement for a given marker obtained from a known diseased sample will be in the range of disease-associated measurements. As defined here, the clinical relevance of the calculated sensitivity value represents an estimation of the probability that a given marker would detect the presence of a clinical condition when applied to a subject with that condition. As used herein, the “specificity” of a given marker (or set of markers used together) refers to the percentage of non-neoplastic samples that report a DNA methylation value below a threshold value that distinguishes between neoplastic and non-neoplastic samples. In some embodiments, a negative is defined as a histology-confirmed non-neoplastic sample that reports a DNA methylation value below the threshold value (e.g., the range associated with no disease) and a false positive is defined as a histology-confirmed non-neoplastic sample that reports a DNA methylation value above the threshold value (e.g, the range associated with disease). The value of specificity, therefore, reflects the probability that a DNA methylation measurement for a given marker obtained from a known non-neoplastic sample will be in the range of non-disease associated measurements. As defined here, the clinical relevance of the calculated specificity value represents an estimation of the probability that a given marker would detect the absence of a clinical condition when applied to a patient without that condition. 2026204791   22 Jun 2026 The term “AUC” as used herein is an abbreviation for the “area under a curve”. In particular it refers to the area under a Receiver Operating Characteristic (ROC) curve. The ROC curve is a plot of the true positive rate against the false positive rate for the different possible cut points of a diagnostic test. It shows the trade-off between sensitivity and specificity depending on the selected cut point (any increase in sensitivity will be accompanied by a decrease in specificity). The area under an ROC curve (AUC) is a measure for the accuracy of a diagnostic test (the larger the area the better; the optimum is 1; a random test would have a ROC curve lying on the diagonal with an area of 0.5; for reference: J. P. Egan. (1975) Signal Detection Theory and ROC Analysis, Academic Press, New York). The term "neoplasm" as used herein refers to any new and abnormal growth of tissue. Thus, a neoplasm can be a premalignant neoplasm or a malignant neoplasm. The term "neoplasm-specific marker," as used herein, refers to any biological material or element that can be used to indicate the presence of a neoplasm. Examples of biological materials include, without limitation, nucleic acids, polypeptides, carbohydrates, fatty acids, cellular components (e.g., cell membranes and mitochondria), and whole cells. In some instances, markers are particular nucleic acid regions (e.g., genes, intragenic regions, specific loci, etc.). Regions of nucleic acid that are markers may be referred to, e.g., as "marker genes," "marker regions," "marker sequences," "marker loci," etc. As used herein, the term “adenoma” refers to a benign tumor of glandular origin. Although these growths are benign, over time they may progress to become malignant. The term “pre-cancerous” or “pre-neoplastic” and equivalents thereof refer to any cellular proliferative disorder that is undergoing malignant transformation. A “site” of a neoplasm, adenoma, cancer, etc. is the tissue, organ, cell type, anatomical area, body part, etc. in a subject’s body where the neoplasm, adenoma, cancer, etc. is located. As used herein, a “diagnostic” test application includes the detection or identification of a disease state or condition of a subject, determining the likelihood that a subject will contract a given disease or condition, determining the likelihood that a subject with a disease or condition will respond to therapy, determining the prognosis of a subject with a disease or condition (or its likely progression or regression), and determining the effect of a treatment on a subject with a disease or condition. For example, a diagnostic can be used for detecting the presence or likelihood of a subject contracting a neoplasm or the likelihood that such a 2026204791   22 Jun 2026 subject will respond favorably to a compound (e.g., a pharmaceutical, e.g., a drug) or other treatment. The term “isolated” when used in relation to a nucleic acid, as in “an isolated oligonucleotide” refers to a nucleic acid sequence that is identified and separated from at least one contaminant nucleic acid with which it is ordinarily associated in its natural source. Isolated nucleic acid is present in a form or setting that is different from that in which it is found in nature. In contrast, non-isolated nucleic acids, such as DNA and RNA, are found in the state they exist in nature. Examples of non-isolated nucleic acids include: a given DNA sequence (e.g., a gene) found on the host cell chromosome in proximity to neighboring genes; RNA sequences, such as a specific mRNA sequence encoding a specific protein, found in the cell as a mixture with numerous other mRNAs which encode a multitude of proteins. However, isolated nucleic acid encoding a particular protein includes, by way of example, such nucleic acid in cells ordinarily expressing the protein, where the nucleic acid is in a chromosomal location different from that of natural cells, or is otherwise flanked by a different nucleic acid sequence than that found in nature. The isolated nucleic acid or oligonucleotide may be present in single-stranded or double-stranded form. When an isolated nucleic acid or oligonucleotide is to be utilized to express a protein, the oligonucleotide will contain at a minimum the sense or coding strand (i.e., the oligonucleotide may be singlestranded), but may contain both the sense and anti-sense strands (i.e., the oligonucleotide may be double-stranded). An isolated nucleic acid may, after isolation from its natural or typical environment, by be combined with other nucleic acids or molecules. For example, an isolated nucleic acid may be present in a host cell in which into which it has been placed, e.g., for heterologous expression. The term “purified” refers to molecules, either nucleic acid or amino acid sequences that are removed from their natural environment, isolated, or separated. An “isolated nucleic acid sequence” may therefore be a purified nucleic acid sequence. “Substantially purified” molecules are at least 60% free, preferably at least 75% free, and more preferably at least 90% free from other components with which they are naturally associated. As used herein, the terms “purified” or “to purify” also refer to the removal of contaminants from a sample. The removal of contaminating proteins results in an increase in the percent of polypeptide or nucleic acid of interest in the sample. In another example, recombinant polypeptides are expressed in plant, bacterial, yeast, or mammalian host cells and the polypeptides are purified 2026204791   22 Jun 2026 by the removal of host cell proteins; the percent of recombinant polypeptides is thereby increased in the sample. The term “composition comprising” a given polynucleotide sequence or polypeptide refers broadly to any composition containing the given polynucleotide sequence or polypeptide. The composition may comprise an aqueous solution containing salts (e.g., NaCl), detergents (e.g., SDS), and other components (e.g., Denhardt’s solution, dry milk, salmon sperm DNA, etc.). The term “sample” is used in its broadest sense. In one sense it can refer to an animal cell or tissue. In another sense, it refers to a specimen or culture obtained from any source, as well as biological and environmental samples. Biological samples may be obtained from plants or animals (including humans) and encompass fluids, solids, tissues, and gases. Environmental samples include environmental material such as surface matter, soil, water, and industrial samples. These examples are not to be construed as limiting the sample types applicable to the present invention. As used herein, a “remote sample” as used in some contexts relates to a sample indirectly collected from a site that is not the cell, tissue, or organ source of the sample. As used herein, the terms “patient” or “subject” refer to organisms to be subject to various tests provided by the technology. The term “subject” includes animals, preferably mammals, including humans. In a preferred embodiment, the subject is a primate. In an even more preferred embodiment, the subject is a human. Further with respect to diagnostic methods, a preferred subject is a vertebrate subject. A preferred vertebrate is warm-blooded; a preferred warm-blooded vertebrate is a mammal. A preferred mammal is most preferably a human. As used herein, the term “subject1 includes both human and animal subjects. Thus, veterinary therapeutic uses are provided herein. As such, the present technology provides for the diagnosis of mammals such as humans, as well as those mammals of importance due to being endangered, such as Siberian tigers; of economic importance, such as animals raised on farms for consumption by humans; and / or animals of social importance to humans, such as animals kept as pets or in zoos. Examples of such animals include but are not limited to: carnivores such as cats and dogs; swine, including pigs, hogs, and wild boars; ruminants and / or ungulates such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels; pinnipeds; and horses. Thus, also provided is the diagnosis and treatment of livestock, including, but not limited to, domesticated swine, ruminants, ungulates, horses (including race horses), and the like. The presently-disclosed subject matter further includes a system for 2026204791   22 Jun 2026 diagnosing a lung cancer in a subject. The system can be provided, for example, as a commercial kit that can be used to screen for a risk of lung cancer or diagnose a lung cancer in a subject from whom a biological sample has been collected. An exemplary system provided in accordance with the present technology includes assessing the methylation state of a marker described herein. As used herein, the term “kit” refers to any delivery system for delivering materials. In the context of reaction assays, such delivery systems include systems that allow for the storage, transport, or delivery of reaction reagents (e.g., oligonucleotides, enzymes, etc. in the appropriate containers) and / or supporting materials (e.g., buffers, written instructions for performing the assay etc.) from one location to another. For example, kits include one or more enclosures (e.g., boxes) containing the relevant reaction reagents and / or supporting materials. As used herein, the term “fragmented kit” refers to delivery systems comprising two or more separate containers that each contain a subportion of the total kit components. The containers may be delivered to the intended recipient together or separately. For example, a first container may contain an enzyme for use in an assay, while a second container contains oligonucleotides. The term “fragmented kit” is intended to encompass kits containing Analyte specific reagents (ASR's) regulated under section 520(e) of the Federal Food, Drug, and Cosmetic Act, but are not limited thereto. Indeed, any delivery system comprising two or more separate containers that each contains a subportion of the total kit components are included in the term “fragmented kit.” In contrast, a “combined kit” refers to a delivery system containing all of the components of a reaction assay in a single container (e.g., in a single box housing each of the desired components). The term “kit” includes both fragmented and combined kits. As used herein, the term “information” refers to any collection of facts or data. In reference to information stored or processed using a computer system(s), including but not limited to internets, the term refers to any data stored in any format (e.g., analog, digital, optical, etc.). As used herein, the term “information related to a subject” refers to facts or data pertaining to a subject (e.g., a human, plant, or animal). The term “genomic information” refers to information pertaining to a genome including, but not limited to, nucleic acid sequences, genes, percentage methylation, allele frequencies, RNA expression levels, protein expression, phenotypes correlating to genotypes, etc. “Allele frequency information” refers to facts or data pertaining to allele frequencies, including, but not limited to, allele identities, statistical correlations between the presence of an allele and a characteristic of a subject (e.g, 2026204791   22 Jun 2026 a human subject), the presence or absence of an allele in an individual or population, the percentage likelihood of an allele being present in an individual having one or more particular characteristics, etc. DETAILED DESCRIPTION In this detailed description of the various embodiments, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of the embodiments disclosed. One skilled in the art will appreciate, however, that these various embodiments may be practiced with or without these specific details. In other instances, structures and devices are shown in block diagram form. Furthermore, one skilled in the art can readily appreciate that the specific sequences in which methods are presented and performed are illustrative and it is contemplated that the sequences can be varied and still remain within the spirit and scope of the various embodiments disclosed herein. Provided herein is technology for EC screening and particularly, but not exclusively, to methods, compositions, and related uses for detecting the presence of EC and / or specific forms of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC). As the technology is described herein, the section headings used are for organizational purposes only and are not to be construed as limiting the subject matter in any way. Indeed, as described in Examples 1, 2 and 3, experiments conducted during the course for identifying embodiments for the present invention identified a novel set of 499 differentially methylated regions (DMRs) for discriminating cancer of the endometrium derived DNA from non-neoplastic control DNA. From these 499 novel DNA methylation markers, further experiments identified markers capable of distinguishing different types of EC from normal endometrial tissue. For example, separate sets of DMRs were identified capable of distinguishing 1) EC from normal endometrial tissue; 2) clear cell EC from normal endometrial tissue; 3) serous EC from normal endometrial tissue; 4) carcinosarcoma EC from normal endometrial tissue; and 5) endometrioid EC from normal endometrial tissue. Although the disclosure herein refers to certain illustrated embodiments, it is to be understood that these embodiments are presented by way of example and not by way of limitation. In particular aspects, the present technology provides compositions and methods for identifying, determining, and / or classifying a cancer such as EC. The methods comprise determining the methylation status of at least one methylation marker in a biological sample 2026204791   22 Jun 2026 isolated from a subject (e.g., stool sample, endometrial tissue sample, plasma sample), wherein a change in the methylation state of the marker is indicative of the presence, class, or site of EC. Particular embodiments relate to markers comprising a differentially methylated region (DMR, e.g., DMR 1-499, see Tables 1, 8 and 21) that are used for diagnosis (e.g., screening) of EC and various types of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC). In addition to embodiments wherein the methylation analysis of at least one marker, a region of a marker, or a base of a marker comprising a DMR (e.g., DMR, e.g., DMR 1-499) provided herein and listed in Tables 1, 8 and 21 is analyzed, the technology also provides panels of markers comprising at least one marker, region of a marker, or base of a marker comprising a DMR with utility for the detection of cancers, in particular EC. Some embodiments of the technology are based upon the analysis of the CpG methylation status of at least one marker, region of a marker, or base of a marker comprising a DMR. In some embodiments, the present technology provides for the use of a reagent that modifies DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent) in combination with one or more methylation assays to determine the methylation status of CpG dinucleotide sequences within at least one marker comprising a DMR (e.g., DMR 1-499, see Tables 1, 8 and 21). Genomic CpG dinucleotides can be methylated or unmethylated (alternatively known as up- and down-methylated respectively). However the methods of the present invention are suitable for the analysis of biological samples of a heterogeneous nature, e.g., a low concentration of tumor cells, or biological materials therefrom, within a background of a remote sample (e.g., blood, organ effluent, or stool). Accordingly, when analyzing the methylation status of a CpG position within such a sample one may use a quantitative assay for determining the level (e.g., percent, fraction, ratio, proportion, or degree) of methylation at a particular CpG position. According to the present technology, determination of the methylation status of CpG dinucleotide sequences in markers comprising a DMR has utility both in the diagnosis and characterization of cancers such as EC. 2026204791   22 Jun 2026 Combinations of markers In some embodiments, the technology relates to assessing the methylation state of combinations of markers comprising a DMR from Tables 1, 8 and 21 (e.g., DMRNos. 1499). In some embodiments, assessing the methylation state of more than one marker increases the specificity and / or sensitivity of a screen or diagnostic for identifying a neoplasm in a subject (e.g., EC). Various cancers are predicted by various combinations of markers, e.g., as identified by statistical techniques related to specificity and sensitivity of prediction. The technology provides methods for identifying predictive combinations and validated predictive combinations for some cancers. Methods for assaying methylation state In certain embodiments, methods for analyzing a nucleic acid for the presence of 5-methylcytosine involves treatment of DNA with a reagent that modifies DNA in a methylation-specific manner. Examples of such reagents include, but are not limited to, a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent. A frequently used method for analyzing a nucleic acid for the presence of 5-methylcytosine is based upon the bisulfite method described by Frommer, et al. for the detection of 5-methylcytosines in DNA (Frommer et al. (1992) Proc. Natl. Acad. Set. USA 89: 1827-31 explicitly incorporated herein by reference in its entirety for all purposes) or variations thereof. The bisulfite method of mapping 5-methylcytosines is based on the observation that cytosine, but not 5-methylcytosine, reacts with hydrogen sulfite ion (also known as bisulfite). The reaction is usually performed according to the following steps: first, cytosine reacts with hydrogen sulfite to form a sulfonated cytosine. Next, spontaneous deamination of the sulfonated reaction intermediate results in a sulfonated uracil. Finally, the sulfonated uracil is desulfonated under alkaline conditions to form uracil. Detection is possible because uracil base pairs with adenine (thus behaving like thymine), whereas 5-methylcytosine base pairs with guanine (thus behaving like cytosine). This makes the discrimination of methylated cytosines from non-methylated cytosines possible by, e.g., bisulfite genomic sequencing (Grigg G, & Clark S, Bioessays (1994) 16: 431-36; Grigg G, DNA Seq. (1996) 6: 189-98),methylation-specific PCR (MSP) as is disclosed, e.g, in U.S. Patent No. 5,786,146, or using an assay comprising sequence-specific probe cleavage, e.g., a 2026204791   22 Jun 2026 QuARTS flap endonuclease assay (see, e.g, Zou et al. (2010) “Sensitive quantification of methylated markers with a novel methylation specific technology” Clin Chem 56: Al 99; and in U.S. Pat. Nos. 8,361,720; 8,715,937; 8,916,344; and 9,212,392. Some conventional technologies are related to methods comprising enclosing the DNA to be analyzed in an agarose matrix, thereby preventing the diffusion and renaturation of the DNA (bisulfite only reacts with single-stranded DNA), and replacing precipitation and purification steps with a fast dialysis (Olek A, et al. (1996) “A modified and improved method for bisulfite based cytosine methylation analysis” Nucleic Acids Res. 24: 5064-6). It is thus possible to analyze individual cells for methylation status, illustrating the utility and sensitivity of the method. An overview of conventional methods for detecting 5-methylcytosine is provided by Rein, T., et al. (1998) Nucleic Acids Res. 26: 2255. The bisulfite technique typically involves amplifying short, specific fragments of a known nucleic acid subsequent to a bisulfite treatment, then either assaying the product by sequencing (Olek & Walter (1997) Nat. Genet. 17: 275-6) or a primer extension reaction (Gonzalgo & Jones (1997) Nucleic Acids Res. 25: 2529-31; WO 95 / 00669; U.S. Pat. No. 6,251,594) to analyze individual cytosine positions. Some methods use enzymatic digestion (Xiong & Laird (1997) Nucleic Acids Res. 25: 2532-4). Detection by hybridization has also been described in the art (Olek et al., WO 99 / 28498). Additionally, use of the bisulfite technique for methylation detection with respect to individual genes has been described (Grigg & Clark (1994) Bioessays 16: 431-6; Zeschnigk et al. (1991) Hum Mol Genet. 6: 38795; Feil et al. (1994) Nucleic Acids Res. 22: 695; Martin et al. (1995) Gene 157: 261-4; WO 9746705; WO 9515373). Various methylation assay procedures can be used in conjunction with bisulfite treatment according to the present technology. These assays allow for determination of the methylation state of one or a plurality of CpG dinucleotides (e.g, CpG islands) within a nucleic acid sequence. Such assays involve, among other techniques, sequencing of bisulfite-treated nucleic acid, PCR (for sequence-specific amplification), Southern blot analysis, and use of methylation-specific restriction enzymes, e.g., methylation-sensitive or methylationdependent enzymes. For example, genomic sequencing has been simplified for analysis of methylation patterns and 5-methylcytosine distributions by using bisulfite treatment (Frommer et al. (1992) Proc. Natl. Acad. Sci. USA 89: 1827-1831). Additionally, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA finds use in assessing 2026204791   22 Jun 2026 methylation state, e.g., as described by Sadri & Hornsby (1997) Nucl. Acids Res. 24: 50585059 or as embodied in the method known as COBRA (Combined Bisulfite Restriction Analysis) (Xiong & Laird (1997) Nucleic Acids Res. 25: 2532-2534). COBRA™ analysis is a quantitative methylation assay useful for determining DNA methylation levels at specific loci in small amounts of genomic DNA (Xiong & Laird, Nucleic Acids Res. 25:2532-2534, 1997). Briefly, restriction enzyme digestion is used to reveal methylation-dependent sequence differences in PCR products of sodium bisulfite-treated DNA. Methylation-dependent sequence differences are first introduced into the genomic DNA by standard bisulfite treatment according to the procedure described by Frommer et al. (Proc. Natl. Acad. Sci. USA 89:1827-1831, 1992). PCR amplification of the bisulfite converted DNA is then performed using primers specific for the CpG islands of interest, followed by restriction endonuclease digestion, gel electrophoresis, and detection using specific, labeled hybridization probes. Methylation levels in the original DNA sample are represented by the relative amounts of digested and undigested PCR product in a linearly quantitative fashion across a wide spectrum of DNA methylation levels. In addition, this technique can be reliably applied to DNA obtained from microdissected paraffin-embedded tissue samples. Typical reagents (e.g., as might be found in a typical COBRA™-based kit) for COBRA™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence , CpG island, etc.); restriction enzyme and appropriate buffer; gene-hybridization oligonucleotide; control hybridization oligonucleotide; kinase labeling kit for oligonucleotide probe; and labeled nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery reagents or kits (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components. Assays such as “Methy Light™” (a fluorescence-based real-time PCR technique) (Eads et al., Cancer Res. 59:2302-2306, 1999), Ms-SNuPE™ (Methylation-sensitive Single Nucleotide Primer Extension) reactions (Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531, 1997), methylation-specific PCR (“MSP”; Herman et al., Proc. Natl. Acad. Sci. USA 93:9821-9826, 1996; U.S. Pat. No. 5,786,146), and methylated CpG island amplification (“MCA”; Toyota et al., Cancer Res. 59:2307-12, 1999) are used alone or in combination with one or more of these methods. 2026204791   22 Jun 2026 The “HeavyMethyl™” assay, technique is a quantitative method for assessing methylation differences based on methylation-specific amplification of bisulfite-treated DNA. Methylation-specific blocking probes (“blockers”) covering CpG positions between, or covered by, the amplification primers enable methylation-specific selective amplification of a nucleic acid sample. The term “HeavyMethyl™ MethyLight™” assay refers to a HeavyMethyl™ MethyLight™ assay, which is a variation of the MethyLight™ assay, wherein the MethyLight™ assay is combined with methylation specific blocking probes covering CpG positions between the amplification primers. The HeavyMethyl™ assay may also be used in combination with methylation specific amplification primers. Typical reagents (e.g, as might be found in a typical MethyLight™-based kit) for HeavyMethyl™ analysis may include, but are not limited to: PCR primers for specific loci (e.g, specific genes, markers, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, or bisulfite treated DNA sequence or CpG island, etc.); blocking oligonucleotides; optimized PCR buffers and deoxynucleotides; and Taq polymerase. MSP (methylation-specific PCR) allows for assessing the methylation status of virtually any group of CpG sites within a CpG island, independent of the use of methylation-sensitive restriction enzymes (Herman et al. Proc. Natl. Acad. Sci. USA 93:9821-9826, 1996; U.S. Pat. No. 5,786,146). Briefly, DNA is modified by sodium bisulfite, which converts unmethylated, but not methylated cytosines, to uracil, and the products are subsequently amplified with primers specific for methylated versus unmethylated DNA. MSP requires only small quantities of DNA, is sensitive to 0.1% methylated alleles of a given CpG island locus, and can be performed on DNA extracted from paraffin-embedded samples. Typical reagents (e.g, as might be found in a typical MSP-based kit) for MSP analysis may include, but are not limited to: methylated and unmethylated PCR primers for specific loci (e.g, specific genes, markers, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); optimized PCR buffers and deoxynucleotides, and specific probes. The MethyLight™ assay is a high-throughput quantitative methylation assay that utilizes fluorescence-based real-time PCR (e.g, TaqMan®) that requires no further manipulations after the PCR step (Eads et al., Cancer Res. 59:2302-2306, 1999). Briefly, the MethyLight™ process begins with a mixed sample of genomic DNA that is converted, in a sodium bisulfite reaction, to a mixed pool of methylation-dependent sequence differences according to standard procedures (the bisulfite process converts unmethylated cytosine 2026204791   22 Jun 2026 residues to uracil). Fluorescence-based PCR is then performed in a “biased” reaction, e.g., with PCR primers that overlap known CpG dinucleotides. Sequence discrimination occurs both at the level of the amplification process and at the level of the fluorescence detection process. The MethyLight™ assay is used as a quantitative test for methylation patterns in a nucleic acid, e.g, a genomic DNA sample, wherein sequence discrimination occurs at the level of probe hybridization. In a quantitative version, the PCR reaction provides for a methylation specific amplification in the presence of a fluorescent probe that overlaps a particular putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primers, nor the probe, overlie any CpG dinucleotides. Alternatively, a qualitative test for genomic methylation is achieved by probing the biased PCR pool with either control oligonucleotides that do not cover known methylation sites (e.g., a fluorescence-based version of the Heavy Methyl™ and MSP techniques) or with oligonucleotides covering potential methylation sites. The MethyLight™ process is used with any suitable probe (e.g. a “TaqMan®” probe, a Lightcycler® probe, etc.) For example, in some applications double-stranded genomic DNA is treated with sodium bisulfite and subjected to one of two sets of PCR reactions using TaqMan® probes, e.g, with MSP primers and / or Heavy Methyl blocker oligonucleotides and a TaqMan® probe. The TaqMan® probe is dual-labeled with fluorescent “reporter” and “quencher” molecules and is designed to be specific for a relatively high GC content region so that it melts at about a 10°C higher temperature in the PCR cycle than the forward or reverse primers. This allows the TaqMan® probe to remain fully hybridized during the PCR annealing / extension step. As the Taq polymerase enzymatically synthesizes anew strand during PCR, it will eventually reach the annealed TaqMan® probe. The Taq polymerase 5' to 3' endonuclease activity will then displace the TaqMan® probe by digesting it to release the fluorescent reporter molecule for quantitative detection of its now unquenched signal using a real-time fluorescent detection system. Typical reagents (e.g, as might be found in a typical Methy Light™-based kit) for MethyLight™ analysis may include, but are not limited to: PCR primers for specific loci (e.g, specific genes, markers, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); TaqMan® or Lightcycler® probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase. 2026204791   22 Jun 2026 The QM™ (quantitative methylation) assay is an alternative quantitative test for methylation patterns in genomic DNA samples, wherein sequence discrimination occurs at the level of probe hybridization. In this quantitative version, the PCR reaction provides for unbiased amplification in the presence of a fluorescent probe that overlaps a particular putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primers, nor the probe, overlie any CpG dinucleotides. Alternatively, a qualitative test for genomic methylation is achieved by probing the biased PCR pool with either control oligonucleotides that do not cover known methylation sites (a fluorescence-based version of the HeavyMethyl™ and MSP techniques) or with oligonucleotides covering potential methylation sites. The QM™ process can be used with any suitable probe, e.g., “TaqMan®” probes, Lightcycler® probes, in the amplification process. For example, double-stranded genomic DNA is treated with sodium bisulfite and subjected to unbiased primers and the TaqMan® probe. The TaqMan® probe is dual-labeled with fluorescent “reporter” and “quencher” molecules, and is designed to be specific for a relatively high GC content region so that it melts out at about a 10°C higher temperature in the PCR cycle than the forward or reverse primers. This allows the TaqMan® probe to remain fully hybridized during the PCR annealing / extension step. As the Taq polymerase enzymatically synthesizes anew strand during PCR, it will eventually reach the annealed TaqMan® probe. The Taq polymerase 5' to 3' endonuclease activity will then displace the TaqMan® probe by digesting it to release the fluorescent reporter molecule for quantitative detection of its now unquenched signal using a real-time fluorescent detection system. Typical reagents (e.g., as might be found in a typical QM™-based kit) for QM™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); TaqMan® or Lightcycler® probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase. The Ms-SNuPE™ technique is a quantitative method for assessing methylation differences at specific CpG sites based on bisulfite treatment of DNA, followed by singlenucleotide primer extension (Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531, 1997). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil while leaving 5-methylcytosine unchanged. Amplification of the desired target sequence is then performed using PCR primers specific for bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the CpG site of 2026204791   22 Jun 2026 interest. Small amounts of DNA can be analyzed (e.g., microdissected pathology sections) and it avoids utilization of restriction enzymes for determining the methylation status at CpG sites. Typical reagents (e.g, as might be found in a typical Ms-SNuPE™-based kit) for Ms-SNuPE™ analysis may include, but are not limited to: PCR primers for specific loci (e.g, specific genes, markers, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); optimized PCR buffers and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE™ primers for specific loci; reaction buffer (for the Ms-SNuPE reaction); and labeled nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery reagents or kit (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components. Reduced Representation Bisulfite Sequencing (RRBS) begins with bisulfite treatment of nucleic acid to convert all unmethylated cytosines to uracil, followed by restriction enzyme digestion (e.g., by an enzyme that recognizes a site including a CG sequence such as MspI) and complete sequencing of fragments after coupling to an adapter ligand. The choice of restriction enzyme enriches the fragments for CpG dense regions, reducing the number of redundant sequences that may map to multiple gene positions during analysis. As such, RRBS reduces the complexity of the nucleic acid sample by selecting a subset (e.g, by size selection using preparative gel electrophoresis) of restriction fragments for sequencing. As opposed to whole-genome bisulfite sequencing, every fragment produced by the restriction enzyme digestion contains DNA methylation information for at least one CpG dinucleotide. As such, RRBS enriches the sample for promoters, CpG islands, and other genomic features with a high frequency of restriction enzyme cut sites in these regions and thus provides an assay to assess the methylation state of one or more genomic loci. A typical protocol for RRBS comprises the steps of digesting a nucleic acid sample with a restriction enzyme such as MspI, filling in overhangs and A-tailing, ligating adaptors, bisulfite conversion, and PCR. See, e.g, et al. (2005) “Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution” Nat Methods 7: 133-6; Meissner et al. (2005) “Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis” Nucleic Acids Res. 33: 5868-77. In some embodiments, a quantitative allele-specific real-time target and signal amplification (QuARTS) assay is used to evaluate methylation state. Three reactions 2026204791   22 Jun 2026 sequentially occur in each QuARTS assay, including amplification (reaction 1) and target probe cleavage (reaction 2) in the primary reaction; and FRET cleavage and fluorescent signal generation (reaction 3) in the secondary reaction. When target nucleic acid is amplified with specific primers, a specific detection probe with a flap sequence loosely binds to the amplicon. The presence of the specific invasive oligonucleotide at the target binding site causes a 5' nuclease, e.g., a FEN-1 endonuclease, to release the flap sequence by cutting between the detection probe and the flap sequence. The flap sequence is complementary to a non-hairpin portion of a corresponding FRET cassette. Accordingly, the flap sequence functions as an invasive oligonucleotide on the FRET cassette and effects a cleavage between the FRET cassette fluorophore and a quencher, which produces a fluorescent signal. The cleavage reaction can cut multiple probes per target and thus release multiple fluorophore per flap, providing exponential signal amplification. QuARTS can detect multiple targets in a single reaction well by using FRET cassettes with different dyes. See, e.g., in Zou et al. (2010) “Sensitive quantification of methylated markers with a novel methylation specific technology” Clin Chem 56: A199), and U.S. Pat. Nos. 8,361,720; 8,715,937; 8,916,344; and 9,212,392, each of which is incorporated herein by reference for all purposes. The term “bisulfite reagent” refers to a reagent comprising bisulfite, disulfite, hydrogen sulfite, or combinations thereof, useful as disclosed herein to distinguish between methylated and unmethylated CpG dinucleotide sequences. Methods of said treatment are known in the art (e.g., PCT / EP2004 / 011715 and WO 2013 / 116375, each of which is incorporated by reference in its entirety). In some embodiments, bisulfite treatment is conducted in the presence of denaturing solvents such as but not limited to n-alkyleneglycol or diethylene glycol dimethyl ether (DME), or in the presence of dioxane or dioxane derivatives. In some embodiments the denaturing solvents are used in concentrations between 1% and 35% (v / v). In some embodiments, the bisulfite reaction is carried out in the presence of scavengers such as but not limited to chromane derivatives, e.g., 6-hydroxy-2,5,7,8,-tetramethylchromane 2-carboxylic acid or trihydroxybenzone acid and derivates thereof, e.g, Gallic acid (see: PCT / EP2004 / 011715, which is incorporated by reference in its entirety). In certain preferred embodiments, the bisulfite reaction comprises treatment with ammonium hydrogen sulfite, e.g, as described in WO 2013 / 116375. In some embodiments, fragments of the treated DNA are amplified using sets of primer oligonucleotides according to the present invention (e.g., see Tables 10, 19 and 20) and an amplification enzyme. The amplification of several DNA segments can be carried out 2026204791   22 Jun 2026 simultaneously in one and the same reaction vessel. Typically, the amplification is carried out using a polymerase chain reaction (PCR). Amplicons are typically 100 to 2000 base pairs in length. In another embodiment of the method, the methylation status of CpG positions within or near a marker comprising a DMR (e.g., DMR 1-499, Tables 1, 8 and 21) may be detected by use of methylation-specific primer oligonucleotides. This technique (MSP) has been described in U.S. Pat. No. 6,265,171 to Herman. The use of methylation status specific primers for the amplification of bisulfite treated DNA allows the differentiation between methylated and unmethylated nucleic acids. MSP primer pairs contain at least one primer that hybridizes to a bisulfite treated CpG dinucleotide. Therefore, the sequence of said primers comprises at least one CpG dinucleotide. MSP primers specific for non-methylated DNA contain a “T” at the position of the C position in the CpG. The fragments obtained by means of the amplification can carry a directly or indirectly detectable label. In some embodiments, the labels are fluorescent labels, radionuclides, or detachable molecule fragments having a typical mass that can be detected in a mass spectrometer. Where said labels are mass labels, some embodiments provide that the labeled amplicons have a single positive or negative net charge, allowing for better detectability in the mass spectrometer. The detection may be carried out and visualized by means of, e.g., matrix assisted laser desorption / ionization mass spectrometry (MALDI) or using electron spray mass spectrometry (ESI). Methods for isolating DNA suitable for these assay technologies are known in the art. In particular, some embodiments comprise isolation of nucleic acids as described in U.S. Pat. Appl. Ser. No. 13 / 470,251 (“Isolation of Nucleic Acids”), incorporated herein by reference in its entirety. In some embodiments, the markers described herein find use in QUARTS assays performed on stool samples. In some embodiments, methods for producing DNA samples and, in particular, to methods for producing DNA samples that comprise highly purified, low-abundance nucleic acids in a small volume (e.g., less than 100, less than 60 microliters) and that are substantially and / or effectively free of substances that inhibit assays used to test the DNA samples (e.g, PCR, INVADER, QuARTS assays, etc.) are provided. Such DNA samples find use in diagnostic assays that qualitatively detect the presence of, or quantitatively measure the activity, expression, or amount of, a gene, a gene variant (e.g., an allele), or a gene modification (e.g., methylation) present in a sample taken from a patient. 2026204791   22 Jun 2026 For example, some cancers are correlated with the presence of particular mutant alleles or particular methylation states, and thus detecting and / or quantifying such mutant alleles or methylation states has predictive value in the diagnosis and treatment of cancer. Many valuable genetic markers are present in extremely low amounts in samples and many of the events that produce such markers are rare. Consequently, even sensitive detection methods such as PCR require a large amount of DNA to provide enough of a low-abundance target to meet or supersede the detection threshold of the assay. Moreover, the presence of even low amounts of inhibitory substances compromise the accuracy and precision of these assays directed to detecting such low amounts of a target. Accordingly, provided herein are methods providing the requisite management of volume and concentration to produce such DNA samples. In some embodiments, the sample comprises blood, serum, leukocytes, plasma, or saliva. In some embodiments, the subject is human. Such samples can be obtained by any number of means known in the art, such as will be apparent to the skilled person. Cell free or substantially cell free samples can be obtained by subjecting the sample to various techniques known to those of skill in the art which include, but are not limited to, centrifugation and filtration. Although it is generally preferred that no invasive techniques are used to obtain the sample, it still may be preferable to obtain samples such as tissue homogenates, tissue sections, and biopsy specimens. The technology is not limited in the methods used to prepare the samples and provide a nucleic acid for testing. For example, in some embodiments, a DNA is isolated from a stool sample or from blood or from a plasma sample using direct gene capture, e.g, as detailed in U.S. Pat. Nos. 8,808,990 and 9,169,511, and in WO 2012 / 155072, or by a related method. The analysis of markers can be carried out separately or simultaneously with additional markers within one test sample. For example, several markers can be combined into one test for efficient processing of multiple samples and for potentially providing greater diagnostic and / or prognostic accuracy. In addition, one skilled in the art would recognize the value of testing multiple samples (for example, at successive time points) from the same subject. Such testing of serial samples can allow the identification of changes in marker methylation states over time. Changes in methylation state, as well as the absence of change in methylation state, can provide useful information about the disease status that includes, but is not limited to, identifying the approximate time from onset of the event, the presence and 2026204791   22 Jun 2026 amount of salvageable tissue, the appropriateness of drug therapies, the effectiveness of various therapies, and identification of the subject's outcome, including risk of future events. The analysis of biomarkers can be carried out in a variety of physical formats. For example, the use of microtiter plates or automation can be used to facilitate the processing of large numbers of test samples. Alternatively, single sample formats could be developed to facilitate immediate treatment and diagnosis in a timely fashion, for example, in ambulatory transport or emergency room settings. It is contemplated that embodiments of the technology are provided in the form of a kit. The kits comprise embodiments of the compositions, devices, apparatuses, etc. described herein, and instructions for use of the kit. Such instructions describe appropriate methods for preparing an analyte from a sample, e.g., for collecting a sample and preparing a nucleic acid from the sample. Individual components of the kit are packaged in appropriate containers and packaging (e.g., vials, boxes, blister packs, ampules, jars, bottles, tubes, and the like) and the components are packaged together in an appropriate container (e.g, a box or boxes) for convenient storage, shipping, and / or use by the user of the kit. It is understood that liquid components (e.g, a buffer) may be provided in a lyophilized form to be reconstituted by the user. Kits may include a control or reference for assessing, validating, and / or assuring the performance of the kit. For example, a kit for assaying the amount of a nucleic acid present in a sample may include a control comprising a known concentration of the same or another nucleic acid for comparison and, in some embodiments, a detection reagent (e.g., a primer) specific for the control nucleic acid. The kits are appropriate for use in a clinical setting and, in some embodiments, for use in a user's home. The components of a kit, in some embodiments, provide the functionalities of a system for preparing a nucleic acid solution from a sample. In some embodiments, certain components of the system are provided by the user. Methods In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at 2026204791   22 Jun 2026 least one marker selected from a chromosomal region having an annotation selected from the group consisting of AFF3, AIM1A, AMIGO3A, BMP4B, C17orfl07_A, Clorf70_B, C5orf52, CLDN7, DIDO 1 A, EEF1A2, EMX2OS, FEV, FKBP11A, GDF6, GDF7A, JSRP1A, KCTD15A, KLHL21, LRRC8DA, NBPF8, MAX.chrlO.130339363-130339534, MAX.chrl0.22624479-22624553, MAX. chr!4.103021656-103021718, MAX. chr8.145103829-145103992, MAX. chr8.145104263-145104422, MDFI B, MIAT A, MMP23B, NDRG2, OBSCN A, PCOLCE, PYCARD, SEPT9 B, SLC6A3 A, SLC8A3 B, SQSTM1, VILL, ZNF302, ZNF323_A, ZNF506, and ZNF90, and 2) detecting EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting of EMX2OS, CYTH2, C17orfl07_A, DIDO 1 A, GDF6, NBPF8, MAX.chrl4.103021656-103021718, JSRP1_A, GATA2 B, and SFMBT2 B, and 2) detecting EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting of SFMBT2_B, ZNF90, MAX.chr8.145103829- 2026204791   22 Jun 2026 145103992, CYTH2, LRRC8DA, OBSCN_A, DIDO1_A, MAX.chr10.22624479-22624553, JSRP1A, EMX2OS, NBPF8, and MPZ A, and 2) detecting EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting of EMX2OS, CYTH2, NBPF8, MAX.chrl0.22624479-22624553,and 2) detecting EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting of ANKRD35, ARL5C, ARRB1, BCL2L11A, BCL2L11B, BCL2L11_C, BZRAP1, C16orf54, C17orfl01, C6orfl32, CACNA2D4, DEDD2, EPS15L1, FAIM2, FAM125B, FAM189B, FAM78A, FOXP4, GYPC A, GYPC B, IFFO1A, IFFO1B, ITPKA, KLF16, LIMD2, LOC389333, LOC440925_A, LOC646278, LYL1, LYPLAL1, MAX.chrl 1.32355226-32355251, MAX.chrl4.102172621-102172686, MAX.chrl4.105512122-105512239, MAX.chrl5.95128144-95128248, MAX.chrl6.11327016-11327312, MAX.chr3.187676577-187676668, MAX.chr4.174430676-174430847, 2026204791   22 Jun 2026 MAX. chr8.145900783-145900914, MAX.chr8.80804237-80804301, N4BP3, NCOR2, NFATC1A, NFATC1B, NKX2-6, NR2F6, OSM, PALLDC, PIK3CD, PRKAR1B, RAD52, STX16_A, SUCLG2, TNFRSF1B, TNFRSF4, ZDHHC18, and ZNF671_A, and 2) detecting EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting of DIDO1A, NDRG4, MAX.chrl4.103021656-103021718, MMP23B, EMX2OS, SEPT9 B, NBPF8, EEF1A2, AIM1A, BMP4 B, MAX.chr8.145103829-145103992, OBSCN, PYCARD, GDF6, MDFI B, MIAT A, SCL8A3, ZNF323 A, SQSTM1, AFF3, Clorf70, GDF7 A, JSRP1A, LRRC8D A, FEV, and MAX.chr8.145104263-145104422, and 2) detecting clear cell EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting ofZNF323_A, MAX.chr7.104624356-104624730, NDRG2, DIDO1A, MDFI B, MAX.chrl4.103021656-103021718, MMP23B, SEPT9 B, and STX16 A, and 2026204791   22 Jun 2026 2) detecting clear cell EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting SFMBT2B, SQSTM1, ZNF323_A, ZNF90, MAX. chr8.145103 829-145103 992, CYTH2, LRRC8D A, OBSCN_A, DIDO1_A, MDFI B, GDF7A, MAX.chrl0.22624479-22624553, JSRP1_A, MAX.chrl4.103021656-103021718, EMX2OS, LRRC34, NBPF8, SEPT9_B, EEF1A2, LRRC41C, VILL, and MPZ_A, and 2) detecting clear cell EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting MAX.chr7:104624386-104624529, EMX2OS, DIDO1_B, and OBSCN B, and 2) detecting clear cell EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 2026204791   22 Jun 2026 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample)) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting SFMBT2B, SQSTM1, ZNF323_A, ZNF506, ZNF90, CLDN7, LRRC41B, MAX. chr7.104624356-104624730, NDRG2, CYP11A1, MAX.chr8.145103829-145103992, CYTH2, LRRC8DA, MAX.chr8.145104263-145104422, OBSCNA, DIDO1_A, GDF6, MAX.chrlO. 130339363-130339534, MDFI B, DLL4, GDF7 A, MIAT A, PYCARD, BMP4 B, JSRP1A, MAX.chrl4.103021656-103021718, EMX2, MMP23B, EMX2OS, MAX. chr!7.73073716-73073814, NBPF8, SEPT9_B, LOC440925_A, STX16_A, ITPKA, EEF1A2, FEV, LRRC41C, and NFIC, and 2) detecting clear cell EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting EMX2OS, DIDO 1 A, SBNO2, AMIGO3 A, PCOLCE, CLDN7, CYTH2, OBSCN A, AHSA2, DLL4, EMX2, MAX.chrl4.74100620-74100870, LRRC4, PPP2R5C_A, SQSTM1, MAX.chrl7.73073716-73073814, CYP11A1, ACOXL A, and AIM1B, and 2) detecting carcinosarcoma EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 2026204791   22 Jun 2026 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting EMX2OS, and LRRC34, and 2) detecting carcinosarcoma EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting ZNF5 06, ZNF90, MAX.chr8.145103829-145103992, LRRC8D A, OBSCN A, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and VILL, and 2) detecting carcinosarcoma EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting TRH, MAX.chr7:104624386-104624529, EMX2OS, DIDO I B, and ST3GAL2 B, and 2) detecting carcinosarcoma EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). 2026204791   22 Jun 2026 In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample)) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting SFMBT2B, SMTN, ZNF506, ZNF90, CLDN7, LRRC41 B, CYP11A1, MAX. chr8.145103829-145103992, AHSA2, CYTH2, GATA2B, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, DLL4, MAX.chrl0.22624479-22624553, PYCARD, BMP4_B, JSRP1_A, MAX.chrl4.103021656-103021718, MIATB, EMX2OS, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, and VILL, and 2) detecting carcinosarcoma EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting EMX2OS, KANKI, Clorf70_B, AMIGO3 A, DIDO 1 A, LRRC41C, NFIC, FKBP11A, C17orfl07_A, SMTN, LRRC41B, LRRC8D A, OBSCN A, MAX. chr7.104624356-104624730, MIAT B, and 2) detecting serous EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 2026204791   22 Jun 2026 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting MAX. chr7.104624356-104624730, EMX2OS, and LRRC41C, and 2) detecting serous EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO 1 A, EMX2OS, LRRC41C, and VILL, and 2) detecting serous EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting EMX2OS, and LRRC41D, and 2) detecting serous EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). 2026204791   22 Jun 2026 In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample)) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting SFMBT2B, SMTN, SQSTM1, ZNF90, CLDN7, LRRC41 B, MAX. chr7.104624356-104624730, CYP11A1, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, LRRC8DA, MAX.chr8.145104263-145104422, OBSCNA, GDGF6, DLL4, PYCARD, BMP4 B, JSRP1A, MIAT B, KANKI, EMX2OS, NBPF8, LOC440925_A, ITPKA, EEF1A2, FEV, LRRC41C, NFIC, VILL, MPZ_A, and 2) detecting serous EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting MAX.chrl0.130339363-130339534, SFMBT2_C, CYTH2, SLC6A3, VILL, EMX2OS, MAX.chrl0.22624479-22624553, GDF6, ZNF90, ZNF506, JSRP1A, c5orf52, SFMBT2 B, NBPF8, RHBDL1A, DIDO 1 A, KANKI, and GATA2B, and 2) detecting endometrioid EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 2026204791   22 Jun 2026 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting MAX.chr8.145103829-145103992, CYTH2, DIDO1_A, MAX.chrl0.22624479-22624553, JSRP1_A, SBNO2, NBPF8, and VILL, and 2) detecting endometrioid EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, MAX.chr8.145104263-145104422, OBSCN_A, MAX.chr10.22624479-22624553, JSRP1A, EMX2OS, NBPF8, and MPZ_A, and 2) detecting endometrioid EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from a blood sample (e.g., plasma sample, whole blood sample, leukocyte sample, serum sample)) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting SFMBT2 B, SMTN, SQSTM1, ZNF506, ZNF90, CLDN7, LRRC41B, FKBP11A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2B, 2026204791   22 Jun 2026 LRRC8DA, MAX. chr8.145104263-145104422, DIDO1_A, GDF6, MAX.chrlO.130339363-130339534, DLL4, MAX.chrl0.22624479-22624553, MIATA, PYCARD, BMP4_B, JSRP1_A, MAX.chrl4.103021656-103021718, MIATB, KANKI, SBNO2, c5orf52, EMX2O6, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, VILL, and MPZ A, and 2) detecting endometrioid EC (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting TSPYL5, TRH, JAM3, FAM19A5, PTGDR, SFMBT2 E, JSRP1B, andARL5C, and 2) detecting endometrioid EC Grade 1 (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting TSPYL5, MPZ_B, TRH, CNTN4, FAM19A5, GLT1D1, RYR2 F, PTGDR, EMX2OS, MAX.chrl0:22624470-22624553, SPDYA B, SFMBT2 E, and JSRP1_B, and 2) detecting endometrioid EC Grade 2 (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). 2026204791   22 Jun 2026 In some embodiments of the technology, methods are provided that comprise the following steps: 1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated from endometrial tissue) obtained from the subject with at least one reagent or series of reagents that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker selected from a chromosomal region having an annotation selected from the group consisting TSPYL5, MPZB, TRH, and PTGDR, and 2) detecting endometrioid EC Grade 3 (e.g., afforded with a sensitivity of greater than or equal to 80% and a specificity of greater than or equal to 80%). In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with a reagent that modifies DNA in a methylation-specific manner (e.g., wherein the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein the one or more genes is selected from one of the following groups: (i) AFF3, AIM1A, AMIGO3 A, BMP4 B, C17orfl07_A, Clorf70_B, C5orf52, CLDN7, DIDO 1 A, EEF1A2, EMX2OS, FEV, FKBP11A, GDF6, GDF7 A, JSRP1A, KCTD15 A, KLHL21, LRRC8D A, NBPF8, MAX.chrl0.130339363-130339534, MAX.chrl0.22624479-22624553, MAX.chrl4.103021656-103021718, MAX. chr8.145103829-145103992, MAX. chr8.145104263-145104422, MDFI B, MIAT A, MMP23B, NDRG2, OBSCNA, PCOLCE, PYCARD, SEPT9B, SLC6A3A, SLC8A3B, SQSTM1, VILL, ZNF302, ZNF323 A, ZNF506, and ZNF90; (ii) EMX2OS, CYTH2, C17orfl07_A, DIDO1A, GDF6, NBPF8, MAX.chr!4.103021656-103021718, JSRP1_A, GATA2B, and SFMBT2B; (iii) SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO 1 A, MAX.chrl0.22624479-22624553, JSRP1A, EMX2OS, NBPF8, and MPZ_A; 2026204791   22 Jun 2026 (iv) EMX2OS, CYTH2, NBPF8, MAX.chrl0.22624479-22624553; and (v) ANKRD35, ARL5C, ARRB1, BCL2L11A, BCL2L11B, BCL2L11C, BZRAP1, C16orf54, C17orfl01, C6orfl32, CACNA2D4, DEDD2, EPS15L1, FAIM2, FAM125B, FAM189B, FAM78A, FOXP4, GYPC A, GYPC B, IFFO1A, IFFO1B, ITPKA, KLF16, LIMD2, LOC389333, LOC440925_A, LOC646278, LYL1, LYPLAL1, MAX. chrl 1.32355226-32355251, MAX.chrl4.102172621-102172686, MAX. chrl4.105512122-105512239, MAX.chrl5.95128144-95128248, MAX.chrl6.11327016-11327312, MAX.chr3.187676577-187676668, MAX.chr4.174430676-174430847, MAX. chr8.145900783-145900914, MAX.chr8.80804237-80804301, N4BP3, NCOR2, NFATC1A, NFATC1B, NKX2-6, NR2F6, OSM, PALLD C, PIK3CD, PRKAR1B, RAD52, STX16_A, SUCLG2, TNFRSF1B, TNFRSF4, ZDHHC18, andZNF671_A; 2) amplifying the treated genomic DNA using a set of primers for the selected one or more genes; and 3) determining the methylation level of the one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with a reagent that modifies DNA in a methylation-specific manner (e.g., wherein the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein the one or more genes is selected from one of the following groups: (i) DIDO1A, NDRG4, MAX.chrl4.103021656-103021718, MMP23B, EMX2OS, SEPT9 B, NBPF8, EEF1A2, AIM1A, BMP4 B, MAX.chr8.145103829-145103992, OBSCN, PYCARD, GDF6, MDFI B, MIAT A, SCL8A3, ZNF323_A, SQSTM1, AFF3, Clorf70, GDF7 A, JSRP1A, LRRC8D A, FEV, and MAX.chr8.145104263-145104422; 2026204791   22 Jun 2026 (ii) ZNF323_A, MAX.chr7.104624356-104624730, NDRG2, DIDO1A, MDFIB, MAX.chrl4.103021656-103021718, MMP23B, SEPT9B, and STX16 A; (iii) SFMBT2B, SQSTM1, ZNF323_A, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN_A, DIDO 1 A, MDFI B, GDF7A, MAX.chrl0.22624479-22624553, JSRP1A, MAX.chrl4.103021656-103021718, EMX2OS, LRRC34, NBPF8, SEPT9_B, EEF1A2, LRRC41C, VILL, and MPZ_A; (iv)   MAX. chr7:1046243 86-104624529, EMX2OS, DIDO1_B, and OBSCN B; and (v) SFMBT2 B, SQSTM1, ZNF323 A, ZNF506, ZNF90, CLDN7, LRRC41B, MAX.chr7.104624356-104624730, NDRG2, CYP11A1, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, MAX.chrlO.130339363-130339534, MDFI B, DLL4, GDF7 A, MIAT A, PYCARD, BMP4 B, JSRP1_A, MAX.chrl4.103021656-103021718, EMX2, MMP23B, EMX2OS, MAX.chrl7.73073716-73073814, NBPF8, SEPT9_B, LOC440925_A, STX16 A, ITPKA, EEF1A2, FEV, LRRC41C, andNFIC; 2)     amplifying the treated genomic DNA using a set of primers for the selected one or more genes; and 3) determining the methylation level of the one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with a reagent that modifies DNA in a methylation-specific manner (e.g., wherein the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein the one or more genes is selected from one of the following groups: (i) EMX2OS, DIDO 1 A, SBNO2, AMIGO3 A, PCOLCE, CLDN7, CYTH2, OBSCN A, AHSA2, DLL4, EMX2, 2026204791   22 Jun 2026 MAX.chrl4.74100620-74100870, LRRC4, PPP2R5CA, SQSTM1, MAX. chrl7.73073716-73073814, CYP11A1, ACOXL_A, and AIM1_B; (ii) EMX2OS, and LRRC34; (iii) ZNF506, ZNF90, MAX. chr8.145103 829-145103 992, LRRC8D A, OBSCNA, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and VILL; (iv) TRH, MAX.chr7:104624386-104624529, EMX2OS, DIDO I B, and ST3GAL2B; and (v) SFMBT2B, SMTN, ZNF506, ZNF90, CLDN7, LRRC41B, CYP11A1, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2B, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, DLL4, MAX.chrl0.22624479-22624553, PYCARD, BMP4 B, JSRP1A, MAX. chrl4.103021656-103021718, MIATB, EMX2OS, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, and VILL; 2) amplifying the treated genomic DNA using a set of primers for the selected one or more genes; and 3)     determining the methylation level of the one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with a reagent that modifies DNA in a methylation-specific manner (e.g., wherein the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein the one or more genes is selected from one of the following groups: (i) EMX2OS, KANKI, Clorf70_B, AMIGO3 A, DIDO 1 A, LRRC41C, NFIC, FKBP11A, C17orfl07_A, SMTN, LRRC41B, LRRC8D A, OBSCN A, MAX.chr7.104624356-104624730, MIAT B; (ii) MAX.chr7.104624356-104624730, EMX2OS, and LRRC41C; 2026204791   22 Jun 2026 (iii)   MAX.chr8.145103829-145103992, CYTH2, LRRC8DA, OBSCNA, DIDO 1 A, EMX2OS, LRRC41 C, and VILL; (iv) EMX2OS, and LRRC41D; and (v) SFMBT2B, SMTN, SQSTM1, ZNF90, CLDN7, LRRC41B, MAX.chr7.104624356-104624730, CYP11A1, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, GDGF6, DLL4, PYCARD, BMP4 B, JSRP1A, MIAT B, KANKI, EMX2OS, NBPF8, LOC440925_A, ITPKA, EEF1A2, FEV, LRRC41C, NFIC, VILL, MPZA; 2)     amplifying the treated genomic DNA using a set of primers for the selected one or more genes; and 3) determining the methylation level of the one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with a reagent that modifies DNA in a methylation-specific manner (e.g., wherein the reagent is a bisulfite reagent, a methylation-sensitive restriction enzyme, or a methylation-dependent restriction enzyme), wherein the one or more genes is selected from one of the following groups: (i)    MAX.chrlO.130339363-130339534, SFMBT2_C, CYTH2, SLC6A3, VILL, EMX2OS, MAX.chrl0.22624479-22624553, GDF6, ZNF90, ZNF506, JSRP1A, c5orf52, SFMBT2B, NBPF8, RHBDL1A, DIDO 1 A, KANKI, and GATA2 B; (ii)   MAX.chr8.145103829-145103992, CYTH2, DIDO1_A, MAX.chrlO.22624479-22624553, JSRP1_A, SBNO2, NBPF8, and VILL; (iii) SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, MAX.chr8.145104263-145104422, OBSCN_A, MAX.chrlO.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ_A; (iv) SFMBT2B, SMTN, SQSTM1, ZNF506, ZNF90, CLDN7, LRRC41B, FKBP11A, MAX.chr8.145103829-145103992, AHSA2, 2026204791   22 Jun 2026 CYTH2, GATA2B, LRRC8DA, MAX.chr8.145104263-145104422, DIDO1A, GDF6, MAX.chrl0.130339363-130339534, DLL4, MAX.chrl0.22624479-22624553, MIAT A, PYCARD, BMP4 B, JSRP1A, MAX. chrl4.103021656-103021718, MIATB, KANKI, SBNO2, c5orf52, EMX2O6, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, VILL, and MPZ A; (v) TSPYL5, TRH, JAM3, FAM19A5, PTGDR, SFMBT2 E, JSRP1B, andARL5C; (vi) TSPYL5, MPZ_B, TRH, CNTN4, FAM19A5, GLT1D1, RYR2 F, PTGDR, EMX2OS, MAX.chrl0:22624470-22624553, SPDYA B, SFMBT2 E, and JSRP1_B; and (vii) TSPYL5, MPZ_B, TRH, and PTGDR. 2) amplifying the treated genomic DNA using a set of primers for the selected one or more genes; and 3) determining the methylation level of the one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring an amount of at least one methylated marker gene in DNA from the sample, wherein the one or more genes is selected from one of the following groups: (i) AFF3, AIM1A, AMIGO3 A, BMP4 B, C17orfl07_A, Clorf70_B, C5orf52, CLDN7, DIDO 1 A, EEF1A2, EMX2OS, FEV, FKBP11A, GDF6, GDF7 A, JSRP1A, KCTD15 A, KLHL21, LRRC8D A, NBPF8, MAX.chrl0.130339363-130339534, MAX.chrl0.22624479-22624553, MAX.chrl4.103021656-103021718, MAX. chr8.145103829-145103992, MAX. chr8.145104263-145104422, MDFI B, MIAT A, MMP23B, NDRG2, OBSCN A, PCOLCE, PYCARD, SEPT9 B, SLC6A3 A, SLC8A3 B, SQSTM1, VILL, ZNF302, ZNF323 A, ZNF506, and ZNF90; (ii) EMX2OS, CYTH2, C17orfl07_A, DIDO1A, GDF6, NBPF8, MAX.chr!4.103021656-103021718, JSRP1_A, GATA2 B, and SFMBT2B; 2026204791   22 Jun 2026 (iii) SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCNA, DIDO 1 A, MAX.chrl0.22624479-22624553, JSRP1A, EMX2OS, NBPF8, and MPZ_A; (iv) EMX2OS, CYTH2, NBPF8, MAX.chrl0.22624479-22624553; and (v) ANKRD35, ARL5C, ARRB1, BCL2L11A, BCL2L11B, BCL2L11C, BZRAP1, C16orf54, C17orfl01, C6orfl32, CACNA2D4, DEDD2, EPS15L1, FAIM2, FAM125B, FAM189B, FAM78A, FOXP4, GYPCA, GYPC_B, IFFO1A, IFFO1B, ITPKA, KLF16, LIMD2, LOC389333, LOC440925_A, LOC646278, LYL1, LYPLAL1, MAX.chrl 1.32355226-32355251, MAX.chrl4.102172621-102172686, MAX.chrl4.105512122-105512239, MAX.chrl5.95128144-95128248, MAX.chrl6.11327016-11327312, MAX.chr3.187676577-187676668, MAX. chr4.174430676-174430847, MAX. chr8.145900783-145900914, MAX.chr8.80804237-80804301, N4BP3, NCOR2, NFATC1A, NFATC1B, NKX2-6, NR2F6, OSM, PALLD C, PIK3CD, PRKAR1B, RAD52, STX16_A, SUCLG2, TNFRSF1B, TNFRSF4, ZDHHC18, andZNF671_A; 2)    measuring the amount of at least one reference marker in the DNA; and 3)     calculating a value for the amount of the at least one methylated marker gene measured in the DNA as a percentage of the amount of the reference marker gene measured in the DNA, wherein the value indicates the amount of the at least one methylated marker DNA measured in the sample. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring an amount of at least one methylated marker gene in DNA from the sample, wherein the one or more genes is selected from one of the following groups: (i)    DIDO1A, NDRG4, MAX.chrl4.103021656-103021718, MMP23B, EMX2OS, SEPT9 B, NBPF8, EEF1A2, AIM1A, BMP4 B, MAX.chr8.145103829-145103992, OBSCN, PYCARD, GDF6, MDFI B, MIAT A, SCL8A3, ZNF323_A, SQSTM1, AFF3, Clorf70, GDF7 A, JSRP1A, LRRC8D A, FEV, and MAX.chr8.145104263-145104422; 2026204791   22 Jun 2026 (ii)   ZNF323_A, MAX.chr7.104624356-104624730, NDRG2, DIDO1A, MDFIB, MAX.chrl4.103021656-103021718, MMP23B, SEPT9B, and STX16 A; (iii) SFMBT2B, SQSTM1, ZNF323_A, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN_A, DIDO 1 A, MDFI B, GDF7A, MAX.chrl0.22624479-22624553, JSRP1A, MAX.chrl4.103021656-103021718, EMX2OS, LRRC34, NBPF8, SEPT9_B, EEF1A2, LRRC41C, VILL, and MPZ_A; (iv)   MAX. chr7:1046243 86-104624529, EMX2OS, DIDO1_B, and OBSCN B; and (v) SFMBT2 B, SQSTM1, ZNF323 A, ZNF506, ZNF90, CLDN7, LRRC41B, MAX.chr7.104624356-104624730, NDRG2, CYP11A1, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, MAX.chrlO.130339363-130339534, MDFI B, DLL4, GDF7 A, MIAT A, PYCARD, BMP4 B, JSRP1_A, MAX.chrl4.103021656-103021718, EMX2, MMP23B, EMX2OS, MAX.chrl7.73073716-73073814, NBPF8, SEPT9_B, LOC440925_A, STX16 A, ITPKA, EEF1A2, FEV, LRRC41C, andNFIC; 2)    measuring the amount of at least one reference marker in the DNA; and 3)     calculating a value for the amount of the at least one methylated marker gene measured in the DNA as a percentage of the amount of the reference marker gene measured in the DNA, wherein the value indicates the amount of the at least one methylated marker DNA measured in the sample. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring an amount of at least one methylated marker gene in DNA from the sample, wherein the one or more genes is selected from one of the following groups: (i) EMX2OS, DIDO 1 A, SBNO2, AMIGO3 A, PCOLCE, CLDN7, CYTH2, OBSCN A, AHSA2, DLL4, EMX2, MAX.chrl4.74100620-74100870, LRRC4, PPP2R5C_A, SQSTM1, MAX. chrl7.73073716-73073814, CYP11A1, ACOXL_A, and AIM1_B; (ii) EMX2OS, and LRRC34; 2026204791   22 Jun 2026 (iii) ZNF506, ZNF90, MAX. chr8.145103 829-145103 992, LRRC8DA, OBSCNA, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and VILL; (iv) TRH, MAX.chr7:104624386-104624529, EMX2OS, DIDO I B, and ST3GAL2B; and (v) SFMBT2B, SMTN, ZNF506, ZNF90, CLDN7, LRRC41B, CYP11A1, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2B, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, DLL4, MAX.chrl0.22624479-22624553, PYCARD, BMP4 B, JSRP1A, MAX.chrl4.103021656-103021718, MIAT B, EMX2OS, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, and VILL; 2)    measuring the amount of at least one reference marker in the DNA; and 3)     calculating a value for the amount of the at least one methylated marker gene measured in the DNA as a percentage of the amount of the reference marker gene measured in the DNA, wherein the value indicates the amount of the at least one methylated marker DNA measured in the sample. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring an amount of at least one methylated marker gene in DNA from the sample, wherein the one or more genes is selected from one of the following groups: (i) EMX2OS, KANKI, Clorf70_B, AMIGO3 A, DIDO 1 A, LRRC41C, NFIC, FKBP11A, C17orfl07_A, SMTN, LRRC41B, LRRC8D A, OBSCN A, MAX.chr7.104624356-104624730, MIAT B; (ii) MAX.chr7.104624356-104624730, EMX2OS, and LRRC41C; (iii) MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO 1 A, EMX2OS, LRRC41C, and VILL; (iv) EMX2OS, and LRRC41D; and (v) SFMBT2 B, SMTN, SQSTM1, ZNF90, CLDN7, LRRC41B, MAX.chr7.104624356-104624730, CYP11A1, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, GDGF6, DLL4, PYCARD, 2026204791   22 Jun 2026 BMP4 B, JSRP1A, MIATB, KANKI, EMX2OS, NBPF8, LOC440925_A, ITPKA, EEF1A2, FEV, LRRC41 C, NFIC, VILL, MPZ A; 2)    measuring the amount of at least one reference marker in the DNA; and 3)     calculating a value for the amount of the at least one methylated marker gene measured in the DNA as a percentage of the amount of the reference marker gene measured in the DNA, wherein the value indicates the amount of the at least one methylated marker DNA measured in the sample. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring an amount of at least one methylated marker gene in DNA from the sample, wherein the one or more genes is selected from one of the following groups: (i) MAX.chrlO.130339363-130339534, SFMBT2_C, CYTH2, SLC6A3, VILL, EMX2OS, MAX.chrl0.22624479-22624553, GDF6, ZNF90, ZNF506, JSRP1A, c5orf52, SFMBT2B, NBPF8, RHBDL1A, DIDO 1 A, KANKI, and GATA2 B; (ii) MAX.chr8.145103829-145103992, CYTH2, DIDO1_A, MAX.chrlO.22624479-22624553, JSRP1_A, SBNO2, NBPF8, and VILL; (iii) SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, MAX.chr8.145104263-145104422, OBSCN_A, MAX.chrlO.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ_A; (iv) SFMBT2 B, SMTN, SQSTM1, ZNF506, ZNF90, CLDN7, LRRC41B, FKBP11A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2 B, LRRC8D A, MAX.chr8.145104263-145104422, DIDO1A, GDF6, MAX.chrlO. 130339363-130339534, DLL4, MAX.chrl0.22624479-22624553, MIAT A, PYCARD, BMP4 B, JSRP1A, MAX.chr!4.103021656-103021718, MIAT B, KANKI, SBNO2, c5orf52, EMX2O6, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, VILL, and MPZ A; (v) TSPYL5, TRH, JAM3, FAM19A5, PTGDR, SFMBT2 E, JSRP1B, andARL5C; 2026204791   22 Jun 2026 (vi) TSPYL5, MPZ B, TRH, CNTN4, FAM19A5, GLT1D1, RYR2 F, PTGDR, EMX2OS, MAX.chrl0:22624470-22624553, SPDYA B, SFMBT2E, and JSRP1_B; and (vii) TSPYL5, MPZ B, TRH, and PTGDR; 2)    measuring the amount of at least one reference marker in the DNA; and 3)     calculating a value for the amount of the at least one methylated marker gene measured in the DNA as a percentage of the amount of the reference marker gene measured in the DNA, wherein the value indicates the amount of the at least one methylated marker DNA measured in the sample. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level of a CpG site for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with bisulfite a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent); 2) amplifying the modified genomic DNA using a set of primers for the selected one or more genes; and 3) determining the methylation level of the CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genomic sequencing PCR; wherein the one or more genes is selected from one of the following groups: (i) AFF3, AIM1A, AMIGO3 A, BMP4 B, C17orfl07_A, Clorf70_B, C5orf52, CLDN7, DIDO 1 A, EEF1A2, EMX2OS, FEV, FKBP11A, GDF6, GDF7 A, JSRP1A, KCTD15 A, KLHL21, LRRC8DA, NBPF8, MAX.chrl0.130339363-130339534, MAX.chrl0.22624479-22624553, MAX.chrl4.103021656-103021718, MAX. chr8.145103829-145103992, MAX. chr8.145104263-145104422, MDFI B, MIAT A, MMP23B, NDRG2, OBSCN A, PCOLCE, PYCARD, SEPT9 B, SLC6A3 A, SLC8A3 B, SQSTM1, VILL, ZNF302, ZNF323 A, ZNF506, and ZNF90; 2026204791   22 Jun 2026 (ii) EMX2OS, CYTH2, C17orfl07_A, DIDO1A, GDF6, NBPF8, MAX. chrl4.103021656-103021718, JSRP1_A, GATA2B, and SFMBT2B; (iii) SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO 1 A, MAX.chrl0.22624479-22624553, JSRP1A, EMX2OS, NBPF8, and MPZ_A; (iv) EMX2OS, CYTH2, NBPF8, MAX.chrl0.22624479-22624553; and (v) ANKRD35, ARL5C, ARRB1, BCL2L11A, BCL2L11B, BCL2L11_C, BZRAP1, C16orf54, C17orfl01, C6orfl32, CACNA2D4, DEDD2, EPS15L1, FAIM2, FAM125B, FAM189B, FAM78A, FOXP4, GYPCA, GYPC_B, IFFO1A, IFFO1B, ITPKA, KLF16, LIMD2, LOC389333, LOC440925_A, LOC646278, LYL1, LYPLAL1, MAX.chrl 1.32355226-32355251, MAX.chrl4.102172621-102172686, MAX.chrl4.105512122-105512239, MAX.chrl5.95128144-95128248, MAX.chrl6.11327016-11327312, MAX.chr3.187676577-187676668, MAX. chr4.174430676-174430847, MAX. chr8.145900783-145900914, MAX.chr8.80804237-80804301, N4BP3, NCOR2, NFATC1A, NFATC1B, NKX2-6, NR2F6, OSM, PALLD C, PIK3CD, PRKAR1B, RAD52, STX16_A, SUCLG2, TNFRSF1B, TNFRSF4, ZDHHC18, andZNF671_A. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level of a CpG site for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with bisulfite a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent); 2) amplifying the modified genomic DNA using a set of primers for the selected one or more genes; and 3) determining the methylation level of the CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genomic sequencing PCR; wherein the one or more genes is selected from one of the following groups: 2026204791   22 Jun 2026 (i) DIDO1A, NDRG4, MAX.chrl4.103021656-103021718, MMP23B, EMX2OS, SEPT9B, NBPF8, EEF1A2, AIM1A, BMP4 B, MAX.chr8.145103829-145103992, OBSCN, PYCARD, GDF6, MDFI B, MIAT A, SCL8A3, ZNF323_A, SQSTM1, AFF3, Clorf70, GDF7A, JSRP1A, LRRC8D A, FEV, and MAX.chr8.145104263-145104422; (ii) ZNF323_A, MAX. chr7.104624356-104624730, NDRG2, DIDO1A, MDFI B, MAX.chrl4.103021656-103021718, MMP23B, SEPT9 B, and STX16 A; (iii) SFMBT2 B, SQSTM1, ZNF323 A, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN_A, DIDO 1 A, MDFI B, GDF7 A, MAX.chrl0.22624479-22624553, JSRP1A, MAX.chrl4.103021656-103021718, EMX2OS, LRRC34, NBPF8, SEPT9_B, EEF1A2, LRRC41C, VILL, and MPZ_A; (iv) MAX. chr7:1046243 86-104624529, EMX2OS, DIDO1_B, and OBSCN B; and (v) SFMBT2 B, SQSTM1, ZNF323 A, ZNF506, ZNF90, CLDN7, LRRC41B, MAX.chr7.104624356-104624730, NDRG2, CYP11A1, MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, MAX.chrlO.130339363-130339534, MDFI B, DLL4, GDF7 A, MIAT A, PYCARD, BMP4 B, JSRP1_A, MAX.chrl4.103021656-103021718, EMX2, MMP23B, EMX2OS, MAX.chrl7.73073716-73073814, NBPF8, SEPT9_B, LOC440925_A, STX16 A, ITPKA, EEF1A2, FEV, LRRC41C, andNFIC. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level of a CpG site for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with bisulfite a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent); 2) amplifying the modified genomic DNA using a set of primers for the selected one or more genes; and 2026204791   22 Jun 2026 3) determining the methylation level of the CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genomic sequencing PCR; wherein the one or more genes is selected from one of the following groups: (i) EMX2OS, DIDO 1 A, SBNO2, AMIGO3A, PCOLCE, CLDN7, CYTH2, OBSCNA, AHSA2, DLL4, EMX2, MAX.chrl4.74100620-74100870, LRRC4, PPP2R5C_A, SQSTM1, MAX. chr!7.73073716-73073814, CYP11A1, ACOXL_A, and AIM1_B; (ii) EMX2OS, and LRRC34; (iii)   ZNF506, ZNF90, MAX. chr8.145103 829-145103 992, LRRC8D A, OBSCN A, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and VILL; (iv) TRH, MAX.chr7:104624386-104624529, EMX2OS, DIDO I B, and ST3GAL2 B; and (v) SFMBT2 B, SMTN, ZNF506, ZNF90, CLDN7, LRRC41B, CYP11A1, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2B, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, DLL4, MAX.chrlO.22624479-22624553, PYCARD, BMP4 B, JSRP1A, MAX.chrl4.103021656-103021718, MIAT B, EMX2OS, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, and VILL. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level of a CpG site for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with bisulfite a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent); 2) amplifying the modified genomic DNA using a set of primers for the selected one or more genes; and 3) determining the methylation level of the CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genomic sequencing PCR; 2026204791   22 Jun 2026 wherein the one or more genes is selected from one of the following groups: (i) EMX2OS, KANKI, Clorf70_B, AMIGO3A, DIDO 1 A, LRRC41C, NFIC, FKBP11A, C17orfl07_A, SMTN, LRRC41B, LRRC8DA, OBSCNA, MAX.chr7.104624356-104624730, MIATB; (ii) MAX.chr7.104624356-104624730, EMX2OS, and LRRC41C; (iii) MAX.chr8.145103829-145103992, CYTH2, LRRC8D A, OBSCN A, DIDO 1 A, EMX2OS, LRRC41C, and VILL; (iv) EMX2OS, and LRRC41D; and (v) SFMBT2B, SMTN, SQSTM1, ZNF90, CLDN7, LRRC41B, MAX.chr7.104624356-104624730, CYP11A1, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, LRRC8D A, MAX.chr8.145104263-145104422, OBSCN_A, GDGF6, DLL4, PYCARD, BMP4 B, JSRP1A, MIAT B, KANKI, EMX2OS, NBPF8, LOC440925_A, ITPKA, EEF1A2, FEV, LRRC41C, NFIC, VILL, MPZ A. In some embodiments of the technology, methods are provided that comprise the following steps: 1) measuring a methylation level of a CpG site for one or more genes in a biological sample of a human individual through treating genomic DNA in the biological sample with bisulfite a reagent capable of modifying DNA in a methylation-specific manner (e.g., a methylation-sensitive restriction enzyme, a methylation-dependent restriction enzyme, and a bisulfite reagent); 2) amplifying the modified genomic DNA using a set of primers for the selected one or more genes; and 3) determining the methylation level of the CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genomic sequencing PCR; wherein the one or more genes is selected from one of the following groups: (i) MAX.chrlO.130339363-130339534, SFMBT2_C, CYTH2, SLC6A3, VILL, EMX2OS, MAX.chrl0.22624479-22624553, GDF6, ZNF90, ZNF506, JSRP1A, c5orf52, SFMBT2B, NBPF8, RHBDL1A, DIDO 1 A, KANKI, and GATA2 B; 2026204791   22 Jun 2026 (ii) MAX.chr8.145103829-145103992, CYTH2, DIDO1_A, MAX.chrl0.22624479-22624553, JSRP1_A, SBNO2, NBPF8, and VILL; (iii) SFMBT2B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, MAX.chr8.145104263-145104422, OBSCN_A, MAX.chrl0.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ_A; (iv) SFMBT2B, SMTN, SQSTM1, ZNF506, ZNF90, CLDN7, LRRC41B, FKBP11A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2B, LRRC8D A, MAX.chr8.145104263-145104422, DIDO1A, GDF6, MAX.chrl0.130339363-130339534, DLL4, MAX.chrl0.22624479-22624553, MIAT A, PYCARD, BMP4 B, JSRP1A, MAX. chrl4.103021656-103021718, MIAT B, KANKI, SBNO2, c5orf52, EMX2O6, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, VILL, and MPZ A; (v) TSPYL5, TRH, JAM3, FAM19A5, PTGDR, SFMBT2 E, JSRP1B, andARL5C; (vi) TSPYL5, MPZ_B, TRH, CNTN4, FAM19A5, GLT1D1, RYR2 F, PTGDR, EMX2OS, MAX.chrl0:22624470-22624553, SPDYA B, SFMBT2 E, and JSRP1_B; and (vii) TSPYL5, MPZ_B, TRH, and PTGDR. Preferably, the sensitivity for such methods is from about 70% to about 100%, or from about 80% to about 90%, or from about 80% to about 85%. Preferably, the specificity is from about 70% to about 100%, or from about 80% to about 90%, or from about 80% to about 85%. Genomic DNA may be isolated by any means, including the use of commercially available kits. Briefly, wherein the DNA of interest is encapsulated in by a cellular membrane the biological sample must be disrupted and lysed by enzymatic, chemical or mechanical means. The DNA solution may then be cleared of proteins and other contaminants, e.g., by digestion with proteinase K. The genomic DNA is then recovered from the solution. This may be carried out by means of a variety of methods including salting out, organic extraction, or binding of the DNA to a solid phase support. The choice of method will be affected by several factors including time, expense, and required quantity of DNA. All clinical sample types comprising neoplastic matter or pre-neoplastic matter are suitable for use in the present 2026204791   22 Jun 2026 method, e.g., cell lines, histological slides, biopsies, paraffin-embedded tissue, body fluids, stool, breast tissue, endometrial tissue, leukocytes, colonic effluent, urine, blood plasma, blood serum, whole blood, isolated blood cells, cells isolated from the blood, and combinations thereof. The technology is not limited in the methods used to prepare the samples and provide a nucleic acid for testing. For example, in some embodiments, a DNA is isolated from a stool sample or from blood or from a plasma sample using direct gene capture, e.g., as detailed in U.S. Pat. Appl. Ser. No. 61 / 485386 or by a related method. The genomic DNA sample is then treated with at least one reagent, or series of reagents, that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker comprising a DMR (e.g., DMR 1-499 e.g., as provided by Tables 1, 8 and 21). In some embodiments, the reagent converts cytosine bases which are unmethylated at the 5'-position to uracil, thymine, or another base which is dissimilar to cytosine in terms of hybridization behavior. However in some embodiments, the reagent may be a methylation sensitive restriction enzyme. In some embodiments, the genomic DNA sample is treated in such a manner that cytosine bases that are unmethylated at the 5' position are converted to uracil, thymine, or another base that is dissimilar to cytosine in terms of hybridization behavior. In some embodiments, this treatment is carried out with bisulfite (hydrogen sulfite, disulfite) followed by alkaline hydrolysis. The treated nucleic acid is then analyzed to determine the methylation state of the target gene sequences (at least one gene, genomic sequence, or nucleotide from a marker comprising a DMR, e.g., at least one DMR chosen from DMR 1-499, e.g., as provided in Tables 1, 8 and 21). The method of analysis may be selected from those known in the art, including those listed herein, e.g., QuARTS and MSP as described herein. Aberrant methylation, more specifically hypermethylation of a marker comprising a DMR (e.g., DMR 1-499, e.g., as provided by Tables 1, 8 and 21) is associated with EC and / or a type of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC). The technology relates to the analysis of any sample associated with an EC. For example, in some embodiments the sample comprises a tissue and / or biological fluid obtained from a patient. In some embodiments, the sample comprises a secretion. In some embodiments, the sample comprises blood, serum, plasma, gastric secretions, pancreatic 2026204791   22 Jun 2026 juice, a gastrointestinal biopsy sample, microdissected cells from a breast biopsy, and / or cells recovered from stool. In some embodiments, the sample comprises endometrial tissue. In some embodiments, the subject is human. The sample may include cells, secretions, or tissues from the endometrium, breast, liver, bile ducts, pancreas, stomach, colon, rectum, esophagus, small intestine, appendix, duodenum, polyps, gall bladder, anus, and / or peritoneum. In some embodiments, the sample comprises cellular fluid, ascites, urine, feces, pancreatic fluid, fluid obtained during endoscopy, blood, mucus, or saliva. In some embodiments, the sample is a stool sample. In some embodiments, the sample is an endometrial tissue sample. Such samples can be obtained by any number of means known in the art, such as will be apparent to the skilled person. For instance, urine and fecal samples are easily attainable, while blood, ascites, serum, or pancreatic fluid samples can be obtained parenterally by using a needle and syringe, for instance. Cell free or substantially cell free samples can be obtained by subjecting the sample to various techniques known to those of skill in the art which include, but are not limited to, centrifugation and filtration. Although it is generally preferred that no invasive techniques are used to obtain the sample, it still may be preferable to obtain samples such as tissue homogenates, tissue sections, and biopsy specimens In some embodiments, the technology relates to a method for treating a patient (e.g., a patient with EC, with early stage EC, or who may develop EC) (e.g., a patient with one or more clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC), the method comprising determining the methylation state of one or more DMR as provided herein and administering a treatment to the patient based on the results of determining the methylation state. The treatment may be administration of a pharmaceutical compound, a vaccine, performing a surgery, imaging the patient, performing another test. Preferably, said use is in a method of clinical screening, a method of prognosis assessment, a method of monitoring the results of therapy, a method to identify patients most likely to respond to a particular therapeutic treatment, a method of imaging a patient or subject, and a method for drug screening and development. In some embodiments of the technology, a method for diagnosing an EC in a subject is provided. The terms “diagnosing” and “diagnosis” as used herein refer to methods by which the skilled artisan can estimate and even determine whether or not a subject is suffering from a given disease or condition or may develop a given disease or condition in the future. The skilled artisan often makes a diagnosis on the basis of one or more diagnostic 2026204791   22 Jun 2026 indicators, such as for example a biomarker (e.g., a DMR as disclosed herein), the methylation state of which is indicative of the presence, severity, or absence of the condition. Along with diagnosis, clinical cancer prognosis relates to determining the aggressiveness of the cancer and the likelihood of tumor recurrence to plan the most effective therapy. If a more accurate prognosis can be made or even a potential risk for developing the cancer can be assessed, appropriate therapy, and in some instances less severe therapy for the patient can be chosen. Assessment (e.g., determining methylation state) of cancer biomarkers is useful to separate subjects with good prognosis and / or low risk of developing cancer who will need no therapy or limited therapy from those more likely to develop cancer or suffer a recurrence of cancer who might benefit from more intensive treatments. As such, “making a diagnosis” or “diagnosing”, as used herein, is further inclusive of determining a risk of developing cancer or determining a prognosis, which can provide for predicting a clinical outcome (with or without medical treatment), selecting an appropriate treatment (or whether treatment would be effective), or monitoring a current treatment and potentially changing the treatment, based on the measure of the diagnostic biomarkers (e.g., DMR) disclosed herein. Further, in some embodiments of the presently disclosed subject matter, multiple determination of the biomarkers over time can be made to facilitate diagnosis and / or prognosis. A temporal change in the biomarker can be used to predict a clinical outcome, monitor the progression of EC, and / or monitor the efficacy of appropriate therapies directed against the cancer. In such an embodiment for example, one might expect to see a change in the methylation state of one or more biomarkers (e.g., DMR) disclosed herein (and potentially one or more additional biomarker(s), if monitored) in a biological sample over time during the course of an effective therapy. The presently disclosed subject matter further provides in some embodiments a method for determining whether to initiate or continue prophylaxis or treatment of a cancer in a subject. In some embodiments, the method comprises providing a series of biological samples over a time period from the subject; analyzing the series of biological samples to determine a methylation state of at least one biomarker disclosed herein in each of the biological samples; and comparing any measurable change in the methylation states of one or more of the biomarkers in each of the biological samples. Any changes in the methylation states of biomarkers over the time period can be used to predict risk of developing cancer, predict clinical outcome, determine whether to initiate or continue the prophylaxis or therapy of the cancer, and whether a current therapy is effectively treating the cancer. For example, a 2026204791   22 Jun 2026 first time point can be selected prior to initiation of a treatment and a second time point can be selected at some time after initiation of the treatment. Methylation states can be measured in each of the samples taken from different time points and qualitative and / or quantitative differences noted. A change in the methylation states of the biomarker levels from the different samples can be correlated with EC risk, prognosis, determining treatment efficacy, and / or progression of the cancer in the subject. In preferred embodiments, the methods and compositions of the invention are for treatment or diagnosis of disease at an early stage, for example, before symptoms of the disease appear. In some embodiments, the methods and compositions of the invention are for treatment or diagnosis of disease at a clinical stage. As noted, in some embodiments, multiple determinations of one or more diagnostic or prognostic biomarkers can be made, and a temporal change in the marker can be used to determine a diagnosis or prognosis. For example, a diagnostic marker can be determined at an initial time, and again at a second time. In such embodiments, an increase in the marker from the initial time to the second time can be diagnostic of a particular type or severity of cancer, or a given prognosis. Likewise, a decrease in the marker from the initial time to the second time can be indicative of a particular type or severity of cancer, or a given prognosis. Furthermore, the degree of change of one or more markers can be related to the severity of the cancer and future adverse events. The skilled artisan will understand that, while in certain embodiments comparative measurements can be made of the same biomarker at multiple time points, one can also measure a given biomarker at one time point, and a second biomarker at a second time point, and a comparison of these markers can provide diagnostic information. As used herein, the phrase “determining the prognosis” refers to methods by which the skilled artisan can predict the course or outcome of a condition in a subject. The term “prognosis” does not refer to the ability to predict the course or outcome of a condition with 100% accuracy, or even that a given course or outcome is predictably more or less likely to occur based on the methylation state of a biomarker (e.g., a DMR). Instead, the skilled artisan will understand that the term “prognosis” refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition. For example, in individuals not exhibiting the condition (e.g., having a normal methylation state of one or more DMR), the chance of a given outcome (e.g., suffering from an EC) may be very low. 2026204791   22 Jun 2026 In some embodiments, a statistical analysis associates a prognostic indicator with a predisposition to an adverse outcome. For example, in some embodiments, a methylation state different from that in a normal control sample obtained from a patient who does not have a cancer can signal that a subject is more likely to suffer from a cancer than subjects with a level that is more similar to the methylation state in the control sample, as determined by a level of statistical significance. Additionally, a change in methylation state from a baseline (e.g., “normal”) level can be reflective of subject prognosis, and the degree of change in methylation state can be related to the severity of adverse events. Statistical significance is often determined by comparing two or more populations and determining a confidence interval and / or ap value. See, e.g., Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York, 1983, incorporated herein by reference in its entirety. Exemplary confidence intervals of the present subject matter are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% and 99.99%, while exemplary p values are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001. In other embodiments, a threshold degree of change in the methylation state of a prognostic or diagnostic biomarker disclosed herein (e.g., a DMR) can be established, and the degree of change in the methylation state of the biamarker in a biological sample is simply compared to the threshold degree of change in the methylation state. A preferred threshold change in the methylation state for biomarkers provided herein is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 50%, about 75%, about 100%, and about 150%. In yet other embodiments, a “nomogram” can be established, by which a methylation state of a prognostic or diagnostic indicator (biomarker or combination of biomarkers) is directly related to an associated disposition towards a given outcome. The skilled artisan is acquainted with the use of such nomograms to relate two numeric values with the understanding that the uncertainty in this measurement is the same as the uncertainty in the marker concentration because individual sample measurements are referenced, not population averages. In some embodiments, a control sample is analyzed concurrently with the biological sample, such that the results obtained from the biological sample can be compared to the results obtained from the control sample. Additionally, it is contemplated that standard curves can be provided, with which assay results for the biological sample may be compared. Such standard curves present methylation states of a biomarker as a function of assay units, e.g., fluorescent signal intensity, if a fluorescent label is used. Using samples taken from multiple 2026204791   22 Jun 2026 donors, standard curves can be provided for control methylation states of the one or more biomarkers in normal tissue, as well as for “at-risk” levels of the one or more biomarkers in tissue taken from donors with metaplasia or from donors with an EC. In certain embodiments of the method, a subject is identified as having metaplasia upon identifying an aberrant methylation state of one or more DMR provided herein in a biological sample obtained from the subject. In other embodiments of the method, the detection of an aberrant methylation state of one or more of such biomarkers in a biological sample obtained from the subject results in the subject being identified as having cancer. The analysis of markers can be carried out separately or simultaneously with additional markers within one test sample. For example, several markers can be combined into one test for efficient processing of a multiple of samples and for potentially providing greater diagnostic and / or prognostic accuracy. In addition, one skilled in the art would recognize the value of testing multiple samples (for example, at successive time points) from the same subject. Such testing of serial samples can allow the identification of changes in marker methylation states over time. Changes in methylation state, as well as the absence of change in methylation state, can provide useful information about the disease status that includes, but is not limited to, identifying the approximate time from onset of the event, the presence and amount of salvageable tissue, the appropriateness of drug therapies, the effectiveness of various therapies, and identification of the subject's outcome, including risk of future events. The analysis of biomarkers can be carried out in a variety of physical formats. For example, the use of microtiter plates or automation can be used to facilitate the processing of large numbers of test samples. Alternatively, single sample formats could be developed to facilitate immediate treatment and diagnosis in a timely fashion, for example, in ambulatory transport or emergency room settings. In some embodiments, the subject is diagnosed as having an EC if, when compared to a control methylation state, there is a measurable difference in the methylation state of at least one biomarker in the sample. Conversely, when no change in methylation state is identified in the biological sample, the subject can be identified as not having EC, not being at risk for the cancer, or as having a low risk of the cancer. In this regard, subjects having the cancer or risk thereof can be differentiated from subjects having low to substantially no cancer or risk thereof. Those subjects having a risk of developing an EC can be placed on a more intensive and / or regular screening schedule, including endoscopic surveillance. On the other hand, 2026204791   22 Jun 2026 those subjects having low to substantially no risk may avoid being subjected to additional testing for EC (e.g., invasive procedure), until such time as a future screening, for example, a screening conducted in accordance with the present technology, indicates that a risk of EC has appeared in those subjects. As mentioned above, depending on the embodiment of the method of the present technology, detecting a change in methylation state of the one or more biomarkers can be a qualitative determination or it can be a quantitative determination. As such, the step of diagnosing a subject as having, or at risk of developing, an EC indicates that certain threshold measurements are made, e.g., the methylation state of the one or more biomarkers in the biological sample varies from a predetermined control methylation state. In some embodiments of the method, the control methylation state is any detectable methylation state of the biomarker. In other embodiments of the method where a control sample is tested concurrently with the biological sample, the predetermined methylation state is the methylation state in the control sample. In other embodiments of the method, the predetermined methylation state is based upon and / or identified by a standard curve. In other embodiments of the method, the predetermined methylation state is a specifically state or range of state. As such, the predetermined methylation state can be chosen, within acceptable limits that will be apparent to those skilled in the art, based in part on the embodiment of the method being practiced and the desired specificity, etc. Further with respect to diagnostic methods, a preferred subject is a vertebrate subject. A preferred vertebrate is warm-blooded; a preferred warm-blooded vertebrate is a mammal. A preferred mammal is most preferably a human. As used herein, the term “subject’ includes both human and animal subjects. Thus, veterinary therapeutic uses are provided herein. As such, the present technology provides for the diagnosis of mammals such as humans, as well as those mammals of importance due to being endangered, such as Siberian tigers; of economic importance, such as animals raised on farms for consumption by humans; and / or animals of social importance to humans, such as animals kept as pets or in zoos. Examples of such animals include but are not limited to: carnivores such as cats and dogs; swine, including pigs, hogs, and wild boars; ruminants and / or ungulates such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels; and horses. Thus, also provided is the diagnosis and treatment of livestock, including, but not limited to, domesticated swine, ruminants, ungulates, horses (including race horses), and the like. 2026204791   22 Jun 2026 The presently-disclosed subject matter further includes a system for diagnosing a EC and / or a specific form of EC (e.g., clear cell EC, carcinosarcoma EC, endometrioid EC, serous EC) in a subject. The system can be provided, for example, as a commercial kit that can be used to screen for a risk of an EC or diagnose an EC cancer in a subject from whom a biological sample has been collected. An exemplary system provided in accordance with the present technology includes assessing the methylation state of a DMR as provided in Tables 1, 8 and 21. EXAMPLES Example I. This example describes the discovery and validation of novel DNA methylation markers for the detection of endometrial cancer (EC) and histological subtypes of EC (e.g., serous EC, clear cell EC, carcinosarcoma EC, and endometrioid EC) through methylome-wide analysis selection. A proprietary methodology of sample preparation, sequencing, analyses pipelines, and filters were utilized to identify and narrow differentially methylated regions (DMRs) to those which would pinpoint EC and various histological subtypes of EC (e.g., serous EC, clear cell EC, carcinosarcoma EC, and endometrioid EC) and excel in a clinical testing environment. From the tissue to tissue analysis 318 hypermethylated EC DMRs were identified (Table 1). Table 2 shows the area-under-the-curve and fold-change in comparison to EC controls for the markers recited in Table 1. Table 1. Identified methylated regions distinguishing endometrial cancer tissue from normal endometrial tissue. DMR No. Gene Annotation Chromosome No. Region on Chromosome (starting base-ending base) 1 ACCN1 17 31619687-31619729 2 ACOXL A 2 111875367-111875453 3 ADAL A 15 43622287-43622368 4 ADAL B 15 43622411-43622462 5 ADAL C 15 43622604-43622732 6 AES 19 3061334-3061694 7 AFF3 2 100721707-100721817 8 AGBL2 11 47736766-47736965 2026204791   22 Jun 2026 9 AGRN A 1 975957-976051 10 AHSA2 2 61405232-61405286 11 AIM1 A 6 106960032-106960380 12 AIM1 B 6 106960531-106960593 13 AMIGO3 A 3 49756685-49756736 14 AMIGO3 B 3 49757071-49757168 15 ANKAR 2 190539103-190539193 16 ANKRD33B 5 10563557-10563627 17 ANO8 19 17439445-17439539 18 ARHGAP20 A 11 110582609-110582670 19 ARHGAP20 B 11 110583216-110583345 20 ARL10 5 175792690-175792780 21 ARMC4 10 28287932-28287982 22 ATP10A 15 26108587-26108685 23 BCAT1 12 25102116-25102197 24 BCL6 3 187456434-187456528 25 BMP4 A 14 54421048-54421118 26 BMP4 B 14 54421619-54421918 27 C14orf169 14 73957777-73957867 28 C17orf107 A 17 4802544-4802828 29 C18orf18 A 18 5237508-5237617 30 C18orf18 B 18 5237862-5237960 31 C18orf18 C 18 5238088-5238139 32 C1orf103 1 111506798-111506903 33 C1orf177 1 55266904-55266944 34 C1orf70 A 1 1475622-1475650 35 C1orf70 B 1 1475957-1476127 36 C1QL3 10 16563604-16563702 37 C21orf58 21 47743021-47743081 38 C2orf43 2 21022503-21022588 39 C2orf62 2 219232460-219232543 40 C5orf52 5 157098189-157098379 41 C7orf51 7 100091227-100091353 42 C8orf73 A 8 144650834-144650918 43 CABP7 22 30116807-30116866 44 CACNA1A 19 13318767-13318855 45 CCDC102A 16 57571055-57571105 46 CCDC48 3 128720910-128720950 47 CCDC85B 11 65658914-65658969 48 CCND2 A 12 4380216-4380297 49 CCND2 B 12 4384302-4384354 50 CCNI2 5 132082878-132082968 51 CD14 5 140012292-140012386 52 CELSR3 3 48693776-48694065 2026204791   22 Jun 2026 53 CES4A 16 67034701-67034744 54 CHMP2A 19 59066468-59066653 55 CLDN7 17 7164898-7164949 56 CLIP4 2 29338393-29338448 57 CYP11A1 15 74658391-74658452 58 CYP2R1 11 14912680-14912762 59 CYTH2 19 48984043-48984140 60 DAB2IP A 9 124461305-124461390 61 DAB2IP B 9 124461600-124461696 62 DEM1 1 40974518-40974785 63 DIDO1 A 20 61560557-61560728 64 DLEC1 A 3 38080673-38080754 65 DLEC1 B 3 38080864-38081010 66 DLEC1 C 3 38081058-38081100 67 DLL4 15 41218290-41218501 68 DNAJC6 1 65731433-65731660 69 DPP7 9 140008731-140008820 70 DSCAML1 11 117667818-117667979 71 DSEL 18 65184250-65184305 72 DTX1 12 113494626-113494665 73 DTX3L 3 122283010-122283080 74 EDARADD 1 236558654-236558751 75 EEF1A2 20 62119741-62119795 76 EGR2 10 64574899-64574948 77 EME2 16 1821271-1821566 78 EMILIN2 A 18 2906050-2906082 79 EMILIN2 B 18 2906258-2906313 80 EMX2 10 119297161-119297228 81 EMX2OS 10 119294950-119295039 82 EPN3 17 48619601-48619768 83 FAM109B 22 42470299-42470599 84 FAM89A 1 231175193-231175307 85 FER1L4 A 20 34189084-34189184 86 FER1L4 B 20 34189488-34189566 87 FEV 2 219849013-219849064 88 FKBP11 A 12 49318865-49319221 89 FLJ22184 19 7933862-7934065 90 FLJ22536 6 21666442-21666683 91 FLJ42875 1 2985432-2985534 92 FLJ43390 14 62584120-62584204 93 FLOT1 6 30711556-30711726 94 FUT11 10 75532571-75532762 95 GABBR2 A 9 101471226-101471281 96 GABBR2_B 9 101471435-101471481 2026204791   22 Jun 2026 97 GABBR2 C 9 101471498-101471518 98 GALR3 22 38214828-38214926 99 GATA2 A 3 128211202-128211292 100 GATA2 B 3 128216370-128216468 101 GBGT1 9 136039231-136039283 102 GDF6 8 97157670-97157756 103 GDF7 A 2 20866007-20866400 104 GHITM 10 85899387-85899545 105 GNB2 7 100273805-100273883 106 GNE 9 36258402-36258585 107 GPR135 14 59931440-59931647 108 GPX1 A 3 49394997-49395054 109 GPX1 B 3 49395134-49395366 110 GRASP 12 52400510-52400570 111 GSTM4 1 110198575-110198883 112 HLA-A 6 29910301-29910371 113 HNRNPF 10 43892386-43892538 114 HOPX A 4 57521826-57521992 115 HOXB2 17 46621333-46621372 116 HOXC8 12 54403025-54403114 117 HS3ST3B1 A 17 14202739-14202781 118 HS3ST3B1 B 17 14203182-14203258 119 IL12RB2 1 67773620-67773674 120 IL13 5 131992171-131992245 121 ITGA4 A 2 182322199-182322409 122 ITGB2 21 46352018-46352116 123 ITPKB 1 226925140-226925336 124 JSRP1 A 19 2253201-2253345 125 JUN 1 59247951-59248035 126 KANK1 9 706956-707230 127 KBTBD11 A 8 1949493-1949584 128 KCNA3 1 111217656-111217716 129 KCNK17 6 39281347-39281518 130 KCNK9 8 140716494-140716600 131 KCNQ5 6 73331959-73332019 132 KCTD15 A 19 34288324-34288423 133 KCTD15 B 19 34288611-34288741 134 KLHL21 1 6663497-6663683 135 KREMEN1 22 29467629-29467716 136 KRT86 12 52702379-52702559 137 LHFPL2 A 5 77806193-77806291 138 LOC100192379 A 4 122686333-122686376 139 LOG 100507463 6 32811543-32811624 140 LOC157627 A 8 9763927-9763997 2026204791   22 Jun 2026 141 LOG 157627 B 8 9764220-9764309 142 LOC338799 12 122243001-122243268 143 LOC402778 11 1770349-1770441 144 LOC729678 5 180258409-180258505 145 LRRC32 11 76381971-76382070 146 LRRC34 3 169530340-169530527 147 LRRC41 A 1 46767677-46767761 148 LRRC41 B 1 46767939-46768016 149 LRRC41 C 1 46768188-46768283 150 LRRC41 D 1 46768830-46768913 151 LRRC41 E 1 46769340-46769650 152 LRRC8D A 1 90308856-90308955 153 LRRK2 12 40618745-40618814 154 LRRN1 3 3841364-3841692 155 MACROD1 11 63767975-63768042 156 MAST1 19 12978432-12978558 157 MATK 19 3786252-3786339 158 MAX.chrl. 110627072 110627257 1 110627072-110627257 159 MAX.chrl.111098121- 111098213 1 111098121-111098213 160 MAX.chrl.116710856- 116710945 1 116710856-116710945 161 MAX.chrl.148000592- 148000777 1 148000592-148000777 162 NBPF8 1 148247951-148248032 163 MAX.chrl .6151971261519821 1 61519712-61519821 164 MAX.chrl 0.102497246102497372 10 102497246-102497372 165 MAX.chrl 0.130339363130339534 10 130339363-130339534 166 MAX.chrl 0.2254150222541587 10 22541502-22541587 167 MAX.chrl 0.2262447922624553 10 22624479-22624553 168 MAX.chrl 1.123301058123301153 11 123301058-123301153 169 MAX.chrl 1.80405948040647 11 8040594-8040647 170 MAX.chrl 2.125534393125534458 12 125534393-125534458 171 MAX.chr12.133485161-133485240 12 133485161-133485240 172 MAX.chrl 2.133485417133485505 12 133485417-133485505 173 MAX.chrl 2.133485542133485675 12 133485542-133485675 174 MAX.chrl 4.103021656103021718 14 103021656-103021718 175 MAX.chrl 4.103557994103558154 14 103557994-103558154 2026204791   22 Jun 2026 176 MAX.chrl 4.103558061 -103558154 14 103558061-103558154 177 MAX.chrl 4.7410062074100870 14 74100620-74100870 178 MAX.chrl 7.2933535829335628 17 29335358-29335628 179 MAX.chrl 7.4608973846089851 17 46089738-46089851 180 MAX.chrl 7.7307371673073814 17 73073716-73073814 181 MAX.ch r19.31210519-31210593 19 31210519-31210593 182 MAX.chrl 9.3728860737288752 19 37288607-37288752 183 M AX.ch r2.102867766-102867826 2 102867766-102867826 184 M AX.ch r2.127783244-127783311 2 127783244-127783311 185 MAX.ch r2.233283604-233283736 2 233283604-233283736 186 MAX.ch r2.43038072-43038159 2 43038072-43038159 187 M AX. ch r2.96192422-96192520 2 96192422-96192520 188 MAX.chr2.96192422-96192610 2 96192422-96192610 189 MAX.ch r20.37302903-37302984 20 37302903-37302984 190 MAX.chr21.30375011-30375136 21 30375011-30375136 191 MAX.chr21.38936278-38936494 21 38936278-38936494 192 M AX. ch r22.42679801-42679979 22 42679801-42679979 193 MAX.chr3.128336893-128336988 3 128336893-128336988 194 MAX.chr3.18486889-18486958 3 18486889-18486958 195 M AX.ch r3.44038012-44038064 3 44038012-44038064 196 MAX.chr4.186049532-186049660 4 186049532-186049660 197 MAX.chr5.177371520-177371612 5 177371520-177371612 198 M AX.ch r5.42950901-42951088 5 42950901-42951088 199 MAX.ch r5.64398959-64399179 5 64398959-64399179 200 MAX.chr6.130687108-130687268 6 130687108-130687268 201 MAX.chr6.26171901-26172479 6 26171901-26172479 202 MAX.chr6.26172225-26172432 6 26172225-26172432 203 MAX.ch r6.30923280-30923382 6 30923280-30923382 204 MAX.chr7.104624356-104624730 7 104624356-104624730 2026204791   22 Jun 2026 205 MAX.chr8.142216090-142216173 8 142216090-142216173 206 MAX.chr8.143532758-143532822 8 143532758-143532822 207 MAX.chr8.145103829-145103992 8 145103829-145103992 208 MAX.chr8.145104263-145104422 8 145104263-145104422 209 MAZ 16 29818932-29819149 210 MBLAC1 7 99725558-99725690 211 MDFI A 6 41606074-41606165 212 MDFI B 6 41606379-41606439 213 MFSD2B 2 24232924-24233011 214 MIAT A 22 27053316-27053559 215 MIAT B 22 27068733-27069240 216 MIDN 19 1252654-1252814 217 MIR155HG 21 26934273-26934466 218 MMP23B 1 1567450-1567633 219 MRPS21 1 150266158-150266227 220 MRPS33 7 140714767-140714925 221 MYOZ3 5 150036505-150036584 222 N4BP2L1 A 13 33001508-33001672 223 N4BP2L1 B 13 33001696-33001851 224 NCKIPSD 3 48723553-48723614 225 NCRNA00085 19 52207418-52207571 226 NDRG2 14 21493523-21494033 227 NEAT1 A 11 65189991-65190140 228 NEAT1 B 11 65190826-65190987 229 NEK9 14 75593252-75593340 230 NFIC 19 3361080-3361200 231 NR1I2 3 119528931-119529062 232 NTRK3 A 15 88799070-88799125 233 NTRK3 B 15 88799973-88800085 234 OBSCN A 1 228463593-228463692 235 OLFM1 9 137979377-137979461 236 PALLD A 4 169753101-169753185 237 PALLD B 4 169753319-169753406 238 PCOLCE 7 100202395-100202728 239 PDGFRA 4 55092628-55092682 240 PHLDB1 A 11 118481753-118481830 241 PISD 22 32026307-32026516 242 PODN 1 53528224-53528302 243 PPP2R5C A 14 102247689-102247929 244 PPP2R5C B 14 102248127-102248216 245 PTCH2 1 45285985-45286035 246 PTPRN2 7 157361644-157361762 2026204791   22 Jun 2026 247 PXMP4 20 32307913-32308002 248 PYCARD 16 31213623-31213709 249 RAI1 17 17627101-17627256 250 RBM20 10 112432331-112432394 251 RFTN1 3 16554709-16554808 252 RHBDL1 A 16 725291-725617 253 RIMS2 8 104512743-104512831 254 RLTPR 16 67678899-67678952 255 RTN4RL2 11 57244132-57244225 256 SBNO2 19 1131812-1132072 257 SEPT11 4 77869938-77870029 258 SEPT9 A 17 75447455-75447554 259 SEPT9 B 17 75447656-75448049 260 SERPINB9 6 2903415-2903513 261 SFMBT2 A 10 7450743-7450831 262 SFMBT2 B 10 7451000-7451098 263 SFMBT2 C 10 7451771-7451869 264 SFMBT2 D 10 7452346-7452367 265 SIGIRR 11 407086-407183 266 SIX4 14 61188239-61188329 267 SLC12A8 3 124860700-124860798 268 SLC13A5 A 17 6616764-6616852 269 SLC43A3 11 57194548-57194650 270 SLC6A3 5 1445562-1445659 271 SLC8A3 14 70654774-70654899 272 SLCO4C1 5 101632152-101632237 273 SMTN 22 31481122-31481208 274 SNTG2 2 946417-946458 275 SPOCK2 A 10 73847389-73847446 276 SPOCK2 B 10 73847890-73848209 277 SPON1 11 13985007-13985088 278 SQSTM1 5 179243864-179243955 279 ST3GAL2 A 16 70415734-70415777 280 SV2A 1 149889374-149889466 281 TBX1 22 19754292-19754349 282 TCF3 19 1651268-1651408 283 TECR 19 14667597-14667690 284 TEPP 16 58018744-58018831 285 TFR2 7 100230996-100231069 286 THAP4 2 242549705-242549757 287 TICAM2 5 114937802-114937980 288 TMCO1 A 1 165737880-165737973 289 TMCO1 B 1 165738121-165738246 290 TMEM130 7 98467740-98467817 2026204791   22 Jun 2026 291 TMEM163 2 135475828-135475890 292 TMEM63B 6 44119717-44119780 293 TNFRSF10D 8 23021299-23021396 294 TRIM71 A 3 32859463-32859793 295 TSHZ3 A 19 31839967-31840038 296 TSHZ3 B 19 31840244-31840330 297 TSHZ3 C 19 31841427-31841476 298 TSPAN2 1 115632183-115632276 299 TTBK1 6 43242971-43243178 300 TTC14 3 180320089-180320177 301 UST A 6 149068948-149069040 302 VILL 3 38035645-38035743 303 WNT1 12 49373374-49373532 304 WNT7B 22 46366771-46366866 305 ZMIZ1 A 10 81002372-81002568 306 ZMIZ1 B 10 81002818-81003006 307 ZMIZ1 C 10 81002928-81002991 308 ZNF167 3 44596832-44596885 309 ZNF292 6 87861730-87861807 310 ZNF302 19 35168826-35168915 311 ZNF304 19 57862463-57862983 312 ZNF323 A 6 28303870-28304162 313 ZNF354C 5 178487210-178487466 314 ZNF506 19 19932386-19932525 315 ZNF568 A 19 37407197-37407284 316 ZNF586 B 19 58281309-58281368 317 ZNF880 19 52873064-52873107 318 ZNF90 19 20189032-20189134 Table 2. Area-under-the-curve, fold-change, and p-value for EC tissue in comparison to EC controls for the markers recited in Table 1. DMR No. Gene Annotation AUC EC vs. EC control Fold Change EC vs. EC control p value EC vs. EC control 1 ACCN1 0.6618 21.88 0.0005565 2 ACOXL_A 0.8597 50.89 0.007356 3 ADAL_A 0.6656 110 0.006193 4 ADAL_B 0.6627 21.39 0.0005691 5 ADAL_C 0.7773 47.72 0.0001428 6 AES 0.6948 83.12 4.00E-08 7 AFF3 0.9188 31.72 2.95E-09 2026204791   22 Jun 2026 8 AGBL2 0.6667 375.3 3.74E-05 9 AGRN_A 0.863 597.6 1.36E-05 10 AHSA2 0.8978 59.27 0.001032 11 AIM1_A 0.9408 369.2 5.34E-06 12 AIM1_B 0.7828 21.43 0.0003948 13 AMIGO3_A 0.9306 40 0.00008386 14 AMIGO3_B 0.6818 101.4 0.003578 15 AN KAR 0.703 99.07 0.001126 16 ANKRD33B 0.6869 143 3.54E-05 17 ANO8 0.765 5.712 0.001607 18 ARHGAP20_A 0.6516 33.86 0.000001454 19 ARHGAP20_B 0.7344 23.35 0.0004238 20 ARL10 0.8325 255.7 1.05E-07 21 ARMC4 0.7164 17.89 0.005436 22 ATP10A 0.7597 66.21 0.0002969 23 BCAT1 0.8932 47.95 1.49E-07 24 BCL6 0.7222 30.29 0.002576 25 BMP4_A 0.6585 21.31 0.0003743 26 BMP4_B 0.9408 33.04 1.179E-08 27 C14orf169 0.6655 90.03 0.00124 28 C17orf107_A 0.907 93.14 6.96E-12 29 C18orf18_A 0.75 15.49 2.14E-05 30 C18orf18_B 0.7507 66.92 0.00001497 31 C18orf18_C 0.8582 107 0.00003015 32 C1orf103 0.6555 15.58 4.442E-08 33 C1orf177 0.6508 506.2 0.001782 34 C1orf70_A 0.7483 97.16 2.41 E-07 35 C1orf70_B 0.9134 252.1 1.56E-07 36 C1QL3 0.852 43.14 1.45E-07 37 C21orf58 0.7227 23.94 9.62E-05 38 C2orf43 0.677 7.064 0.0004705 39 C2orf62 0.8221 41.27 2.974E-07 40 C5orf52 0.9047 165.5 1.655E-07 41 C7orf51 0.8093 29.63 0.0001685 42 C8orf73_A 0.8768 48.29 7.25E-07 43 CABP7 0.821 490 0.003538 44 CACNA1A 0.745 32.19 5.80E-05 45 CCDC102A 0.7417 13.76 8.518E-10 46 CCDC48 0.6835 20.97 0.003088 47 CCDC85B 0.6688 17.2 0.0002735 48 CCND2_A 0.7801 12.08 7.18E-06 49 CCND2_B 0.6608 9.469 8.33E-06 50 CCNI2 0.6574 16.09 0.0006968 51 CD14 0.6903 458.2 0.003672 2026204791   22 Jun 2026 52 CELSR3 0.8034 33.68 0.00002479 53 CES4A 0.6623 22.78 0.0006918 54 CHMP2A 0.7432 123.8 0.001936 55 CLDN7 0.913 61.86 0.005037 56 CLIP4 0.6758 72.6 0.004004 57 CYP11A1 0.8646 60.77 0.001696 58 CYP2R1 0.6638 61.08 6.43E-05 59 CYTH2 0.8351 10.35 0.00007307 60 DAB2IP_A 0.7647 287.3 0.0005537 61 DAB2IP_B 0.7273 48.72 3.44E-05 62 DEM1 0.7546 307.8 0.003765 63 DIDO1_A 0.9809 238.3 5.6E-12 64 DLEC1_A 0.6568 34.04 7.97E-06 65 DLEC1_B 0.7992 99.03 3.00E-05 66 DLEC1_C 0.6941 551.8 2.79E-05 67 DLL4 0.8963 16.68 0.0001774 68 DNAJC6 0.8065 70.75 7.229E-07 69 DPP7 0.8643 97.69 2.89E-05 70 DSCAML1 0.6913 37.53 1.26E-06 71 DSEL 0.6707 45.39 0.001035 72 DTX1 0.7321 865.9 0.001687 73 DTX3L 0.6583 152.6 4.39E-05 74 EDARADD 0.7337 236.2 0.005977 75 EEF1A2 0.9532 67.76 0.000003221 76 EGR2 0.7083 25.5 0.000008596 77 EME2 0.6861 139.5 0.00005428 78 EMILIN2_A 0.7266 265 8.81 E-05 79 EMILIN2_B 0.6722 102.4 5.74E-07 80 EMX2 0.6606 160.6 6.34E-05 81 EMX2OS 0.9709 235.4 1.486E-07 82 EPN3 0.6991 47.75 0.0005864 83 FAM109B 0.8416 56.4 0.000003558 84 FAM89A 0.7633 119.1 0.005136 85 FER1 L4_A 0.8381 115.3 1.34E-06 86 FER1 L4_B 0.8457 418.6 0.0001132 87 FEV 0.9004 14.43 1.075E-09 88 FKBP11_A 0.9091 721.9 0.001236 89 FLJ22184 0.7844 53.15 8.099E-08 90 FLJ22536 0.7792 49.09 6.41 E-05 91 FLJ42875 0.6562 64.58 0.000001282 92 FLJ43390 0.6647 13.09 0.001351 93 FLOT1 0.7566 34.14 1.308E-08 94 FUT11 0.6861 1144 0.004405 95 GABBR2_A 0.7711 58.41 0.00001818 2026204791   22 Jun 2026 96 GABBR2_B 0.7276 24.2 0.0001021 97 GABBR2_C 0.6635 30.79 0.0000827 98 GALR3 0.8157 169.5 0.009018 99 GATA2_A 0.7206 6.751 0.0006726 100 GATA2_B 0.888 24.4 9.709E-09 101 GBGT1 0.6765 32.52 0.001294 102 GDF6 0.929 38.04 7.975E-07 103 GDF7_A 0.9133 53.71 2.737E-08 104 GHITM 0.6536 76.28 0.0037 105 GNB2 0.7125 93.16 1.05E-05 106 GNE 0.7 360.7 0.001421 107 GPR135 0.6529 106.8 8.52E-05 108 GPX1_A 0.7786 61.03 1.89E-06 109 GPX1_B 0.7716 42.37 0.0008024 110 GRASP 0.7014 53.88 0.004852 111 GSTM4 0.6722 73.93 0.001751 112 HLA-A 0.6709 123 0.003296 113 HNRNPF 0.8736 533.7 0.007898 114 HOPX 0.6616 33.21 0.000002593 115 HOXB2 0.7143 45.08 0.000256 116 HOXC8 0.6599 21.32 0.000192 117 HS3ST3B1_A 0.7727 7.377 0.0005749 118 HS3ST3B1_B 0.8182 12.17 2.44E-06 119 IL12RB2 0.701 445.5 0.005105 120 IL13 0.8421 85.78 0.009485 121 ITGA4 0.6935 53.03 0.00001091 122 ITGB2 0.7078 9.851 0.000122 123 ITPKB 0.8362 105.4 1.38E-05 124 JSRP1_A 0.907 72.11 5.16E-10 125 JUN 0.6875 59.16 0.000889 126 KANK1 0.8884 135.4 0.000001051 127 KBTBD11_A 0.8143 278.1 0.0001492 128 KCNA3 0.7775 45.7 0.000001416 129 KCNK17 0.7758 21.29 5.81 E-06 130 KCNK9 0.8312 54.29 0.00002916 131 KCNQ5 0.7401 17.31 0.0006638 132 KCTD15_A 0.9266 27.56 0.002706 133 KCTD15_B 0.87 64.21 0.0003926 134 KLHL21 0.9277 115.9 0.0003778 135 KREMEN1 0.7411 49.03 0.0005224 136 KRT86 0.6819 47.5 0.002128 137 LHFPL2_A 0.8115 928.7 0.001375 138 LOC100192379_ A 0.6905 41.04 0.00005452 2026204791   22 Jun 2026 139 LOC100507463 0.6883 24.82 6.97E-05 140 LOC157627_A 0.6999 22.5 0.00001095 141 LOC157627_B 0.7064 25.83 0.001724 142 LOC338799 0.6984 108.8 0.001105 143 LOC402778 0.7145 79.33 0.0002123 144 LOC729678 0.7667 113.4 0.00001356 145 LRRC32 0.7805 10.73 1.389E-07 146 LRRC34 0.7909 155.5 0.00003603 147 LRRC41_A 0.7716 29.68 3.37E-09 148 LRRC41_B 0.7955 237 6.97E-07 149 LRRC41_C 0.789 69.55 3.11E-08 150 LRRC41_D 0.7677 133.1 4.95E-06 151 LRRC41_E 0.7316 479.6 5.30E-05 152 LRRC8D_A 0.9026 27.37 9.12E-05 153 LRRK2 0.7284 53.89 0.005952 154 LRRN1 0.7202 14.85 0.00000822 155 MACROD 1 0.7012 200.4 0.0003994 156 MAST1 0.7232 50.03 0.00318 157 MATK 0.6571 21.21 0.00007402 158 MAX.chrl .110627 072-110627257 0.8366 36.7 1.23E-07 159 MAX.chrl .111098 121-111098213 0.7737 166 0.004094 160 MAX.chrl .116710 856-116710945 0.8219 22.41 0.0000407 161 MAX.chrl .148000 592-148000777 0.7051 77.72 0.00004245 162 NBPF8 0.9697 53.41 1.606E-08 163 MAX.chrl .615197 12-61519821 0.7167 43.36 2.02E-08 164 MAX.chrl 0.10249 7246-102497372 0.7528 18.98 1.14E-05 165 MAX.chrl 0.13033 9363-130339534 0.9709 29.28 0.000001534 166 MAX.chrl 0.22541 502-22541587 0.6588 11.22 0.001261 167 MAX.chrl 0.22624 479-22624553 0.9172 62.87 1.417E-10 168 MAX.chrl 1.12330 1058-123301153 0.6975 28.22 4.74E-06 169 MAX.chrl 1.80405 94-8040647 0.8311 40.67 0.00003799 170 MAX.ch r12.12553 4393-125534458 0.8414 23.5 7.617E-07 171 MAX.chr12.13348 5161-133485240 0.7591 40.03 0.0001313 172 MAX.chr12.13348 5417-133485505 0.7125 57.66 0.0001017 173 MAX.chr12.13348 5542-133485675 0.6853 40.55 0.00001341 174 MAX.chr14.10302 1656-103021718 0.9766 127 5.89E-07 2026204791   22 Jun 2026 175 MAX.chr14.10355 7994-103558154 0.7488 113.7 0.0001156 176 MAX.chr14.10355 8061-103558154 0.6882 49.76 0.0003841 177 MAX.ch r14.74100 620-74100870 0.8808 49.39 0.0005545 178 MAX.chrl 7.29335 358-29335628 0.8279 201.5 0.002438 179 MAX.chrl 7.46089 738-46089851 0.7339 287.6 0.0001518 180 MAX.chrl 7.73073 716-73073814 0.8737 394.1 1.38E-05 181 MAX.chrl 9.31210 519-31210593 0.6504 41.46 0.00398 182 MAX.chrl 9.37288 607-37288752 0.811 88.11 0.000003103 183 MAX.chr2.102867 766-102867826 0.6968 28.75 0.0002521 184 MAX.chr2.127783 244-127783311 0.7289 30.07 0.00003288 185 MAX.chr2.233283 604-233283736 0.875 45.08 0.0001526 186 MAX.chr2.430380 72-43038159 0.6579 40.43 0.005182 187 MAX.chr2.961924 22-96192520 0.667 9.372 0.003974 188 MAX.chr2.961924 22-96192610 0.827 37.66 1.602E-08 189 MAX.ch r20.37302 903-37302984 0.7703 19.49 0.00000328 190 MAX.chr21.30375 011-30375136 0.6519 118.9 0.002865 191 MAX.chr21.38936 278-38936494 0.6512 34.05 0.0002117 192 MAX.ch r22.42679 801-42679979 0.8457 46.57 5.42E-07 193 MAX.chr3.128336 893-128336988 0.8505 207.8 3.97E-05 194 MAX.chr3.184868 89-18486958 0.875 45.35 1.268E-07 195 MAX.chr3.440380 12-44038064 0.7214 26.7 0.00005333 196 MAX.chr4.186049 532-186049660 0.7656 31.4 0.000865 197 MAX.chr5.177371 520-177371612 0.8 33.61 0.002158 198 MAX.chr5.429509 01-42951088 0.8615 28.06 0.00005216 199 MAX.chr5.643989 59-64399179 0.6882 27.48 0.00001 200 M AX.ch r6.130687 108-130687268 0.7631 53.84 0.0002403 201 MAX.chr6.261719 01-26172479 0.7333 14.26 0.0004651 202 M AX.ch r6.261722 25-26172432 0.6614 82.29 0.004157 203 MAX.ch r6.309232 80-30923382 0.8799 35.99 1.61E-05 2026204791   22 Jun 2026 204 M AX.ch r7.104624 356-104624730 0.8723 1101 1.93E-05 205 MAX.chr8.142216 090-142216173 0.7464 100.8 0.0007861 206 MAX.chr8.143532 758-143532822 0.741 5.751 0.0001482 207 MAX.chr8.145103 829-145103992 0.9351 26.27 6.522E-08 208 MAX.chr8.145104 263-145104422 0.9004 51.51 0.0001458 209 MAZ 0.7927 125.9 0.0002086 210 MBLAC1 0.7812 15.75 2.83E-08 211 MDFI_A 0.7424 13.66 0.0003535 212 MDFI_B 0.9286 80.17 3.453E-07 213 MFSD2B 0.8432 53.41 0.0003069 214 MIAT_A 0.9264 68.47 4.28E-07 215 MIAT_B 0.8605 47.34 0.0000377 216 MIDN 0.7849 21.42 0.000005938 217 MIR155HG 0.733 36.79 0.008797 218 MMP23B 0.974 87.98 4.161E-10 219 MRPS21 0.6753 11.41 0.001936 220 MRPS33 0.7068 33.47 0.0004814 221 MYOZ3 0.7949 74.89 0.0002419 222 N4BP2L1_A 0.7495 1311 0.0008957 223 N4BP2L1_B 0.704 1324 0.002896 224 NCKIPSD 0.7162 126 0.0009659 225 NCRNA00085 0.6889 194.7 0.000006047 226 NDRG2 0.9789 83.94 1.082E-07 227 NEAT1_A 0.6898 188.9 0.006251 228 NEAT1_B 0.6891 59.78 0.001232 229 NEK9 0.7791 33.3 0.00255 230 NFIC 0.8041 74.17 3.33E-06 231 NR1I2 0.777 46.68 0.0001105 232 NTRK3_A 0.6654 54.09 0.001975 233 NTRK3_B 0.7374 83.81 0.00007934 234 OBSCN_A 0.9324 436.3 5.79E-08 235 OLFM1 0.6928 53.72 0.0005697 236 PALLD_A 0.6628 70.25 0.0001169 237 PALLD_B 0.673 43.51 0.00002727 238 PCOLCE 0.9136 41.3 0.0009516 239 PDGFRA 0.6522 20.49 0.000009416 240 PHLDB1 0.8075 295.8 0.002509 241 PISD 0.8139 209.3 1.30E-06 242 PGDN 0.697 119.7 6.84E-06 243 PPP2R5C_A 0.8799 168.5 0.00006792 244 PPP2R5C_B 0.7177 315.6 0.003545 2026204791   22 Jun 2026 245 PTCH2 0.8664 27.51 0.0009989 246 PTPRN2 0.6926 19.93 7.26E-05 247 PXMP4 0.788 222.5 0.000004164 248 PYCARD 0.9302 335.8 0.0004632 249 RAI1 0.8198 17.29 6.111E-07 250 RBM20 0.7132 500.5 0.0003599 251 RFTN1 0.7375 23.01 0.0005169 252 RHBDL1_A 0.8988 51.18 0.00001338 253 RIMS2 0.6754 5.933 0.009377 254 RLTPR 0.7173 109.6 1.75E-06 255 RTN4RL2 0.7675 20.03 0.0001403 256 SBNO2 0.817 116 0.0001235 257 SEPT11 0.6992 32.13 0.000554 258 SEPT9_A 0.8474 318.8 0.006383 259 SEPT9_B 0.9704 101.2 0.000001335 260 SERPINB9 0.7617 83.53 0.007034 261 SFMBT2_A 0.803 7.161 0.003198 262 SFMBT2_B 0.8359 21.85 1.30E-06 263 SFMBT2_C 0.8994 23.85 3.37E-07 264 SFMBT2_D 0.6765 32.88 0.0006383 265 SIGIRR 0.6811 47.57 0.004517 266 SIX4 0.8312 19.08 2.91 E-05 267 SLC12A8 0.7944 19.75 0.0003137 268 SLC13A5_A 0.6719 353 0.0006269 269 SLC43A3 0.7455 27.29 5.534E-08 270 SLC6A3_A 0.9318 24.27 1.074E-07 271 SLC8A3_B 0.9239 55.38 1.944E-09 272 SLCO4C1 0.6786 112.2 0.00007596 273 SMTN 0.8052 42.68 5.47E-05 274 SNTG2 0.7862 14.28 0.0004986 275 SPOCK2_A 0.8486 68.87 2.41 E-09 276 SPOCK2_B 0.6956 45.22 1.64E-05 277 SPON1 0.7247 25.58 0.000003926 278 SQSTM1 0.9228 145.7 4.725E-10 279 ST3GAL2_A 0.838 40.37 0.0007039 280 SV2A 0.8137 15.68 7.13E-05 281 TBX1 0.6667 127.2 0.0005607 282 TCF3 0.7783 22.84 7.97E-06 283 TECR 0.6767 203.8 0.001696 284 TEPP 0.8578 33.96 0.00000822 285 TFR2 0.6812 169.5 0.006637 286 THAP4 0.6528 62.88 0.0005633 287 TICAM2 0.6943 35.39 0.001777 288 TMCO1_A 0.7368 27.29 0.00008104 2026204791   22 Jun 2026 289 TMCO1_B 0.6972 141.8 0.002057 290 TMEM130 0.6622 11.02 0.0001735 291 TMEM163 0.6844 12.78 0.0000597 292 TMEM63B 0.8026 20.36 6.39E-06 293 TNFRSF10D 0.6775 15.68 0.002517 294 TRIM71_A 0.74 18.84 1.78E-05 295 TSHZ3_A 0.8161 13.38 3.93E-05 296 TSHZ3_B 0.8312 30.94 0.001939 297 TSHZ3_C 0.661 71.41 0.007574 298 TSPAN2 0.6647 72.46 0.000005262 299 TTBK1 0.79 29.97 3.99E-05 300 TTC14 0.779 481.4 0.006875 301 UST 0.7114 157.8 0.0004509 302 VILL 0.9293 66.67 5.346E-11 303 WNT1 0.8359 33.69 5.21 E-06 304 WNT7B 0.8895 26.27 1.23E-06 305 ZMIZ1_A 0.7273 38.92 0.001658 306 ZMIZ1_B 0.7707 111.2 1.06E-09 307 ZMIZ1_C 0.7664 60.43 0.003325 308 ZNF167 0.722 132.9 0.0002713 309 ZNF292 0.815 531.8 0.008253 310 ZNF302 0.9 46.65 1.08E-05 311 ZNF304 0.8604 142.2 0.0006362 312 ZNF323_A 0.9232 364.4 0.00005473 313 ZNF354C 0.7944 56.82 4.34E-05 314 ZNF506 0.9142 71.02 9.384E-10 315 ZNF568_A 0.7041 73.74 0.0002323 316 ZNF586_B 0.7045 19.73 4.81 E-08 317 ZNF880 0.6615 33.53 1.739E-07 318 ZNF90 0.9149 103.9 0.00003791 Such EC DMRs included EC specific regions, EC subtype specific regions, as well as those regions which targeted a more universal cancer spectrum. The top overall DMRs distinguishing EC and normal endometrial tissue are shown in Table 3. The top overall DMRs distinguishing clear cell EC and normal endometrial tissue are shown in Table 4. The top overall DMRs distinguishing carcinosarcoma EC and normal endometrial tissue are shown in Table 5. The top overall DMRs distinguishing endometrioid EC and normal endometrial tissue are shown in Table 6. The top overall DMRs distinguishing serous EC and normal endometrial tissue are shown in Table 7. The grey-scaled red shading over certain genes in Tables 4, 5, 6, and 7 indicates DMRs which overlap with multiple subtypes. 2026204791   22 Jun 2026 Table 3. Top methylated regions distinguishing endometrial cancer tissue from normal endometrial tissue. Gene Name DMR No. AUC FC EMX2OS 81 0.9309 264 CYTH2 59 0.8856 20.37 C17orf107 A 28 0.8328 64.08 DIDO1 A 63 0.8777 126.3 GDF6 102 0.8772 22.97 NBPF8 162 0.8718 42.83 MAX.ch r14.103021656-103021718 174 0.8679 100.9 JSRP1 A 124 0.8642 38.78 GATA2 B 100 0.8639 19.23 SFMBT2_B 262 0.8431 18.31 Table 4. Top overall DMRs distinguishing clear cell EC and normal endometrial tissue. Gene Name DMR No. AUC FC p-value DIDO1_A 63 0.98 238 5.6E-12 NDRG2 226 0.98 84 1.08E-07 MAX.ch r14.103021656-103021718 174 0.98 127 5.89E-07 MMP23B 218 0.97 88 4.16E-10 EMX2OS 81 0.97 235 1.49E-07 SEPT9_B 259 0.97 101 1.34E-06 NBPF8 162 0.97 53 1.61E-08 EEF1A2 75 0.95 68 3.22E-06 AIM1_A 11 0.94 369 5.34E-06 BMP4_B 26 0.94 33 1.18E-08 MAX.chr8.145103829-145103992 207 0.94 26 6.52E-08 OBSCN_A 234 0.93 436 5.79E-08 PYCARD 248 0.93 336 0.000463 GDF6 102 0.93 38 7.98E-07 MDFI_B 212 0.93 80 3.45E-07 MIAT_A 214 0.93 68 4.28E-07 SLC8A3 271 0.92 55 1.94E-09 ZNF323_A 312 0.92 364 5.47E-05 SQSTM1 278 0.92 146 4.73E-10 AFF3 7 0.92 32 2.95E-09 C1orf70 34 0.91 252 1.56E-07 GDF7_A 103 0.91 54 2.74E-08 JSRP1_A 124 0.91 72 5.16E-10 LRRC8D_A 152 0.90 27 9.12E-05 FEV 87 0.90 14 1.08E-09 MAX.chr8.145104263-145104422 208 0.90 52 0.000146 Table 5. Top overall DMRs distinguishing carcinosarcoma EC and normal endometrial tissue. | Gene Name                         | DMR No. | AUC | FC | p-value 2026204791   22 Jun 2026 EMX2OS 81 0.94 323 4.11E-05 DIDO1_A 63 0.94 143 1.84E-06 SBNO2 256 0.94 129 0.003217 AMIGO3_A 13 0.93 40 8.39E-05 PCOLCE 238 0.91 41 0.000952 CLDN7 55 0.91 62 0.005037 CYTH2 59 0.91 19 4.92E-06 OBSCN_A 234 0.90 159 0.007225 AHSA2 10 0.90 59 0.001032 DLL4 67 0.90 17 0.000177 EMX2 80 0.89 308 0.007177 MAX.ch r14.74100620-74100870 177 0.88 49 0.000555 LRRC34 146 0.88 150 0.002837 PPP2R5C_A 243 0.88 169 6.79E-05 SQSTM1 278 0.88 102 0.005911 M AX.Ch r17.73073716-73073814 180 0.87 586 0.008309 CYP11A1 57 0.86 61 0.001696 ACOXL_A 2 0.86 51 0.007356 AIM1_B 12 0.86 95 0.001099 Table 6. Top overall DMRs distinguishing endometrioid EC and normal endometrial tissue. Gene Name DMR No. AUC FC p-value MAX.ch r10.130339363-130339534 165 0.97 29 1.53E-06 SFMBT2_C 263 0.95 33 6.01 E-08 CYTH2 59 0.94 25 2.18E-08 SLC6A3 270 0.93 24 1.07E-07 VILL 302 0.93 67 5.35E-11 EMX2OS 81 0.92 299 9.02E-06 M AX.Ch r10.22624479-22624553 167 0.92 63 1.42E-10 GDF6 102 0.92 28 7.96E-07 ZNF90 318 0.91 104 3.79E-05 ZNF506 314 0.91 71 9.38E-10 JSRP1_A 124 0.91 70 1.24E-10 C5orf52 40 0.90 166 1.66E-07 SFMBT2_B 262 0.90 36 2.01 E-09 NBPF8 162 0.90 66 2.95E-07 RHBDL1_A 252 0.90 51 1.34E-05 DIDO1_A 63 0.90 90 1.81 E-08 KANK1 126 0.89 135 1.05E-06 GATA2_B 100 0.89 24 9.71 E-09 Table 7. Top overall DMRs distinguishing serous EC and normal endometrial tissue. Gene Name DMR No. AUC FC p-value EMX2OS 81 1.00 277 3.71E-10 KANK1 126 0.94 65 3.2E-07 C1orf70_B 35 0.94 49 5.25E-06 AMIGO3_A 13 0.92 23 2.81E-05 DIDO1_A 63 0.92 127 3.83E-07 LRRC41_C 149 0.91 50 7.06E-08 2026204791   22 Jun 2026 NFIC 230 0.91 46 7.52E-05 FKBP11_A 88 0.91 722 0.001236 C17orf107_A 28 0.91 93 6.96E-12 SMTN 273 0.90 87 2.18E-06 LRRC41_B 148 0.90 93 9.19E-06 LRRC8D_A 152 0.89 59 3.13E-06 OBSCN_A 234 0.87 128 2.48E-05 MAX.Chr7.104624356-104624730 204 0.86 403 0.000153 MIAT_B 215 0.86 47 3.77E-05 A tissue to leukocyte (buffy coat) analysis yielded 129 hypermethylated endometrial tissue DMRs with less than 1% noise in WBCs (Table 8). Table 9 shows the area-under-the-curve, fold-change, and p-value in comparison to EC buffy controls for the markers recited in Table 8. Table 8. Hypermethylated endometrial tissue DMRs with less than 1% noise in WBCs DMR No. Gene Annotation Chromosome No. Region on Chromosome (starting baseending base) 319 ACOXL B 2 111875309-111875359 320 ACTG1 17 79478295-79478468 321 ANKRD35 1 145562791-145562906 499 ARL5C 17 37321564-37321723 322 ARRB1 11 75063559-75063646 323 BCL2L11 A 2 111876440-111876609 324 BCL2L11 B 2 111876958-111877258 325 BCL2L11 C 2 111876624-111876822 326 BEST4 1 45250035-45250159 327 BZRAP1 17 56409702-56409821 328 C14orf169 B 14 73958204-73958363 329 C14orf169 C 14 73958382-73958475 330 C14orf80 14 105954029-105954198 331 C16orf54 16 29757319-29757405 332 C17orf101 17 80358847-80358919 333 C18orf1 18 13641597-13641678 334 C6orf132 6 42072052-42072186 335 C9orf171 9 135285696-135285783 336 CACNA2D4 12 1906260-1906350 337 CCDC61 19 46519515-46519568 338 DEDD2 19 42703469-42703790 339 DGKE 17 54912117-54912243 340 EGFL7 9 139559853-139559951 341 EMB 5 49736982-49737041 342 EOMES 3 27763388-27763413 343 EPS15L1 19 16482437-16482520 2026204791   22 Jun 2026 344 FAIM2 12 50297582-50297690 345 FAM125B 9 129233651-129233705 346 FAM159A 1 53099143-53099216 347 FAM189B 1 155220306-155220399 348 FAM78A 9 134151289-134151464 349 FMNL1 17 43298726-43298774 350 FOXP4 6 41528837-41528899 351 GAL3ST4 7 99769426-99769470 352 GATA2 C 3 128216774-128216891 353 GP1BB 22 19706153-19706187 354 GYPC A 2 127413698-127413901 355 GYPC B 2 127414106-127414189 356 HAAO 2 43019891-43019972 357 HAND2 4 174450783-174450843 358 HDAC7 12 48206687-48206801 359 HOPX B 4 57522083-57522182 360 HOXA7 7 27196352-27196425 361 HOXB4 17 46659392-46659496 362 HRH2 5 175085144-175085212 363 IFFO1 A 12 6664616-6664694 364 IFFO1 B 12 6664873-6665023 119 IL12RB2 1 67773620-67773674 365 IQSEC3 A 12 187211-187344 366 IQSEC3 B 12 187115-187194 367 ITGA4 B 2 182321830-182321917 368 ITPKA 15 41787637-41787780 369 KLF16 19 1856980-1857037 370 LIMD2 17 61778259-61778367 371 LOC100129726 A 2 43452130-43452229 382 LOC100192379 B 4 122686329-122686394 373 LOC339529 1 244080908-244080979 374 LOC389333 5 138728189-138728287 375 LOC440925 A 2 171570158-171570471 376 LOC646278 15 29077327-29077423 377 LTBP2 14 75078651-75078687 378 LYL1 19 13210058-13210180 379 LYPLAL1 1 219347185-219347277 380 MAX.chrl .228651512-228651589 1 228651512-228651589 381 MAX.chrl .8014264-8014320 1 8014264-8014320 382 MAX.chrl 0.22541719-22541758 10 22541719-22541758 383 MAX.chrl 0.94459281-94459353 10 94459281-94459353 384 MAX.chrl 1.32355226-32355251 11 32355226-32355251 385 MAX.chrl 1.8041275-8041318 11 8041275-8041318 385 MAX.chrl 1.8041275-8041318 11 8041275-8041318 386 MAX.chrl 4.102172621-102172686 14 102172621-102172686 387 MAX.chrl 4.105512122-105512239 14 105512122-105512239 388 MAX.chrl 5.65186405-65186481 15 65186405-65186481 389 MAX.chrl 5.95128144-95128248 15 95128144-95128248 390 MAX.chrl 6.11327016-11327312 16 11327016-11327312 391 MAX.chrl 7.77789297-77789347 17 77789297-77789347 392 MAX.chrl 9.13266870-13266980 19 13266870-13266980 2026204791   22 Jun 2026 393 MAX.chrl 9.42028466-42028519 19 42028466-42028519 394 MAX.chr2.231693015-231693073 2 231693015-231693073 395 MAX.chr2.73511979-73512039 2 73511979-73512039 396 MAX.chr3.187676577-187676668 3 187676577-187676668 397 MAX.Chr4.174430676-174430847 4 174430676-174430847 398 MAX.chr5.77147757-77147813 5 77147757-77147813 399 MAX.Chr6.130088620-130088690 6 130088620-130088690 400 M AX.Chr6.42738968-42739055 6 42738968-42739055 401 MAX.chr8.145900783-145900914 8 145900783-145900914 402 M AX.chr8.80804237-80804301 8 80804237-80804301 403 M AX.Chr9.33524209-33524289 9 33524209-33524289 404 MPZ A 1 161275561-161275996 405 N4BP2L1 C 13 33001374-33001575 406 N4BP3 5 177543694-177543863 407 NCOR2 12 124941781-124942044 408 NFATC1 A 18 77159542-77159614 409 NFATC1 B 18 77159813-77159893 410 NKX2-6 8 23564281-23564374 411 NR2F6 19 17346567-17346673 412 NR3C1 A 5 142784971-142785160 413 NR3C1 B 5 142784614-142784698 414 NTN1 17 9143174-9143253 415 OSM 22 30662648-30662807 416 PALLD C 4 169799226-169799423 417 PHLDB1 B 11 118481753-118481814 418 PIK3CD 1 9777870-9777967 419 PLCL2 3 16925870-16925914 420 PNMAL2 19 46996933-46996985 421 PRDM13 6 100061723-100061766 422 PRKAR1B 7 644126-644332 423 RAD52 12 1059296-1059503 424 SEPT9 C 17 75447656-75447714 425 SNN 16 11763081-11763138 426 SPDYA A 2 29033287-29033484 427 SPON2 4 1161228-1161298 428 ST8SIA1 12 22487403-22487492 429 STX16 A 20 57224620-57224975 430 SUCLG2 3 67706348-67706568 431 TJP2 9 71788863-71788954 432 TLE4 9 82188097-82188284 433 TNFRSF1B 1 12227425-12227514 434 TNFRSF4 1 1148413-1148487 435 TNRC18 7 5436900-5436991 436 TSPAN33 7 128809205-128809241 437 UST B 6 149068833-149068925 438 VENTX 10 135050110-135050178 439 WDR86 7 151078576-151078610 440 XKR6 8 11058545-11058598 441 ZDHHC18 1 27160118-27160221 442 ZNF227 19 44711531-44711781 315 ZNF568A 19 37407197-37407284 2026204791   22 Jun 2026 443 ZNF586 C 19 58281020-58281200 444 ZNF671 A 19 58238740-58238799 Table 9. Area-under-the-curve, fold-change, and p-value for EC in comparison to EC huffy controls for the markers recited in Table 8. DMR No. Gene Annotation AUC EC vs. EC buffy control Fold Change EC vs. EC buffy control p-value EC vs. EC buffy control 319 ACOXL B 0.6786 26080000 0.991 320 ACTG1 0.709 34560000 0.9928 321 ANKRD35 1 627.5 0.005999 499 ARL5C 0.9614 137.5 0.0001678 322 ARRB1 0.9044 59100000 0.9902 323 BCL2L11 A 1 237.1 0.0004125 324 BCL2L11 B 0.9975 370.4 0.002033 325 BCL2L11 C 1 342.4 0.002845 326 BEST4 0.7845 38650000 0.9902 327 BZRAP1 0.9918 2116 0.0002676 328 C14orf169 B 0.7045 71770000 0.9904 329 C14orf169 C 0.7045 141100000 0.9914 330 C14orf80 0.875 162800000 0.9914 331 C16orf54 1 542.3 7.39E-05 332 C17orf101 1 2.27E+09 0.9918 333 C18orf1 0.7738 19.55 0.009107 334 C6orf132 1 593.2 0.001901 335 C9orf171 0.7321 36790000 0.9896 336 CACNA2D4 0.9338 80450000 0.9891 337 CCDC61 0.7109 63800000 0.9915 338 DEDD2 1 527.9 6.12E-08 339 DGKE 0.7426 24970000 0.9924 340 EGFL7 0.7344 72630000 0.9928 341 EMB 0.86 189900000 0.9916 342 EOMES 0.6633 50060000 0.9913 343 EPS15L1 1 725500000 0.99 344 FAIM2 0.9828 330.1 0.00851 345 FAM125B 0.9394 7.61 E+08 0.9899 346 FAM159A 0.6889 100800000 0.9924 347 FAM189B 0.995 135.3 0.003158 348 FAM 78 A 1 1404 9.92E-06 349 FMNL1 0.8333 75120000 0.9918 350 FOXP4 0.9776 582200000 0.9892 351 GAL3ST4 0.8167 90980000 0.9908 352 GATA2 C 0.8492 109200000 0.9901 353 GP1BB 0.7119 42170000 0.9924 354 GYPC A 0.9924 770700000 0.9901 355 GYPC B 0.9397 664100000 0.9906 356 HAAO 0.8889 1.53E+08 0.9906 2026204791   22 Jun 2026 357 HAND2 0.7923 46610000 0.9895 358 HDAC7 0.7537 50550000 0.9898 359 HOPX B 0.6983 70210000 0.9914 360 HOXA7 0.7404 83950000 0.9909 361 HOXB4 0.697 42010000 0.9915 362 HRH2 0.7419 78270000 0.9913 363 IFFO1 A 0.9692 92880000 0.9902 364 IFFO1 B 0.9701 744200000 0.9913 119 IL12RB2 0.6953 16740000 0.9928 365 IQSEC3 A 0.7576 29400000 0.9894 366 IQSEC3 B 0.7302 45010000 0.9926 367 ITGA4 B 0.7647 129900000 0.992 368 ITPKA 1 499.2 0.003773 369 KLF16 0.9083 165100000 0.9921 370 LIMD2 0.9603 493100000 0.9911 371 LOC100129726_ A 0.6692 29650000 0.9887 382 LOC100192379_ B 0.6667 3.10E+07 0.9939 373 LOC339529 0.8273 115200000 0.9902 374 LOC389333 0.9545 393300000 0.9917 375 LOC440925 A 0.9959 274.8 0.007478 376 LOC646278 0.9016 86610000 0.9898 377 LTBP2 0.7636 65880000 0.9912 378 LYL1 0.9887 545.8 0.006049 379 LYPLAL1 0.9846 2.10E+09 0.9917 380 MAX.chrl .228651 512-228651589 0.7734 51900000 0.9889 381 MAX.chrl .801426 4-8014320 0.8929 188300000 0.9925 382 MAX.chrl 0.22541 719-22541758 0.8871 139100000 0.992 383 MAX.chrl 0.94459 281-94459353 0.8364 1.26E+08 0.9927 384 MAX.chrl 1.32355 226-32355251 0.9731 471.5 0.008476 385 MAX.chrl 1.80412 75-8041318 0.6562 50170000 0.9949 386 MAX.chrl 4.10217 2621-102172686 0.9224 380600000 0.9916 387 MAX.chrl 4.10551 2122-105512239 0.9924 512600000 0.989 388 MAX.chrl 5.65186 405-65186481 0.7769 134800000 0.9917 389 MAX.chrl 5.95128 144-95128248 0.9678 126.9 0.003344 390 MAX.chrl 6.11327 016-11327312 0.9984 837.5 1.85E-05 391 MAX.chrl 7.77789 297-77789347 0.8689 82590000 0.9919 392 MAX.chrl 9.13266 870-13266980 0.7077 104900000 0.993 393 MAX.chrl 9.42028 466-42028519 0.8727 246200000 0.9902 2026204791   22 Jun 2026 394 MAX.chr2.231693 015-231693073 0.6932 71170000 0.9936 395 MAX.chr2.735119 79-73512039 0.6778 95180000 0.9931 396 MAX.chr3.187676 577-187676668 0.9984 677.2 1.78E-05 397 MAX.chr4.174430 676-174430847 0.9877 105.5 0.00112 398 MAX.chr5.771477 57-77147813 0.6596 31920000 0.9924 399 MAX.chr6.130088 620-130088690 0.7281 75460000 0.9915 400 M AX.chr6.427389 68-42739055 0.6923 41460000 0.9921 401 MAX.chr8.145900 783-145900914 1 1127 2.26E-05 402 M AX.chr8.808042 37-80804301 0.9519 83590000 0.9922 403 M AX. chr9.335242 09-33524289 0.7653 68470000 0.9939 404 MPZ A 0.8914 26.19 0.0129 405 N4BP2L1 C 0.675 43500000 0.9929 406 N4BP3 1 284.9 0.003001 407 NCOR2 0.9992 334.7 0.002568 408 NFATC1 A 0.9886 360.7 0.003405 409 NFATC1 B 0.9385 73470000 0.9885 410 NKX2-6 0.9889 452800000 0.9932 411 NR2F6 0.9403 1.10E+09 0.9931 412 NR3C1 A 0.7687 42200000 0.9904 413 NR3C1 B 0.6846 41810000 0.9926 414 NTN1 0.8361 100500000 0.9909 415 OSM 0.9906 894.1 0.0004214 416 PALLD C 1 369.1 0.001153 417 PHLDB1 B 0.6786 42240000 0.9894 418 PIK3CD 0.9731 82.97 0.0002239 419 PLCL2 0.7705 67150000 0.9898 420 PNMAL2 0.8433 117600000 0.9914 421 PRDM13 0.7347 35550000 0.9914 422 PRKAR1B 1 537 0.003643 423 RAD 52 0.9252 71.45 0.002206 424 SEPT9 C 0.6909 30860000 0.991 425 SNN 0.71 49210000 0.994 426 SPDYA A 0.8696 113700000 0.9899 427 SPON2 0.7803 64470000 0.9911 428 ST8SIA1 0.6939 61890000 0.9932 429 STX16 A 1 889.9 0.0002353 430 SUCLG2 1 4174 0.001157 431 TJP2 0.6923 64420000 0.9901 432 TLE4 0.6667 65910000 0.9928 433 TNFRSF1B 0.9196 99080000 0.9908 434 TNFRSF4 0.9615 205500000 0.9893 435 TNRC18 0.8906 186900000 0.9914 436 TSPAN33 0.8125 96600000 0.9903 437 UST_B 0.6885 29650000 0.9931 2026204791   22 Jun 2026 438 VENTX 0.8016 32390000 0.9904 439 WDR86 0.8939 184500000 0.9874 440 XKR6 0.8021 47230000 0.9913 441 ZDHHC18 0.9926 5.61 E+09 0.9902 442 ZNF227 0.7132 51410000 0.9916 315 ZNF568 A 0.6967 59100000 0.9923 443 ZNF586 C 0.7188 42520000 0.9877 444 ZNF671A 0.9167 200800000 0.9923 From these marker groups 56 candidates were chosen for an initial pilot. Methylationspecific PCR assays were developed and tested on two rounds of samples; those that were sequenced and larger independent cohorts. Short amplicon primers (<150bp) were designed to target the most discriminant CpGs within a DMR and tested on controls to ensure that fully methylated fragments amplified robustly and in a linear fashion, that unmethylated and / or unconverted fragments did not amplify. The 112 primer sequences and annealing temperatures for the 56 candidate markers are listed in Table 10. Table 10. Gene Annotation DM R No. Forward Primer 5'3' Seq ID Reverse Primer 5'-3' Seq ID Annealing Temperature °C / Final SFMBT2_B 262 GCG CGC GGT TTT GGG AGA TAA GTA C 1 AAAAAAAAC AAC CCC TCG CCT CGA C 2 70 SMTN 273 AGG Illi IAGGA TAT TTA GTT GAG TGG CGG 3 ACC TCG ATC CCG AAT TCG AAT TCG AC 4 70 SQSTM1 278 GTT TTC GGT TAT TCG GTG ACG G 5 AAA AAA CTA AAA AAC GAA TCG CGC T 6 65 ZNF323_A 312 TTT AAT GAT CGA TTA ATC GTA AAG GTC GG 7 AAC CAA TAA ACT CAA AAC GAC TAA CGC A 8 65 ZNF506 314 TTA GGT TTT TAG GGG GTT TCG GCG T 9 ATC GTC TTC ACT ACT CTA TAG CGT C 10 65 ZNF90 318 AAT TGG GTA AGG AGA AGT CGG TCG T 11 ATA ACG AAA CTT AAA CCT CCC CGC A 12 70 2026204791   22 Jun 2026 ACOXL_A 2 AGT TAA GTT TTA ACG GGT GTG GCG G 13 AAA CGT CGA TAA AAC GAA CGTCGTA 14 70 CLDN7 55 TAT CGT TGT TTC GAG TCG GGG ACG A 15 AAC CGA AAT TCC GAC GAC TAG ACG T 16 65 LRRC41_B 148 GGT TCG GAG CGG TTT AAA TAA GCG A 17 CTT AAC CCT TCC CGC CTA TCC GTC 18 70 MAX.chr7.1 04624356104624730 204 TTG GGG GTT GTC GGT TTT TGG AGA C 19 CCG ATC TAA ATA CCC CAA ACG AAA TCG AA 20 70 NDRG2 226 CGT TTT TAG ATT TAG TGG TGG GAA TCG G 21 TCG AAC GAA AAA AAT CGA ACT CGT A 22 60 CYP11A1 57 III IIC GCG GGT CGT TTA TTT TCGT 23 AAA CGA ATA AAC TCG AAC TAT ATC GAA 24 65 FKBP11_A 88 TTA CGA TCG GAT TAT AGG GGT TAG GG 25 TAG CGA ATC TAA AAA CGA AAA CGA A 26 65 MAX.chr8.1 45103829145103992 207 GGG GAG TTA TAG GGG TGA AGG TCG C 27 GCC TCC GCC AAA CTC GCT ACG TC 28 70 AHSA2 10 TAT TTG GCG CGT GGG GAG AGG TC 29 TCC CTT CCG AAA ATT CTA CGA CGA A 30 65 CYTH2 59 TTT TAG GGT AAA TAG CGG GTT TCGT 31 CGA CCG CCC TAG ATA CAA TTC ATC CG 32 65 GATA2_B 100 GTG TGA TAG ACG TTA GAG CGG CGG 33 CGT TTT AAT CAA AAA AAT CTC CCG TA 34 65 LRRC8D_A 152 GGG AGA ATT CGA GTA GTA GTT GTA AAC GG 35 AAT AAC CTC GCT ACC AAC CAC CCG C 36 65 MAX.chr8.1 45104263145104422 208 GGG CGT TGT TTC G I I III III ATC GT 37 GAA ACG CGC TTA CCC GTC GAA 38 70 2026204791   22 Jun 2026 OBSCN_A 234 GTT CGT TAT CGT TTG GTT TTG TAT AAC GT 39 TAT ATC TTA TCA TCC GAC GTC TCG CA 40 65 DIDO1_A 63 TAT TTG GGA TTT AGA GAG GTA GCG G 41 CCA AAA ACC GAA ACC TAA ACG CT 42 70 GDF6 102 TTT TAT TTC GTA GAC GAT TTT TCG T 43 GAA AAA ACC GCA ACT CCG CGC 44 65 MAX.chMO. 130339363130339534 165 AAT AAT AGG AAT TAG AGG TTG TCG G 45 AAATAACAA ACT CCG CGC GCG AA 46 65 MDFI_B 212 TAG GGT TCG TAG GAG TGA GTG GAC GT 47 ACG CCG AAA ACG AAC AAA AAACGAT 48 70 DLL4 67 III IIC GTA GCG ATC GTA GCG GCG T 51 ACC TAG TAA ACA AAC CAA AAACGAA 52 65 GDF7_A 103 TTC GTT TAG AAG GCG GGT GGA AGG TC 53 AAA AAA TCT CGC GCG AAA ATA CGC T 54 65 MAX.chMO. 2262447922624553 167 GGA AGG TTA GGG GGA AAT TTG TAT TTC GT 55 CGT AAC ATC GTC ATT TCT TAA CCG CGA T 56 70 MIAT_A 214 TTT CGT ATT AAA ATT TTA TGG GCG T 57 TCT AAT CCC GCG AAC GCA ACC G 58 60 PYCARD 248 TAG TTT TGT TTA GGG GTA GGA GGA ATA GAA AGC G 59 ACA CCA ACG CTT ACC CCG CGA A 60 65 BMP4_B 26 TTT TCG ATC GTG GAT GTT CGG AGTC 61 GAA AAC CGC GCG ACT CTT ACC GAA 62 70 JSRP1_A 124 GGG AGG GGT CGT AGG AGT GTT TTC G 63 ATA ACG TTC TAG CGC CTT TCC CCT ACG C 64 70 MAX.chr14. 103021656103021718 174 GAA AGC GAA ACG GTT TCG GCG TC 65 CAA ACT TCC GAA TCC TAG CCC CGC 66 70 2026204791   22 Jun 2026 MIAT_B 215 TCG AGA GAG GTC GGT III III TAT CGT 67 AAA CTT CCG ATC ACG ACC CCA CGT C 68 70 RHBDL1_A 252 TCG TTG GTA AAT GGA GTT ACG G 69 GAA AAA ACT ATA AAA AAA CGA ACG AT 70 60 EMX2 80 GTA TTT ATC GCG TTT TCG AGT TCG A 71 TAT AAC GCG ACC CCA ACG CT 72 70 KANK1 126 GTA GTC GGA GGG AGA TTT CGT CGG 73 ATA AAC TTA ACC GAC CAC GCTCGAA 74 65 MMP23B 218 CGG GTT GTA ATT CGA GTC GTC GA 75 CAA AAC CTC CGA AAA AAA TCC GAA 76 65 SBNO2 256 GTA TAG GGC GTC G I I III AGT TCG A 77 AAA AAA TCT ACC GAA AAA TTC CGA A 78 60 C5orf52 40 TTG GTT TAA TTC GTT ATT CGT TTC GT 79 AAC AAA CCT TTT CCG CTT CGA CGT A 80 65 EMX2OS 81 CGA AGT TCG GGT AGG GTA AGC GTT GC 81 CGA CGT AAA AAT ACG AAA CGC ACG AA 82 65 LRRC34 146 GTG AGG CGG TTA TAG GAG TTT CGG C 83 CAA AAA ACC TCC ACA AAA TAA ACG AT 84 65 MAX.chr17. 7307371673073814 180 III IIC GAG TCG TTT TAT TTC GCG G 85 GAA CTC CGA ACG CCG CTT AAA CGT A 86 70 NBPF8 162 CGC GTA GGT GTT TAA CGT GAT TAG CGC 87 CTT ACA TCC TCA AAA CCC GCC CGA C 88 65 SEPT9_B 259 TTA TGG TGG CGG TGT CGG GAG TTA C 89 CCC TCT CCT AAA AAC CCC GCT CGA T 90 70 LOC440925 _A 375 AGT TCG CGT TCG G I I III I IG TTC G 91 GTC CGT CCC GAT CGC AAT ACG A 92 65 2026204791   22 Jun 2026 STX16_A 429 CGC GTT GCG CGG AAG TTA GAG TC 93 CCA CAT AAA ATC GAA AAA ACC GCG AA 94 65 ITPKA 368 GGG TTT ATA AGT TCG GAG GTC GA 95 CAC CCA ACA CCT AAC GAC GA 96 65 AIM1_A 11 AGC G I I III AGG GAG TTC GGC GTT C 97 AAT CGA AAA AAC GAA AAA AAT CGC A 98 65 EEF1A2 75 TAG GTC GTT TCG TCG TGC GC 101 ATA ACC TTA CCG ACG CCG CCG CT 102 70 FEV 87 III I I G AAG AGA TCG TTT TCG ACG G 103 CCC CCT TAA ACC TTA ACC CGA A 104 65 LRRC41_C 149 GGC GTT TCG All III TCG TTC GG 105 CCG AAA CTC CAA CAT CTA CCT AAC ACG CC 106 65 NFIC 230 CGT AAT TTT TGG CGA GCG ACG TTT GC 107 CAA CCT TCG AAA TCC CCC ATC CGC T 108 70 VILL 302 GGT TTT GGG GGA TTT AGG GTT CGG 49 TCC GCG AAA ACC CCT ACC TAA CGT C 50 70 MPZ_A 404 GGG GCG TAT ATA TTA GTT ATC GAG CGA 99 AAA AAA AAC CCT AAA AAC CGC CGA A 100 65 The results from round one validation were analyzed logistically to determine AUC and fold change. From previous work it was recognized that the epigenetics of cancer subtypes within an organ differ and that the best panels are derived from combinations of subtype markers. Analyses for the tissue and buffy coat controls were run separately. Results are highlighted in Tables 11 (clear cell EC vs. buffy coat), 12 (serous EC vs. buffy coat), 13 (cacinosarcoma EC vs. buffy coat), and 14 (endometrioid EC vs. buffy coat). The gray-scaled red shading over certain genes indicates DMRs which overlap with multiple subtypes. The degree of grey-scaled red shading indicates the discrimination strength of the marker assay. A number of assays were 100% discriminant in EC from buffy coat samples and approaching 100% in the EC vs benign endometrium comparison. 2026204791   22 Jun 2026 Table 11. DMRs distinguishing 1) clear cell EC and huffy coat and 2) clear cell EC and normal endometrium and normal cervicovaginal tissue DMR No. Gene Name AUC / Buffy Coat FC / Buffy Coat AUC / normal endometrium and normal cervicovaginal tissue FC / normal endometrium and normal cervicovaginal tissue 262 SFMBT2_B 0.97 1179.65 0.72 11.52 273 SMTN 0.89 199.57 0.51 7.59 278 SQSTM1 0.91 201.30 0.87 18.50 312 ZNF323_A 1.00 422080.20 0.98 343.83 314 ZNF506 0.94 451.38 0.70 8.09 318 ZNF90 0.93 44.22 0.50 1.77 2 ACOXL_A 0.81 122.93 0.61 1.43 55 CLDN7 0.97 15.54 0.73 0.65 148 LRRC41_B 0.97 142.56 0.68 6.81 204 MAX.chr7.104624356-104624730 0.93 187.02 0.93 100.90 226 NDRG2 0.95 285.41 0.91 111.93 57 CYP11A1 0.94 101.29 0.59 1.83 88 FKBP11_A 0.83 17.00 0.65 4.46 207 MAX.chr8.145103829-145103992 0.93 1107.83 0.74 18.09 10 AHSA2 0.88 61.87 0.72 5.77 59 CYTH2 0.99 152.59 0.81 3.59 100 GATA2_B 0.89 518.49 0.67 12.96 152 LRRC8D_A 0.92 323.21 0.74 11.32 208 MAX.chr8.145104263-145104422 0.96 258.53 0.67 12.62 234 OBSCN_A 1.00 2614.39 0.89 30.04 63 DIDO1_A 0.97 918.19 0.91 16.37 102 GDF6 0.99 203.64 0.62 4.22 165 MAX.chrl 0.130339363130339534 0.92 18.64 0.75 3.52 212 MDFI_B 0.94 1749.15 0.90 42.70 67 DLL4 0.96 12.73 0.60 0.31 103 GDF7_A 0.92 224.96 0.84 27.86 167 MAX.chrl 0.2262447922624553 0.85 2399.57 0.75 24.84 214 MIAT_A 0.93 1055.89 0.83 98.21 248 PYCARD 0.94 106.61 0.57 6.24 26 BMP4_B 0.95 127.50 0.56 7.73 124 JSRP1_A 0.98 81.87 0.78 4.52 174 MAX.chr14.103021656-103021718 0.98 2953.08 0.97 184.74 215 MIAT_B 0.87 99.67 0.38 3.32 252 RHBDL1_A 0.71 20.30 0.76 12.49 2026204791   22 Jun 2026 80 EMX2 0.92 422.01 0.85 35.48 126 KANK1 0.73 23.85 0.64 6.84 218 MMP23B 0.97 640.18 0.92 25.53 256 SBNO2 0.83 8.43 0.57 0.51 40 C5orf52 0.59 59.11 0.65 0.68 81 EMX2OS 0.98 154.84 0.89 5.95 146 LRRC34 0.81 62.10 0.61 2.27 180 MAX.chrl 7.7307371673073814 1.00 283.78 0.87 22.84 162 NBPF8 0.97 69.67 0.85 7.68 259 SEPT9_B 0.99 1751.41 0.94 70.17 375 LOC440925_A 1.00 304.06 0.49 1.04 429 STX16_A 0.90 173.42 0.94 53.85 368 ITPKA 1.00 1509.47 0.58 0.96 11 AIM1_A 0.79 15826.65 0.78 307.38 75 EEF1A2 0.97 289.12 0.83 41.35 87 FEV 0.94 537.52 0.84 19.39 149 LRRC41_C 0.98 392.66 0.72 18.82 230 NFIC 0.95 107.52 0.69 6.95 302 VILL 0.88 49.58 0.44 3.39 404 MPZ_A 0.85 1112.98 0.61 6.77 Table 12. DMRs distinguishing 1) serous EC and huffy coat and 2) serous EC and normal endometrium and normal cervicovaginal tissue DMR No. Gene Name AUC / Buffy Coat FC / Buffy Coat AUC / normal endometrium and normal cervicovaginal tissue FC / normal endometrium and normal cervicovaginal tissue 262 SFMBT2_B 0.91 594.75 0.65 5.81 273 SMTN 1.00 235.25 0.70 8.95 278 SQSTM1 1.00 155.56 0.80 14.30 312 ZNF323_A 0.88 400850.18 0.88 326.53 314 ZNF506 0.81 181.26 0.62 3.25 318 ZNF90 1.00 124.47 0.63 4.97 2 ACOXL_A 0.87 4248.44 0.63 49.40 55 CLDN7 1.00 15.07 0.58 0.63 148 LRRC41_B 1.00 170.65 0.82 8.15 204 MAX.chr7.104624356-104624730 0.94 435.76 0.94 235.10 226 NDRG2 0.73 108.35 0.75 42.49 57 CYP11A1 0.91 420.04 0.73 7.61 88 FKBP11_A 0.92 153.12 0.84 40.21 207 MAX.chr8.145103829-145103992 1.00 886.56 0.72 14.47 10 AHSA2 0.94 33.32 0.69 3.11 59 CYTH2 0.97 137.39 0.70 3.23 2026204791   22 Jun 2026 100 GATA2_B 0.81 481.98 0.68 12.05 152 LRRC8D_A 0.98 681.74 0.85 23.87 208 MAX.chr8.145104263-145104422 1.00 236.41 0.70 11.54 234 OBSCN_A 0.93 2837.86 0.76 32.61 63 DIDO1_A 0.83 1663.93 0.80 29.67 102 GDF6 1.00 172.69 0.67 3.58 165 MAX.chrl 0.130339363130339534 0.87 5.69 0.61 1.07 212 MDFI_B 0.74 926.85 0.75 22.63 67 DLL4 0.94 34.95 0.62 0.85 103 GDF7_A 0.71 335.86 0.68 41.60 167 MAX.chrl 0.2262447922624553 0.77 2245.78 0.65 23.25 214 MIAT_A 0.84 378.27 0.61 35.18 248 PYCARD 1.00 29.18 0.40 1.71 26 BMP4_B 0.97 51.17 0.46 3.10 124 JSRP1_A 0.99 78.15 0.65 4.31 174 MAX.chr14.103021656-103021718 0.76 2225.70 0.65 139.24 215 MIAT_B 0.90 325.27 0.69 10.85 252 RHBDL1_A 0.78 32.70 0.76 20.12 80 EMX2 0.68 439.81 0.71 36.98 126 KANK1 0.91 54.42 0.88 15.60 218 MMP23B 0.77 139.13 0.80 5.55 256 SBNO2 0.78 32.10 0.50 1.93 40 C5orf52 0.74 54.72 0.56 0.63 81 EMX2OS 1.00 286.88 0.91 11.02 146 LRRC34 0.72 316.99 0.60 11.61 180 MAX.chrl 7.7307371673073814 0.80 151.03 0.74 12.16 162 NBPF8 0.99 101.15 0.79 11.15 259 SEPT9_B 0.72 508.74 0.64 20.38 375 LOC440925_A 1.00 347.38 0.51 1.18 429 STX16_A 0.76 159.65 0.80 49.58 368 ITPKA 1.00 1869.01 0.50 1.18 11 AIM1_A 0.71 2731.20 0.70 53.05 75 EEF1A2 0.93 59.07 0.63 8.45 87 FEV 0.90 648.38 0.76 23.39 149 LRRC41_C 1.00 530.59 0.94 25.43 230 NFIC 0.92 165.59 0.73 10.71 302 VILL 0.96 120.29 0.66 8.23 404 MPZ_A 0.94 3826.67 0.89 23.28 Table 13. DMRs distinguishing 1) carcinosarcoma EC and huffy coat and 2) carcinosarcoma EC and normal endometrium and normal cervicovaginal tissue 2026204791   22 Jun 2026 DMR No. Gene Name AUC / Buffy Coat FC / Buffy Coat AUC / normal endometrium and normal cervicovaginal tissue FC / normal endometrium and normal cervicovaginal tissue 262 SFMBT2_B 0.99 1428.99 0.68 13.95 273 SMTN 1.00 377.39 0.76 14.36 278 SQSTM1 0.62 284.30 0.58 26.13 312 ZNF323_A 0.85 485857.78 0.86 395.78 314 ZNF506 0.97 536.08 0.79 9.60 318 ZNF90 1.00 114.78 0.77 4.58 2 ACOXL_A 0.73 7752.33 0.64 90.14 55 CLDN7 0.98 115.79 0.46 4.87 148 LRRC41_B 1.00 66.45 0.62 3.17 204 MAX.chr7.104624356-104624730 0.84 745.78 0.85 402.36 226 NDRG2 0.68 29.19 0.69 11.45 57 CYP11A1 0.93 140.16 0.66 2.54 88 FKBP11_A 0.85 25.24 0.73 6.63 207 MAX.chr8.145103829-145103992 0.95 2543.71 0.66 41.53 10 AHSA2 0.96 226.81 0.77 21.16 59 CYTH2 1.00 263.29 0.85 6.19 100 GATA2_B 0.98 576.22 0.61 14.40 152 LRRC8D_A 0.96 776.27 0.75 27.18 208 MAX.chr8.145104263-145104422 0.94 497.10 0.64 24.27 234 OBSCN_A 0.99 3188.04 0.83 36.63 63 DIDO1_A 1.00 2258.45 0.88 40.27 102 GDF6 1.00 298.43 0.79 6.18 165 MAX.chrl 0.130339363130339534 0.87 24.92 0.60 4.71 212 MDFI_B 0.62 282.92 0.65 6.91 67 DLL4 1.00 42.28 0.68 1.03 103 GDF7_A 0.76 455.11 0.71 56.37 167 MAX.chrl 0.2262447922624553 0.93 4917.08 0.82 50.91 214 MIAT_A 0.80 60.34 0.59 5.61 248 PYCARD 0.98 55.62 0.51 3.25 26 BMP4_B 0.98 270.79 0.52 16.41 124 JSRP1_A 1.00 57.52 0.61 3.18 174 MAX.chr14.103021656-103021718 0.91 4012.26 0.88 251.00 215 MIAT_B 0.92 196.61 0.77 6.56 252 RHBDL1_A 0.64 22.37 0.68 13.76 80 EMX2 0.85 485.41 0.86 40.81 126 KANK1 0.82 194.54 0.79 55.77 218 MMP23B 0.43 102.68 0.57 4.09 256 SBNO2 0.89 297.56 0.73 17.85 40 C5orf52 0.76 3076.88 0.59 35.54 2026204791   22 Jun 2026 81 EMX2OS 1.00 383.68 0.99 14.74 146 LRRC34 1.00 634.47 0.90 23.23 180 MAX.chrl 7.7307371673073814 0.89 618.49 0.83 49.78 162 NBPF8 0.98 115.64 0.86 12.75 259 SEPT9_B 0.64 233.76 0.60 9.37 375 LOC440925_A 1.00 450.23 0.57 1.53 429 STX16_A 0.64 296.41 0.73 92.04 368 ITPKA 1.00 4030.59 0.69 2.55 11 AIM1_A 0.81 5230.38 0.78 101.58 75 EEF1A2 0.85 59.43 0.54 8.50 87 FEV 0.87 295.29 0.68 10.65 149 LRRC41_C 0.84 168.11 0.58 8.06 230 NFIC 1.00 141.82 0.70 9.17 302 VILL 0.99 171.70 0.88 11.74 404 MPZ_A 0.84 2691.51 0.66 16.38 Table 14. DMRs distinguishing 1) endometrioid EC and huffy coat and 2) endometrioid EC and normal endometrium and normal cervicovaginal tissue DMR No. Gene Name AUC / Buffy Coat FC / Buffy Coat AUC / normal endometrium and normal cervicovaginal tissue FC / normal endometrium and normal cervicovaginal tissue 262 SFMBT2_B 0.99 4102.95 0.87 40.06 273 SMTN 1.00 177.04 0.72 6.74 278 SQSTM1 1.00 152.79 0.84 14.04 312 ZNF323_A 0.89 767729.43 0.89 625.39 314 ZNF506 0.94 1764.70 0.87 31.61 318 ZNF90 1.00 286.45 0.84 11.44 2 ACOXL_A 0.72 1420.62 0.62 16.52 55 CLDN7 1.00 18.78 0.55 0.79 148 LRRC41_B 1.00 137.20 0.52 6.55 204 MAX.chr7.104624356-104624730 0.80 189.27 0.81 102.12 226 NDRG2 0.69 189.12 0.72 74.17 57 CYP11A1 0.89 356.99 0.60 6.46 88 FKBP11_A 0.96 63.66 0.83 16.72 207 MAX.chr8.145103829-145103992 1.00 4309.89 0.93 70.36 10 AHSA2 0.93 99.00 0.76 9.24 59 CYTH2 1.00 443.30 0.94 10.42 100 GATA2_B 0.91 1201.79 0.68 30.04 152 LRRC8D_A 0.96 1104.73 0.76 38.68 208 MAX.chr8.145104263-145104422 1.00 1291.27 0.82 63.05 234 OBSCN_A 0.89 2144.25 0.73 24.64 63 DIDO1_A 0.99 1143.24 0.90 20.39 2026204791   22 Jun 2026 102 GDF6 1.00 182.30 0.73 3.78 165 MAX.chrl 0.130339363130339534 0.92 40.14 0.69 7.59 212 MDFI_B 0.83 545.42 0.85 13.31 67 DLL4 0.98 17.08 0.52 0.42 103 GDF7_A 0.59 343.11 0.57 42.50 167 MAX.chrl 0.2262447922624553 0.97 12943.30 0.92 134.01 214 MIAT_A 0.91 1058.99 0.66 98.50 248 PYCARD 1.00 47.75 0.55 2.79 26 BMP4_B 0.99 194.14 0.68 11.76 124 JSRP1_A 1.00 136.00 0.91 7.51 174 MAX.chr14.103021656-103021718 0.93 3958.93 0.89 247.66 215 MIAT_B 0.94 436.40 0.73 14.56 252 RHBDL1_A 0.89 35.39 0.86 21.78 80 EMX2 0.75 196.50 0.75 16.52 126 KANK1 0.93 171.50 0.88 49.17 218 MMP23B 0.44 43.50 0.59 1.73 256 SBNO2 1.00 270.32 0.90 16.21 40 C5orf52 0.90 10081....

Claims

1.     A method, comprising:measuring a methylation level for one or more genes in a biological sample of a human individual throughtreating genomic DNA in the biological sample with a reagent that modifies DNA in a methylation-specific manner;amplifying the treated genomic DNA using a set of primers for the selected one or more genes; anddetermining the methylation level of the one or more genes by polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation-specific nuclease, mass-based separation, and target capture;wherein the one or more genes is selected from one of the following groups:(i) AFF3, AIM1_A, AMIGO3_A, BMP4_B, C17orf107_A, C1orf70_B, C5orf52, CLDN7, DIDO1_A, EEF1A2, EMX2OS, FEV, FKBP11_A, GDF6, GDF7_A, JSRP1_A, KCTD15_A, KLHL21, LRRC8D_A, NBPF8, MAX.chr10.130339363-130339534, MAX.chr10.22624479-22624553, MAX.chr14.103021656-103021718, MAX.chr8.145103829-145103992, MAX.chr8.145104263-145104422, MDFI_B, MIAT_A, MMP23B, NDRG2, OBSCN_A, PCOLCE, PYCARD, SEPT9_B, SLC6A3_A, SLC8A3_B, SQSTM1, VILL, ZNF302, ZNF323_A, ZNF506, and ZNF90;(ii)   EMX2OS, CYTH2, C17orf107_A, DIDO1_A, GDF6, NBPF8,MAX.chr14.103021656-103021718, JSRP1_A, GATA2_B, and SFMBT2_B;(iii)   SFMBT2_B, ZNF90, MAX.chr8.145103829-145103992,CYTH2, LRRC8D_A, OBSCN_A, DIDO1_A, MAX.chr10.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ_A;(iv)   EMX2OS, CYTH2, NBPF8, MAX.chr10.22624479-22624553;(v)   ANKRD35, ARL5C, ARRB1, BCL2L11_A, BCL2L11_B,BCL2L11_C, BZRAP1, C16orf54, C17orf101, C6orf132, CACNA2D4,2026204791   22 Jun 2026DEDD2, EPS15L1, FAIM2, FAM125B, FAM189B, FAM78A, FOXP4, GYPC_A, GYPC_B, IFFO1_A, IFFO1_B, ITPKA, KLF16, LIMD2, LOC389333, LOC440925_A, LOC646278, LYL1, LYPLAL1, MAX.chr11.32355226-32355251, MAX.chr14.102172621-102172686, MAX.chr14.105512122-105512239, MAX.chr15.95128144-95128248, MAX.chr16.11327016-11327312, MAX.chr3.187676577-187676668, MAX.chr4.174430676-174430847, MAX.chr8.145900783-145900914, MAX.chr8.80804237-80804301, N4BP3, NCOR2, NFATC1_A, NFATC1_B, NKX2-6, NR2F6, OSM, PALLD_C, PIK3CD, PRKAR1B, RAD52, STX16_A, SUCLG2, TNFRSF1B, TNFRSF4, ZDHHC18, and ZNF671_A;(vi) DIDO1_A, NDRG4, MAX.chr14.103021656-103021718, MMP23B, EMX2OS, SEPT9_B, NBPF8, EEF1A2, AIM1_A, BMP4_B, MAX.chr8.145103829-145103992, OBSCN, PYCARD, GDF6, MDFI_B, MIAT_A, SCL8A3, ZNF323_A, SQSTM1, AFF3, C1orf70, GDF7_A, JSRP1_A, LRRC8D_A, FEV, and MAX.chr8.145104263-145104422;(vii) ZNF323_A, MAX.chr7.104624356-104624730, NDRG2, DIDO1_A, MDFI_B, MAX.chr14.103021656-103021718, MMP23B, SEPT9_B, and STX16_A;(viii) SFMBT2_B, SQSTM1, ZNF323_A, ZNF90, MAX.chr8.145103829-145103992, CYTH2, LRRC8D_A, OBSCN_A, DIDO1_A, MDFI_B, GDF7_A, MAX.chr10.22624479-22624553, JSRP1_A, MAX.chr14.103021656-103021718, EMX2OS, LRRC34, NBPF8, SEPT9_B, EEF1A2, LRRC41_C, VILL, and MPZ_A;(ix)   MAX.chr7:104624386-104624529, EMX2OS, DIDO1_B, andOBSCN_B;(x)   SFMBT2_B, SQSTM1, ZNF323_A, ZNF506, ZNF90,CLDN7, LRRC41_B, MAX.chr7.104624356-104624730, NDRG2, CYP11A1, MAX.chr8.145103829-145103992, CYTH2, LRRC8D_A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, MAX.chr10.130339363-130339534, MDFI_B, DLL4, GDF7_A, MIAT_A, PYCARD, BMP4_B, JSRP1_A, MAX.chr14.103021656-103021718, EMX2, MMP23B, EMX2OS, MAX.chr17.73073716-73073814, NBPF8, SEPT9_B, LOC440925_A, STX16_A, ITPKA, EEF1A2, FEV, LRRC41_C, and NFIC;2026204791   22 Jun 2026(xi) EMX2OS, DIDO1_A, SBNO2, AMIGO3_A, PCOLCE, CLDN7, CYTH2, OBSCN_A, AHSA2, DLL4, EMX2, MAX.chr14.74100620-74100870, LRRC4, PPP2R5C_A, SQSTM1, MAX.chr17.73073716-73073814, CYP11A1, ACOXL_A, and AIM1_B;(xii) EMX2OS, and LRRC34;(xiii) ZNF506, ZNF90, MAX.chr8.145103829-145103992, LRRC8D_A, OBSCN_A, MAX.chr10.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and VILL;(xiv) TRH, MAX.chr7:104624386-104624529, EMX2OS, DIDO1_B, and ST3GAL2_B;(xv) SFMBT2_B, SMTN, ZNF506, ZNF90, CLDN7, LRRC41_B, CYP11A1, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2_B, LRRC8D_A, MAX.chr8.145104263-145104422, OBSCN_A, DIDO1_A, GDF6, DLL4, MAX.chr10.22624479-22624553, PYCARD, BMP4_B, JSRP1_A, MAX.chr14.103021656-103021718, MIAT_B, EMX2OS, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, and VILL;(xvi) EMX2OS, KANK1, C1orf70_B, AMIGO3_A, DIDO1_A, LRRC41_C, NFIC, FKBP11_A, C17orf107_A, SMTN, LRRC41_B, LRRC8D_A, OBSCN_A, MAX.chr7.104624356-104624730, MIAT_B;(xvii) MAX.chr7.104624356-104624730, EMX2OS, and LRRC41_C;(xviii) MAX.chr8.145103829-145103992, CYTH2, LRRC8D_A, OBSCN_A, DIDO1_A, EMX2OS, LRRC41_C, and VILL;(xix) EMX2OS, and LRRC41_D;(xx) SFMBT2_B, SMTN, SQSTM1, ZNF90, CLDN7, LRRC41_B, MAX.chr7.104624356-104624730, CYP11A1, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, LRRC8D_A, MAX.chr8.145104263-145104422, OBSCN_A, GDGF6, DLL4, PYCARD, BMP4_B, JSRP1_A, MIAT_B, KANK1, EMX2OS, NBPF8, LOC440925_A, ITPKA, EEF1A2, FEV, LRRC41_C, NFIC, VILL, MPZ_A;(xxi) MAX.chr10.130339363-130339534, SFMBT2_C, CYTH2, SLC6A3, VILL, EMX2OS, MAX.chr10.22624479-22624553, GDF6, ZNF90,2026204791   22 Jun 2026ZNF506, JSRP1_A, c5orf52, SFMBT2_B, NBPF8, RHBDL1_A, DIDO1_A, KANK1, and GATA2_B;(xxii) MAX.chr8.145103829-145103992, CYTH2, DIDO1_A, MAX.chr10.22624479-22624553, JSRP1_A, SBNO2, NBPF8, and VILL;(xxiii) SFMBT2_B, ZNF90, MAX.chr8.145103829-145103992, CYTH2, MAX.chr8.145104263-145104422, OBSCN_A, MAX.chr10.22624479-22624553, JSRP1_A, EMX2OS, NBPF8, and MPZ_A;(xxiv) SFMBT2_B, SMTN, SQSTM1, ZNF506, ZNF90, CLDN7, LRRC41_B, FKBP11_A, MAX.chr8.145103829-145103992, AHSA2, CYTH2, GATA2_B, LRRC8D_A, MAX.chr8.145104263-145104422, DIDO1_A, GDF6, MAX.chr10.130339363-130339534, DLL4, MAX.chr10.22624479-22624553, MIAT_A, PYCARD, BMP4_B, JSRP1_A, MAX.chr14.103021656-103021718, MIAT_B, KANK1, SBNO2, c5orf52, EMX2O6, LRRC34, NBPF8, LOC440925_A, ITPKA, NFIC, VILL, and MPZ_A;(xxv) TSPYL5, TRH, JAM3, FAM19A5, PTGDR, SFMBT2_E, JSRP1_B, and ARL5C;(xxvi) TSPYL5, MPZ_B, TRH, CNTN4, FAM19A5, GLT1D1, RYR2_F, PTGDR, EMX2OS, MAX.chr10:22624470-22624553, SPDYA_B, SFMBT2_E, and JSRP1_B; and(xxvii) TSPYL5, MPZ_B, TRH, and PTGDR.

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