Establishment method and application of cfDNA fragment map of early-stage bladder cancer

Abstract

The invention discloses an early-stage bladder cancer cfDNA fragment atlas establishment method based on circulating free DNA (cfDNA) and application, and relates to the technical field of cfDNA fragment atlas establishment methods. The invention provides a method for establishing a corresponding cfDNA fragment map aiming at the early bladder cancer, so that the finally obtained cfDNA fragment map can effectively assist in diagnosing the early bladder cancer; the problem that in the prior art, no cfDNA is used for diagnosing the early bladder cancer, and no corresponding method for establishing the cfDNA fragment map of the early bladder cancer exists is solved, diagnosis indexes of the early bladder cancer can be established, and accurate and rapid diagnosis of the bladder cancer is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of methods for establishing cfDNA fragment maps, and specifically to a method for establishing a cfDNA fragment map of early bladder cancer and uses thereof. Background Art

[0002] Bladder cancer is one of the most common urinary malignancies, which brings a heavy burden to patients' families and society. According to statistics, there are about 570,000 new cases of bladder cancer and 210,000 deaths worldwide each year, posing a serious threat to human life and health. Bladder cancer is divided into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). NMIBC accounts for about 75% of bladder cancer and is characterized by frequent recurrence. 10% to 30% of patients will progress to MIBC, with reduced survival and frequent distant metastasis. Despite recent progress in pathological diagnosis and clinical treatment, bladder cancer remains the leading cause of cancer-related morbidity and mortality due to high heterogeneity and recurrence rates and limited treatment options. Currently, the gold standard for bladder cancer diagnosis is still cystoscopy and biopsy of suspicious lesions. However, it is invasive and not the best choice for initial diagnosis. Therefore, non-invasive diagnostic tests with high sensitivity and accurate risk stratification are needed to promote effective diagnostic schemes and reduce intensive treatment caused by delayed diagnosis, thereby reducing the disease and economic burden.

[0003] cfDNA is mainly produced as a byproduct of cell death in normal and / or cancer cells. cfDNA fragments are mainly short molecules with an average length of a single nucleosome, and tend to be more fragmented in internucleosome linkers and open chromatin regions, forming biased, non-random fragments. In addition, tumor-derived DNA fragments (ctDNA) tend to be shorter than those from non-tumor cells, and accumulating evidence suggests that cfDNA fragments can serve as cancer biomarkers at the genome-wide level. Some studies have suggested that the presence of specific genomic regions has preferential tissue-specific or tumor-specific cfDNA fragmentation. Recently, several research groups have thoughtfully characterized the open chromatin landscape in human cancers, which can further infer the cfDNA fragmentation footprint.

[0004] At present, cfDNA fragment omics features have become an emerging technology for early cancer detection. Its features mainly include fragment size, copy number variation, nucleosome imprinting, whole genome cfDNA fragmentation pattern, and fragment end motif features. In recent years, many studies have shown that cfDNA fragment omics features have potential diagnostic value in various cancer types, such as liver cancer, lung cancer, and colorectal cancer. It is reported that Cristiano et al. developed a whole genome cfDNA fragmentation pattern detection technology based on the ratio of the number of short fragments to long fragments of cfDNA. The technology has an AUC of 0.94, a sensitivity range of 57%-99%, and a specificity of 98% in distinguishing patients with 7 types of cancer from healthy individuals. Subsequently, the team successively reported the application of whole genome cfDNA fragmentation features in lung cancer and liver cancer cohorts. These studies have shown that whole genome cfDNA fragmentation patterns can be used as biomarkers for liver cancer or lung cancer detection. In addition, cfDNA fragment motif features represent another type of biomarker for cancer detection. By analyzing cfDNA fragments from patients with hepatocellular carcinoma and non-cancer patients, researchers revealed the preferred end coordinates of tumor-associated cfDNA, which may be attributed to chromatin accessibility, tumor-specific nucleosome positioning, and nuclease activity. Subsequently, based on the preferential pattern of 4bp (base pair) end motifs exhibited by cancer patients, Jiang et al. constructed diagnostic models using support vector machine (SVM) and logistic regression (LR) algorithms to distinguish cancer from non-cancer patients, with an AUC of 0.89 for both models. Recently, Guo et al. constructed a model based on cfDNA breakpoint motifs that can sensitively detect stage I lung adenocarcinoma with an AUC of 0.985, a sensitivity of 98.0%, and a specificity of 94.7%. These studies further indicate that cfDNA fragment omics features play an important role in cancer detection. Given that detection methods based on single cfDNA features may have insufficient detection capabilities, especially for early-stage cancer patients, recently, multidimensional prediction models combining multiple fragment omics and machine learning methods have improved their detection capabilities for specific cancer types. However, to date, the existing technologies for early diagnosis of bladder cancer have not been based on the terminal features of cfDNA sequencing data.

[0005] Patent—CN117373541A is based on the methylation data of plasma cfDNA. By calculating the difference interval Score value corresponding to each cancer type, the cancer tissue origin of cfDNA in plasma is identified, thereby providing a method for tracing the origin of cancer tissue. The invention is based on cfDNA methylation data, and this patent is based on the end characteristics of cfDNA fragments. The diseases studied by the two are also inconsistent. This study focuses on bladder cancer.

[0006] Patent-CN115376616A provides a multi-classification method and device based on cfDNA multi-omics. The patent established a random forest model based on 780 cancer plasma sample data (including breast cancer, colorectal cancer, esophageal cancer, liver cancer, non-small cell lung cancer, pancreatic cancer, and gastric cancer). The average accuracy of the model for all tumors is 0.638. The study did not include bladder cancer, and the accuracy is also lower than this patent.

[0007] Patent CN112086129A discloses a method and system for predicting cfDNA in tumor tissue. The invention develops a pan-cancer diagnostic model based on cfDNA terminal features and Kmer frequency features, with a sensitivity of 90.32% and a specificity of 89.47%. However, the invention does not establish diagnostic indicators for bladder cancer.

[0008] In summary, the current invention does not have a method for diagnosing bladder cancer based on the terminal features of cfDNA sequencing data, and no research has yet been conducted on chr22q_126_130bp_FSD, chr19q_126_130bp_FSD, chr19q_116_120bp_FSD, chr17p_126_130bp_FSD, TCATCT_B6bp, TCATCT_M6bp, TATGTC_M6bp, TATGTC_B6bp, GTTGCG_M6bp, GTTGCG_B6bp, CGGGAA_M6bp, CGGGAA_B6bp, GGAAGC _M6bp, GGACGT_M6bp, GGAAGC_B6bp, GCCAAT_M6bp, CGACAG_M6bp, chr22q_171_175bp_FSD, AGCGGA_B6bp, GGACGT_B6bp, CGACAG_B6bp, GCCAAT_B6bp, ACCAGT_M6bp, CGAA_M4bp, CGACTG_B6bp, CGACTG_M6bp, CCCACG_M6bp, CGAA_B4bp, ACCAGT_B6bp, AGCGGA_M6bp are used as joint markers for diagnosing bladder cancer. Based on this, the present invention is specially proposed. Summary of the invention

[0009] In order to solve the above technical problems, the present invention provides a cfDNA fragment map of early bladder cancer and an establishment method.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect, the present invention proposes a combined circulating cell-free DNA (cfDNA) marker, including: chr22q_126_130bp_FSD, chr19q_126_130bp_FSD, chr19q_116_120bp_FSD, chr17p_126_130bp_FSD, TCATCT_B6bp, TCATCT_M6bp, TATGTC_M6bp, TATGTC_B6bp, GTTGCG_M6bp, GTTGCG_B6bp, CGGGAA_M6bp, CG GGAA_B6bp, GGAAGC_M6bp, GGACGT_M6bp, GGAAGC_B6bp, GCCAAT_M6bp, CGACAG_M6bp, chr22q_171_175bp_FSD, AGCGGA_B6bp, GGACGT_B6bp, CGACAG_B6bp, GCCAAT_B6bp, ACCAGT_M6bp, CGAA_M4bp, CGACTG_B6bp, CGACTG_M6bp, CCCACG_M6bp, CGAA_B4bp, ACCAGT_B6bp, AGCGGA_M6bp, This combined marker can establish diagnostic indicators for early bladder cancer and make accurate and rapid diagnosis of bladder cancer.

[0011] In a second aspect, the present invention proposes a method for establishing a cfDNA fragment profile for early bladder cancer, which specifically comprises the following steps: BZ1. Collect samples from case group members and healthy control group members who meet the collection criteria; BZ2, extraction and quantification of cfDNA in plasma, separation and quantitative extraction of cfDNA from sample plasma; BZ3, end repair, fill in the ends of cfDNA separated from plasma, phosphorylate the 5' end and add a dA tail to the 3' end; BZ4, connector connection, connect the connector to the end of the product repaired at the end; BZ5, library amplification, performing library amplification operation on the cfDNA treated with BZ4, so that the cfDNA sequence is amplified in the container; BZ6, library quality control, quality testing of cfDNA after library amplification to ensure that the library is of qualified quality; BZ7, single-strand circularization and sequencing, the library that passed the quality control was subjected to WGS with an average coverage of 1x on the sequencer, using the paired-end sequencing mode, with 100 bp at each end; For BZ8 and lcWGS data analysis, the original sequencing data rawreads were stored as fastq files, and fastp software was used to perform quality control on the rawreads; BZ9, whole genome fragmentation characteristics, calculate the cfDNA fragment size ratio Fragmentsizeratio in each 5M region, Fragmentsizeratio, FSR, that is, the ratio of the number of short fragments to the number of long fragments; BZ10, terminal motifs, and breakpoint motifs, calculate the frequency of each terminal motif and breakpoint motif, and normalize the frequency of each terminal motif and breakpoint motif according to the total number of all terminal motifs or breakpoint motifs appearing at the ends of cfDNA fragments in each plasma sample; BZ11, terminal motif diversity score, quantifies the diversity of terminal motifs in cfDNA fragments by using a single indicator. Based on the frequency of each terminal motif type, the normalized Shannon entropy algorithm is used to calculate the terminal motif diversity score (MDS) of cfDNA fragments in each plasma sample. BZ12, classification model construction, using the whole genome cfDNA fragmentation features including FSR and FSD and motif features including 4bpendmotifs, 6bpendmotifs, 4bpbreakpointmotifs and 6bpbreakpointmotifs, to build a cancer early diagnosis prediction model in each cohort.

[0012] Preferably, the BZ2 specifically includes: BZ2-1. Plasma pretreatment: Take out the plasma sample stored in a -80℃ refrigerator and freeze-thaw it in a 37℃ water bath. After the plasma is completely thawed, centrifuge it at 16000g for 10 minutes and transfer the supernatant to a new centrifuge tube. BZ2-2, lysis: add 10 μL proteinase K solution to 500 μL plasma, vortex for 30 seconds to mix the proteinase K solution and plasma thoroughly; then add 25 μL 20% SDS, vortex for 30 seconds; BZ2-3, Incubation: Incubate in a 60℃ water bath for 30 minutes. During the incubation period, mix by inverting every 5 minutes; BZ2-4, ice bath: After the incubation in a 60°C water bath, place the mixture on ice for 5 min; BZ2-5, enrichment: After the ice bath, place the sample at room temperature. After the sample temperature returns to room temperature, add 500 μL MagMAX cell-free DNA lysis / binding solution and 10 μL MagMAX™ cfDNA magnetic beads, and then oscillate the mixture on a vortex oscillator at medium speed for 10 minutes. After instantaneous separation, place it on a magnetic stand for 5 minutes until the solution is clear; BZ2-6, washing: remove the supernatant, add 500 μL MagMAX™ cfDNA washing solution, mix the solution thoroughly. Place it in the magnetic stand again until it is clear, then discard the supernatant. Wash twice with 500 μL 80% ethanol; BZ2-7, elution: After the sample is dried on a magnetic rack at room temperature, 30 μL of elution buffer is finally pipetted into it and the resulting mixture is placed on a magnetic rack for 2 minutes until the solution is clear. Aspirate the supernatant and store the cfDNA; BZ2-8, concentration determination: The quantification was performed using the Qubit ds DNA HS detection kit, i.e., the detection working solution in the kit was mixed with the DNA sample to be tested, and then the reading was performed using the Qubit 4.0 fluorescence quantifier to obtain the cfDNA concentration in these plasma samples; BZ2-9, determination of fragment length: the Bioptic QSEP-100 fully automatic nucleic acid protein analysis system was used to detect the distribution range of DNA fragment length. Theoretically, the peak value of cfDNA fragments was around 167 bp; BZ2-10, Storage: Store the isolated cfDNA at -20°C until library construction.

[0013] Preferably, the BZ3 specifically includes: BZ3-1, thaw EndPrepMix4 and mix thoroughly by inversion, and prepare the mixed solution system in a sterilized PCR tube: Input DNA 30ul, EndPrepMix4 15ul, ddH2OTo 65μl; BZ3-2, use a pipette to gently pipette and mix, ensuring that it is not shaken, and briefly centrifuge to collect the reaction solution at the bottom of the tube; BZ3-3. Place the PCR tube in the PCR instrument and carry out the reaction: heat cover at 105℃On, 20℃ for 15 min, 65℃ for 15 min, maintain at 4℃.

[0014] Preferably, the BZ4 specifically comprises: BZ4-1, dilute Adapter to appropriate concentration according to input DNA amount: BZ4-2, thaw RapidLigation buffer, mix thoroughly by inversion, and place on ice for later use; BZ4-3. Prepare the mixed solution system in the PCR tube of the end repair step: BZ4-4, use a pipette to gently blow and mix, do not oscillate to mix, and centrifuge briefly to collect the reaction solution at the bottom of the tube; BZ4-5, place the PCR tube in the PCR instrument and perform the reaction; BZ4-6, use VAHTS DNA Clean Beads to purify the reaction product: Preferably, the BZ4-6 specifically comprises: a) After the magnetic beads are equilibrated to room temperature, vortex and mix the VAHTS DNA Clean Beads; b) Pipette 60 μl of VAHTS DNA Clean Beads into 100 μl of Adapter Ligation product and vortex or pipette gently 10 times to mix thoroughly; c) Incubate at room temperature for 5 minutes; d) Centrifuge the PCR tube briefly and place it on a magnetic rack to separate the beads and liquid. After the solution has clarified for 5 minutes, carefully remove the supernatant. e) Keep the PCR tube in the magnetic rack, add 200 μl of freshly prepared 80% ethanol to rinse the magnetic beads, incubate at room temperature for 30 seconds, and carefully remove the supernatant; f) Repeat step e, rinsing twice in total; g) The PCR tube is always placed in the magnetic rack, and the magnetic beads are air-dried for 5-10 minutes with the lid open until no ethanol remains; h) Remove the PCR tube from the magnetic rack and elute: add 22.5μl of elution buffer, vortex or use a pipette to mix thoroughly, let it stand at room temperature for 2 minutes, centrifuge the PCR tube briefly and place it on the magnetic rack to stand. After the solution has clarified for 5 minutes, carefully transfer 20μl of the supernatant to a new EP tube, and do not touch the magnetic beads.

[0015] Preferably, the eluent is 10 mM TrisHCl, pH 8.0-8.5.

[0016] Preferably, the sequencer used in BZ7 is the BGISEQ-2000RS sequencer.

[0017] In a third aspect, the present invention also proposes a method for establishing a cfDNA fragment map of early bladder cancer and the application of combined circulating cell-free DNA (cfDNA) markers in the diagnosis of early bladder cancer.

[0018] Furthermore, the diagnostic reagent or diagnostic kit prepared by the above application is used for the diagnosis of early bladder cancer.

[0019] Beneficial Effects The present invention provides a method for establishing a cfDNA fragment profile for early bladder cancer. Compared with the prior art, it has the following beneficial effects: 1. The present invention provides a combined circulating cell-free DNA (cfDNA) marker, which can establish a diagnostic index for early bladder cancer by combining 30 specific markers, and perform accurate and rapid diagnosis of bladder cancer.

[0020] 2. The present invention provides a method for establishing a cfDNA fragment map for early bladder cancer. The method provides a method for establishing a corresponding cfDNA fragment map for early bladder cancer, so that the cfDNA fragment map finally obtained can effectively assist in the diagnosis of early bladder cancer, thereby solving the problem in the prior art that there is no corresponding method for establishing a cfDNA fragment map for early bladder cancer due to the lack of diagnosis of early bladder cancer using cfDNA. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation or the prior art description. Obviously, the drawings in the following description show some implementations of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a schematic diagram of the present invention; Figure 2 Schematic diagram of the frequency of the plasma cfDNA fragment terminal motif CCCA of the present invention; Figure 3 is the ROC curve diagram of the MDS of the present invention; Figure 4 is the ROC curve diagram of each model of the present invention in BLCA VS Healthy group; Figure 5 Schematic diagram of the prediction performance of each model of the present invention in the BLCA VS Healthy data set. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Example 1 See also Figures 1 to 5This embodiment provides a technical solution: a method for establishing a cfDNA fragment map of early bladder cancer, specifically comprising: BZ1. Research sample collection This study recruited bladder cancer patients and healthy controls. The inclusion criteria for the case group were as follows: (1) aged over 18 years; (2) patients who were diagnosed with the disease for the first time and had not received any tumor treatment, such as immunotherapy, radiotherapy, chemotherapy, anti-tumor drug treatment, etc.; (3) primary tumors were pathologically confirmed to be BLCA, with pathological stage of T1 or T2, no lymph node and distant metastasis, and surgical resection; (4) no abnormal liver and kidney function; (5) informed consent; Exclusion criteria were as follows: (1) patients with other malignant tumors, such as lung cancer, liver cancer, etc., or with a history of malignant tumors; (2) patients with active infectious diseases, such as active tuberculosis, AIDS, etc.; (3) patients with a history of blood transfusion within 3 months; (4) patients with mental illness or other reasons who were unable to understand the study; Inclusion criteria for the healthy control group were as follows: (1) patients aged over 18 years; (2) patients in good health; (3) patients with informed consent; Exclusion criteria for the healthy control group were as follows: (1) patients with a history of blood transfusion within 3 months; (2) patients who were unable to understand the study for various reasons.

[0025] BZ2, plasma cfDNA extraction and quantification (1) Plasma pretreatment: Take out the plasma sample stored in a -80℃ refrigerator and freeze-thaw it in a 37℃ water bath. After the plasma is completely thawed, centrifuge it at 16000g for 10 min and transfer the supernatant to a new centrifuge tube. (2) Lysis: Add 10μL of proteinase K solution to 500μL of plasma and vortex for 30 seconds to mix the proteinase K solution and plasma thoroughly. Then add 25μL of 20% SDS and vortex for 30 seconds. (3) Incubation: Incubate in a 60℃ water bath for 30 minutes. During the incubation period, Invert and mix every 5 minutes; (4) Ice bath: After the incubation in a 60°C water bath, place the mixture on ice for 5 minutes; (5) Enrichment: After the ice bath, place the sample at room temperature. After the sample temperature returns to room temperature, add a solution and magnetic bead mixture consisting of 500 μL MagMAX cell-free DNA lysis / binding solution and 10 μL MagMAX™ cfDNA magnetic beads, and then vortex the mixture at medium speed on a vortex oscillator for 10 minutes. After instantaneous separation, place it on a magnetic rack for 5 minutes until the solution is clear; (6) Washing: remove the supernatant, add 500 μL MagMAX™ cfDNA washing solution, and mix the solution thoroughly; place it on the magnetic rack again until it is clear, and then discard the supernatant; wash twice with 500 μL 80% ethanol; (7) Elution: after the sample is dried on the magnetic rack at room temperature, finally transfer 30 μL elution buffer into it, and place the resulting mixture on the magnetic rack for 2 minutes until the solution is clear; aspirate the supernatant and store the cfDNA; (8) Determine the concentration: use the Qubit dsDNA HS detection kit for quantification, that is, mix the detection working solution in the kit with the DNA sample to be tested, and then use the Qubit4.0 fluorescence quantification instrument to read, so as to obtain the cfDNA concentration in these plasma samples; (9) Determine the fragment length: use the Bioptic QSEP-100 fully automatic nucleic acid protein analysis system to detect the distribution range of DNA fragment length. In theory, the peak value of cfDNA fragment is around 167 bp; (10) Storage: store the separated cfDNA at -20°C until library construction; BZ3, end repair, this step will be separated from the plasma cfDNA, input DNA, end filling, and phosphorylation at the 5' end and dA tail added to the 3' end, including the following steps: (1) Thaw EndPrepMix4 and mix thoroughly by inversion. Prepare the mixed solution in a sterilized PCR tube: Input DNA 30ul; EndPrepMix4 15ul; ddH2OTo 65μl; (2) Use a pipette to gently blow and mix, do not oscillate to mix, and briefly centrifuge to collect the reaction solution at the bottom of the tube. (3) Place the PCR tube in a PCR instrument and perform the reaction: heat cover at 105℃On; 20℃ for 15 minutes; 65℃ for 15 minutes; maintain at 4℃.

[0026] BZ4, Adapter Ligation, this step will connect the adapter to the end of the repaired product, including the following steps: (1) Dilute the Adapter to an appropriate concentration based on the amount of input DNA; (2) Thaw the RapidLigation buffer, invert and mix thoroughly, and place on ice for later use; (3) Prepare a mixed solution system in the PCR tube for the end repair step; (4) Use a pipette to gently pipette to mix (do not oscillate to mix), and briefly centrifuge to collect the reaction solution at the bottom of the tube; (5) Place the PCR tube in a PCR instrument and perform the reaction; (6) Purify the reaction product using VAHTS DNA Clean Beads; a) After the magnetic beads are equilibrated to room temperature, vortex and mix the VAHTS DNA Clean Beads; b) Pipette 60 μl of VAHTS DNA Clean Beads into 100 μl of Adapter Ligation product and vortex or pipette gently 10 times to mix thoroughly; c) Incubate at room temperature for 5 minutes; d) Briefly centrifuge the PCR tube and place it on a magnetic stand to separate the beads and liquid. After the solution is clear for 5 minutes, carefully remove the supernatant. e) Keep the PCR tube in the magnetic rack, add 200 μl of freshly prepared 80% ethanol to rinse the magnetic beads, incubate at room temperature for 30 seconds, and carefully remove the supernatant; f) Repeat step e, rinsing twice in total; g) The PCR tube is always placed in the magnetic rack, and the magnetic beads are air-dried for 5-10 minutes with the lid open until no ethanol remains; h) Take the PCR tube out of the magnetic rack and elute: add 22.5μl of elution buffer 10mM TrisHCl, pH 8.0-8.5, vortex or use a pipette to gently blow to mix thoroughly, let it stand at room temperature for 2 minutes, centrifuge the PCR tube briefly and place it on the magnetic rack to stand. After the solution is clear for 5 minutes, carefully transfer 20μl of the supernatant to a new EP tube, and do not touch the magnetic beads; BZ5, library amplification, (1) thaw PCR Primer Mix 3 and VAHTS HiFi Amplification Mix, mix by inversion, and prepare the following mixed solution system in a sterile PCR tube; (2) Use a pipette to gently blow and mix (do not oscillate to mix), and briefly centrifuge to collect the reaction solution at the bottom of the tube; (3) Place the PCR tube in a PCR instrument and perform the following reaction; (4) Use VAHTS DNA Clean Beads to purify the reaction product: The specific operation is the same as the purification step in step (2) above. Note: The sample here can be stably stored at 4°C for one week. For long-term storage, place it at -20°C to avoid unnecessary repeated freezing and thawing.

[0027] BZ6, library quality control, (1) library concentration determination: Qubit4.0 fluorescence quantification instrument was used to determine the concentration of the constructed library; (2) library length distribution detection: DNA fragment length distribution was detected by BiopticQSEP-100 fully automatic nucleic acid protein analysis system. The prepared cfDNA library was stored at -20℃ until sequencing.

[0028] BZ7, single-strand circularization and sequencing, the library that passed the quality control was subjected to WGS with an average coverage of 1x on the BGI MGISEQ-2000RS sequencer, using the paired-end sequencing mode, with 100bp at each end. The specific steps are as follows: (1) Denaturing, cyclizing and digesting the double-stranded DNA library to obtain single-stranded circular DNA. The single-stranded DNA is amplified by rolling circle amplification (RCA) to obtain DNA nanoballs (DNBs). (2) Load the prepared DNA onto the Patterned Array microarray chip and sequence it using combined probe-anchored polymerization technology cPAS (Combinatorial Probe-Anchor Synthesis); (3) After the sequencing primer anchor molecule and the fluorescent probe are polymerized on the DNA nanoball, the optical signal is collected, read and identified using a high-resolution imaging system to obtain the single base sequence information, and then the next cycle is carried out to obtain the next base sequence information. Finally, after multiple cycles, the original sequencing data is obtained.

[0029] BZ8 and lcWGS data analysis: The raw sequencing data rawreads are stored as fastq files, and fastp software is used to perform quality control on the rawreads according to the following steps: (1) Reads with a base content of more than 10% of N (N means the base information cannot be determined) are eliminated; (2) Eliminate the linker sequence; (3) Remove reads in which the number of low-quality bases (quality value Q≤15) in the single-end reads accounts for more than 40% of the total read length. After data quality control, clean and valid data (cleanreads) are obtained. FastQC (v.0.11.9) software is used to evaluate the quality of cleanreads. Subsequently, BWA software is used to align cleanreads with the human reference genome hg19; Samtools software is used to calculate the mapping rate (mappingrate), duplication rate (duplicaterate) and genome coverage (genomecoverage); and Sambamba software is used to remove duplicate reads and reads with a mapping rate of <30% during the sequencing process; The fragmentation characteristics of the BZ9 whole genome divided the hg19 autosome into 572 adjacent, non-overlapping regions of 5M in size, and eliminated the previously reported low mapping regions and Duke blacklisted regions. Finally, the hg19 autosome had a total of 473 adjacent, non-overlapping regions. The size of the inserted fragment was calculated based on the double-end reads aligned to the reference genome, which is the cfDNA fragment length. According to the length range of cfDNA fragments in the peripheral blood of all research subjects, fragments between 100-150bp were defined as short fragments; fragments between 151-220bp were defined as long fragments. The cfDNA fragment size ratio (Fragmentsizeratio, FSR) in each 5M region was calculated, that is, the ratio of the number of short fragments to the number of long fragments; BZ10, end motif and breakpoint motif. During the end repair process of library preparation, the 3' end of the cfDNA fragment was modified, that is, the protruding 3' end was digested or filled in to make it complementary to the 5' end; all DNA molecules were repaired and blunted, retaining the original 5' end of each pair of complementary DNA fragments; each cfDNA fragment was analyzed using double-end low-throughput whole-genome sequencing technology; when the cfDNA sequencing reads were aligned with the human reference genome, the first 4 nucleotide sequences from the 5' end of the cfDNA fragment to the 3' end were defined as 4bp end motifs (4bpendmotis); the first 6 nucleotide sequences from the 5' end of the cfDNA fragment to the 3' end were 6bp end motifs (6bpendmotis); there are 256 (4*4*4*4) types of 4bp end motifs, 6bp end motifs There are 4096 (4*4*4*4*4*4*4) types of motifs; the position of the first nucleotide at the 5´ end of cfDNA corresponding to the human reference genome is the cleavage site of the cfDNA fragment, and the 2 nucleotide sequences upstream and downstream of the cleavage site are defined as 4bp breakpoint motifs; the 3 nucleotide sequences upstream and downstream of the cleavage site are defined as 6bp breakpoint motifs; there are 256 (4*4*4*4) types of 4bp breakpoint motifs, and there are 4096 (4*4*4*4*4*4*4) types of 6bp breakpoint motifs; the frequency of each terminal motif and breakpoint motif was calculated, and the frequency of each terminal motif and breakpoint motif was normalized according to the total number of all terminal motifs or breakpoint motifs appearing at the end of the cfDNA fragments of each plasma sample; BZ11, terminal motif diversity score. In order to quantify the diversity of terminal motifs in cfDNA fragments with a single indicator, we calculated the terminal motif diversity score (Motifdiversityscore, MDS) of each plasma sample cfDNA fragment based on the frequency of each terminal motif type using the normalized Shannon entropy algorithm. The specific calculation formula of MDS is as follows: ; Where Pi represents the frequency of a specific terminal motif. The theoretical value range of MDS is 0 to 1. The closer the MDS value is to 1, the more types of terminal motifs there are in plasma cfDNA. On the contrary, the closer it is to 0, the fewer types of terminal motifs there are in plasma cfDNA.

[0030] BZ12, classification model construction, using whole genome cfDNA fragmentation features (including FSR and FSD) and motif features (including 4bpendmotifs, 6bpendmotifs, 4bpbreakpointmotifs and 6bpbreakpointmotifs), early cancer diagnosis prediction models were constructed in each cohort. This study used the following five machine learning methods to predict urinary malignancies, namely LR, RF, SVC, xgboost, and stacking algorithms. The subjects in each case control group were randomly divided into training sets and test sets in a ratio of 7:3. In the training set, a binary classification model was constructed based on the ten-fold cross-validation method; in order to test the robustness of the model, we validated it in an independent test set and performed a ten-fold cross-validation of random sample selection on the entire data set. The accuracy of the model was evaluated using AUC, sensitivity, specificity, positive predictive values ​​(PPV), negative predictive values ​​(NPV), and accuracy.

[0031] Statistical methods used in the above steps: The clinical information of the subjects was uniformly recorded in Excel 2020 software and analyzed in SPSS 26.0 software. After normality test, continuous variables that conformed to normal distribution were expressed as mean and standard deviation (X±S), and the comparison between the two groups was performed by t test; continuous variables that did not conform to normal distribution were expressed as median and interquartile range (P25, P75), and the comparison between the two groups was performed by Mann-Whitney U test. Quantitative data were expressed as number of cases (n) and percentage (%), and the comparison between the two groups was performed by chi-square test. R software (version 4.2.1) and "limma" package were used to analyze the difference in terminal motif and breakpoint motif frequencies between cancer patients and healthy controls. The threshold was set to |Log2FC|≥2, and the adjusted P value was <0.05. Heatmaps, box plots, and ROC curves were drawn based on "pheatmap", "ggplot2", "ggpubr", "ggsci", "PROC" packages of R software. Python software (version 13.9.13) was used to construct the classification model and calculate the sensitivity, specificity, accuracy, PPV, NPV and other indicators. This study used α = 0.05 as the test level, and a two-sided test P < 0.05 indicated a statistically significant difference.

[0032] In summary, the present invention provides a combined circulating cell-free DNA (cfDNA) marker, which is obtained by combining chr22q_126_130bp_FSD, chr19q_126_130bp_FSD, chr19q_116_120bp_FSD, chr17p_126_130bp_FSD, TCATCT_B6bp, TCATCT_M6bp, TATGTC_M6bp, TATGTC_B6bp, GTTGCG_M6bp, GTTGCG_B6bp, CGGGAA_M6bp, CGGGAA_B6bp, GGAAGC_M6bp, GGACGT_M6bp, GGAAGC_B6bp, GCC AAT_M6bp, CGACAG_M6bp, chr22q_171_175bp_FSD, AGCGGA_B6bp, GGACGT_B6bp, CGACAG_B6bp, GCCAAT_B6bp, ACCAGT_M6bp, CGAA_M4bp, CGACTG_B6bp, CGACTG_M6bp, CCCACG_M6bp, CGAA_B4bp, ACCAGT_B6bp, and AGCGGA_M6bp can establish diagnostic indicators for early bladder cancer and accurately and quickly diagnose bladder cancer. The present invention also provides the use of an early bladder cancer cfDNA fragment map established by a method for establishing an early bladder cancer cfDNA fragment map for the diagnosis of early bladder cancer, which can accurately and quickly diagnose early bladder cancer.

[0033] Example 2 See also Figures 1 to 5 This embodiment provides a technical solution: a method for establishing a cfDNA fragment map of early bladder cancer, specifically comprising: (1) Sample collection: 43 consecutive BLCA patients and 64 healthy controls were collected; all bladder cancer patients were in the early stage (T1-T2 stage) and were clinically suitable for surgical resection. Before enrollment, the researchers informed all participants of the purpose and procedures of the study in detail, obtained the consent of the patients and / or guardians, and signed the informed consent form. 5 mL of peripheral blood was collected from the subjects in the morning on an empty stomach using EDTA anticoagulated vacuum blood collection tubes. After blood was drawn, the tubes were gently inverted 8-10 times to mix thoroughly and avoid coagulation, and then placed vertically. ; Subsequently, the members of the experimental group uniformly transported the samples to the laboratory at low temperature for blood sample pretreatment. The separation and storage of plasma samples were completed within 2 hours after blood collection. The blood samples were centrifuged at 1500g for 10 minutes at 4°C. After centrifugation, the supernatant (plasma) was dispensed into cryopreservation tubes in 500μL forms using a pipette. Precautions during the plasma dispensing process included prohibiting the aspiration of the middle white film layer. Sample dispensing was completed on a clean bench. Finally, the dispensed plasma was stored in a -80°C refrigerator for subsequent use. (2) Plasma cfDNA extraction and quantification: MagMAX Cell-free DNA isolation kit (Thermo Fisher Scientific, USA) was used to extract cfDNA in strict accordance with the manufacturer's instructions; see above for detailed steps; (3) Library construction and quality control: After the cfDNA quality control was qualified, the VAHTS Universal DNA Library Prep Kit for Illumina V3 (Novogene Biotech, China) was used to perform end repair, phosphorylation, "A" addition, and sequencer-specific adapters on each cfDNA sample in strict accordance with the manufacturer's instructions, and the cfDNA WGS library was constructed by PCR enrichment; the library construction process is described above; (4) Sequencing Libraries that passed quality control were subjected to WGS with an average coverage of 1× on the BGISEQ-2000RS sequencer, using a paired-end sequencing mode with 100 bp measured at each end; (5) Machine Learning Algorithms Use LR, SVM, RF, XGBoost and Stacking algorithms to build models; these machine learning algorithms are currently popular classification learning algorithms with high accuracy and robustness; the LR model is the simplest classification model. In the field of machine learning, its basic process is: facing a classification problem, first establish a cost function containing parameters, then solve the parameters of the optimal model through optimization methods, and finally test the optimal model through the test set; SVM is a distance-based binary classification model, which mainly finds the linear classifier with the largest margin in the feature space; SVM has strong ability to handle nonlinear models, and the kernel technique in SVM can expand the number of features through polynomials of different orders, interaction terms or other derivatives; due to the distance characteristics of the SVM algorithm, its model is easily affected by the dimensions of each feature. Therefore, when using the SVM method, it is necessary to standardize the data to eliminate the impact of differences in feature dimensions on the model; RF is a set of decision trees , a mainstream ensemble method for classification and regression; each tree of RF randomly samples a subset of the training data in a process called bootstrap clustering; the model fits these smaller data sets and summarizes the prediction results; by sampling with replacement, several instances of the same data can be reused, as a result, the trees are not only trained on different data sets, but also use different features to make decisions; XGBoost is a scalable machine learning system based on a tree boosting algorithm, also known as the XGB algorithm. During the training process, the algorithm generates an integrated tree model by inputting samples containing a feature set and a target value Y. The model will score each sample as the predicted value of y, where Y can be either numerical or categorical; Stacking is an integrated learning method; the strategy is to combine the advantages of multiple models and require each of its sub-models to have good predictive ability. For example, the Stacking model built based on five models, the modeling process can be simply understood as putting the data set on five models separately; (6) Model evaluation method In the field of machine learning, if a model performs well in the training set but poorly in the test set, the model is considered to be overfitting. Two validation methods are used for model evaluation: one is to evaluate model performance by performing 10-fold cross validation on the training set, and the other is to evaluate model performance on an independent test set. The machine learning library scikit-learn is used to calculate the confusion matrix, draw the ROC curve, and calculate the AUC. Usually, the AUC value is used to determine the diagnostic efficacy of the model. The ROC curve can summarize the relationship between the sensitivity and specificity of the model for the prediction of the result. The 45-degree diagonal line from the lower left corner to the upper right corner is a random prediction. The farther the ROC curve is from the diagonal and the larger the AUC is, the better the prediction effect of the model on the result is. The AUC value is usually between 0.5 and 1. When the AUC value is between 0.5 and 0.7, the prediction ability of the model is considered to be low. When AUC=0.5, it means that the diagnostic model does not work at all and has no diagnostic value. When the AUC value is between 0.7 and 0.9, the prediction ability of the model is considered to be strong. When the AUC value is greater than 0.9, the prediction ability of the model is considered to be excellent. In addition, for the classification model , sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy are the most commonly used indicators to evaluate model performance; sensitivity is also called recall rate; positive predictive value is also called precision rate; based on the predicted true positive (TP), false positive (FP), true negative (TN) and false negative False negative (FN), calculate sensitivity, specificity, PPV, NPV, accuracy and F1 score; the specific calculation formula is as follows: sensitivity = TP / (TP+FN) × 100%; specificity = TN / (TN+FP) × 100%; positive predictive value = TP / (TP+FP) × 100%; negative predictive value = TN / (TN+FN) × 100%; accuracy = (TP+TN) / (TP+FP+TN+FN) × 100%; F1 score = 2 × (precision × recall) / (precision + recall) × 100%; (7) Diagnostic model The features included in the final diagnostic model included chr22q_126_130bp_FSD, chr19q_126_130bp_FSD, chr19q_116_120bp_FSD, chr17p_126_130bp_FSD, TCATCT_B6bp, TCATCT_M6bp, TATGTC_M6bp, TATGTC_B6bp, GTTGCG_M6bp, GTTGCG_B6bp, CGGGAA_M6bp, CGGGAA_B6bp, GGAAGC_M6bp, GGAC GT_M6bp, GGAAGC_B6bp, GCCAAT_M6bp, CGACAG_M6bp, chr22q_171_175bp_FSD, AGCGGA_B6bp, GGACGT_B6bp, CGACAG_B6bp, GCCAAT_B6bp, ACCAGT_M6bp, CGAA_M4bp, CGACTG_B6bp, CGACTG_M6bp, CCCACG_M6bp, CGAA_B4bp, ACCAGT_B6bp, AGCGGA_M6bp; In the BLCA VS Healthy training set, we constructed five classification models using different algorithms and ten-fold cross validation. The results are shown in the figure. Figure 4 A and Figure 5 As shown in the figure; in the training set, the optimal models based on RF, SVM, XGBoost and Stacking algorithms performed consistently and showed excellent predictive efficiency; their AUC values ​​were all 100.00%; when the sensitivity reached 91.18%, its specificity, accuracy and F1 score were 100.00%, 96.47% and 95.38% respectively; the optimal model of LR performed relatively poorly, with an AUC value of 82.2%, and when the sensitivity reached 91.18%, its specificity was only 60.78%, and the PPV, NPV and F1 scores were 60.78%, 91.18% and 72.94% respectively; the model was evaluated in the BLCA VS Healthy test set, and the results are shown in the figure. Figure 4 B and Figure 5As shown in the figure; in the classification problem of BLCA and healthy control samples, the SVM and Stacking models performed consistently and showed excellent predictive ability; their AUC values, sensitivity, specificity, PPV, NPV, accuracy and F1 scores all reached 100.00%; according to the AUC value to evaluate the model prediction efficiency, the RF, XGBoost and LR models were second only to the SVM and Stacking models, with AUC values ​​of 95.8%, 92.7% and 88.5%, respectively; according to the F1 score to evaluate the model prediction efficiency, the LR model performed better, with an F1 score, sensitivity and specificity of 85.71%, 100.00% and 76.92%, respectively; in short, the SVM and Stacking models based on the cfDNA fragment omics characteristics can effectively identify early BLCA patients.

[0034] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0035] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for establishing a cfDNA fragment profile for early bladder cancer, characterized in that: The specific steps include: BZ1. Collect samples from case group members and healthy control group members who meet the collection criteria; BZ2, extraction and quantification of cfDNA in plasma, separation and quantitative extraction of cfDNA from sample plasma; BZ3, end repair, fills the ends of cfDNA separated from plasma, phosphorylates the 5' end and adds a dA tail to the 3' end; BZ4, connector connection, connect the connector to the end of the product repaired at the end; BZ5, library amplification, performing library amplification operation on the cfDNA treated with BZ4, so that the cfDNA sequence is amplified in the container; BZ6, library quality control, quality testing of cfDNA after library amplification to ensure that the library is of qualified quality; BZ7, single-strand circularization and sequencing, the library that passed the quality control was subjected to WGS with an average coverage of 1x on the sequencer, using the paired-end sequencing mode, with 100 bp at each end; For BZ8 and lcWGS data analysis, the original sequencing data rawreads were stored as fastq files, and fastp software was used to perform quality control on the rawreads; BZ9, whole genome fragmentation characteristics, calculate the cfDNA fragment size ratio Fragmentsizeratio in each 5M region, Fragmentsizeratio, FSR, that is, the ratio of the number of short fragments to the number of long fragments; BZ10, terminal motifs, and breakpoint motifs, calculate the frequency of each terminal motif and breakpoint motif, and normalize the frequency of each terminal motif and breakpoint motif according to the total number of all terminal motifs or breakpoint motifs appearing at the ends of cfDNA fragments in each plasma sample; BZ11, terminal motif diversity score, quantifies the diversity of terminal motifs in cfDNA fragments by using a single indicator. Based on the frequency of each terminal motif type, the normalized Shannon entropy algorithm is used to calculate the terminal motif diversity score (MDS) of cfDNA fragments in each plasma sample. BZ12, classification model construction, using the whole genome cfDNA fragmentation features including FSR and FSD and motif features including 4bpendmotifs, 6bpendmotifs, 4bpbreakpointmotifs and 6bpbreakpointmotifs, to build a cancer early diagnosis prediction model in each cohort.

2. The method for establishing a cfDNA fragment map of early bladder cancer according to claim 1, characterized in that: The BZ2 specifically comprises the following steps: BZ2-1. Plasma pretreatment: Take out the plasma sample stored in a -80℃ refrigerator and freeze-thaw it in a 37℃ water bath. After the plasma is completely thawed, centrifuge it at 16000g for 10 minutes and transfer the supernatant to a new centrifuge tube. BZ2-2, lysis: add 10 μL proteinase K solution to 500 μL plasma, vortex for 30 seconds to mix the proteinase K solution and plasma thoroughly; then add 25 μL 20% SDS, vortex for 30 seconds; BZ2-3, incubation: incubate in a 60°C water bath for 30 minutes. During the incubation period, mix by inversion every 5 minutes. BZ2-4, ice bath: After the incubation in a 60°C water bath, place the mixture on ice for 5 min; BZ2-5, enrichment: After the ice bath, place the sample at room temperature. After the sample temperature returns to room temperature, add 500 μL MagMAX cell-free DNA lysis / binding solution and 10 μL MagMAX™ cfDNA magnetic beads, and then oscillate the mixture on a vortex oscillator at medium speed for 10 minutes. After instantaneous separation, place it on a magnetic stand for 5 minutes until the solution is clear; BZ2-6, washing: remove the supernatant, add 500 μL MagMAX™ cfDNA washing solution, mix the solution thoroughly, place it in the magnetic stand again until it is clear, then discard the supernatant and wash twice with 500 μL 80% ethanol; BZ2-7, elution: After the sample is dried on a magnetic rack at room temperature, 30 μL of elution buffer is finally pipetted into it and the resulting mixture is placed on a magnetic rack for 2 minutes until the solution is clear. Aspirate the supernatant and store the cfDNA; BZ2-8, concentration determination: The quantification was performed using the Qubit ds DNA HS detection kit, i.e., the detection working solution in the kit was mixed with the DNA sample to be tested, and then the reading was performed using the Qubit 4.0 fluorescence quantifier to obtain the cfDNA concentration in these plasma samples; BZ2-9, determination of fragment length: the Bioptic QSEP-100 fully automatic nucleic acid protein analysis system was used to detect the distribution range of DNA fragment length. Theoretically, the peak value of cfDNA fragments was around 167 bp; BZ2-10, Storage: Store the isolated cfDNA at -20°C until library construction.

3. The method for establishing a cfDNA fragmentation map of early bladder cancer according to claim 1, characterized in that: The BZ3 specifically includes: BZ3-1, thaw EndPrepMix4 and mix thoroughly by inversion, and prepare the mixed solution system in a sterilized PCR tube: Input DNA 30ul, EndPrepMix4 15ul, ddH2OTo 65μl; BZ3-2, use a pipette to gently pipette and mix, ensuring that it is not shaken, and briefly centrifuge to collect the reaction solution at the bottom of the tube; BZ3-3. Place the PCR tube in the PCR instrument and carry out the reaction: heat cover at 105℃On, 20℃ for 15 min, 65℃ for 15 min, maintain at 4℃.

4. The method for establishing a cfDNA fragmentation map of early bladder cancer according to claim 1, characterized in that: The BZ4 specifically includes: BZ4-1, dilute Adapter to appropriate concentration according to input DNA amount: BZ4-2, thaw RapidLigation buffer, mix thoroughly by inversion, and place on ice for later use; BZ4-3. Prepare the mixed solution system in the PCR tube of the end repair step: BZ4-4, use a pipette to gently blow and mix, do not oscillate to mix, and centrifuge briefly to collect the reaction solution at the bottom of the tube; BZ4-5, place the PCR tube in the PCR instrument and perform the reaction; BZ4-6, use VAHTS DNA Clean Beads to purify the reaction products.

5. The method for establishing a cfDNA fragmentation map of early bladder cancer according to claim 4, characterized in that: The BZ4-6 specifically includes: a) After the magnetic beads are equilibrated to room temperature, vortex and mix the VAHTS DNA Clean Beads; b) Pipette 60 μl of VAHTS DNA Clean Beads into 100 μl of Adapter Ligation product and vortex or pipette gently 10 times to mix thoroughly; c) Incubate at room temperature for 5 minutes; d) Centrifuge the PCR tube briefly and place it on a magnetic rack to separate the beads and liquid. After the solution is clear for 5 minutes, carefully remove the supernatant. e) Keep the PCR tube in the magnetic rack, add 200 μl of freshly prepared 80% ethanol to rinse the magnetic beads, incubate at room temperature for 30 seconds, and carefully remove the supernatant; f) Repeat step e, rinsing twice in total; g) The PCR tube is always placed in the magnetic rack, and the magnetic beads are air-dried for 5-10 minutes with the lid open until no ethanol remains; h) Remove the PCR tube from the magnetic rack and elute: add 22.5μl of elution buffer, vortex or use a pipette to mix thoroughly, let it stand at room temperature for 2 minutes, centrifuge the PCR tube briefly and place it on the magnetic rack to stand. After the solution has clarified for 5 minutes, carefully transfer 20μl of the supernatant to a new EP tube, and do not touch the magnetic beads.

6. The method for establishing a cfDNA fragmentation map of early bladder cancer according to claim 5, characterized in that: The eluent is 10 mM TrisHCl, pH 8.0-8.

5.

7. The method for establishing a cfDNA fragmentation map of early bladder cancer according to claim 1, characterized in that: The sequencer used in BZ7 is the BGISEQ-2000RS sequencer.

Images