Method for cancer prognosis and composition therefor

Abstract

The present invention relates to a method for prognosing cancer and a composition therefor. More specifically, according to the present invention, it is possible to prognose cancer, particularly prostate cancer, and to more precisely predict the likelihood of metastasis or recurrence in prostate cancer patients.

Description

Method for predicting cancer prognosis and composition thereof

[0001] The present invention relates to a method for predicting cancer prognosis and a composition thereof. More specifically, the present invention relates to a method for predicting cancer prognosis, and a composition therefor for predicting cancer recurrence and / or cancer metastasis.

[0002] Cancer is one of the most common causes of death worldwide. Approximately 10 million new cases occur annually, accounting for approximately 12% of all deaths, making it the third leading cause of death. Among various types of cancer, prostate cancer is the most common cancer among men worldwide and the second leading cause of death. It occurs primarily in men over 50 years of age and has a rapid increase in incidence with age. While it typically progresses slowly, once it becomes malignant and metastasizes, it is extremely difficult to treat. Metastases typically begin in the lymph nodes, pelvic bones, spine, and bladder surrounding the prostate cancer, gradually spreading throughout the body.

[0003] Currently, primary methods for diagnosing prostate cancer include the prostate-specific antigen (PSA) test and digital rectal examination. Imaging modalities include transrectal ultrasound, CT, MRI, and whole body bone scan (WBBS). Biopsy is also performed. However, most of these methods have low diagnostic accuracy, make early diagnosis difficult, and struggle to detect metastases. Furthermore, they often struggle to distinguish between benign conditions like benign prostatic hyperplasia (BPH) and prostatitis.

[0004] Although prognostic tools based on RNA expression levels (e.g., Decipher, Prolaris, and oncotypeDX) have recently entered the market, they are technologies that utilize classical methods such as PCR and DNA microarray, and thus the number of markers that can be predicted simultaneously is limited to about 30. In addition, the prognostic accuracy of factors currently used for prognostic prediction, including the prostate cancer stage (TNM stage), is significantly lower than that of other cancer types. Furthermore, among prognostic prediction tools, there is a lack of tools that can precisely predict metastasis and the possibility of recurrence.

[0005] Accordingly, the inventors of the present invention discovered a combination of biomarkers that enables analysis of whether prostate cancer has metastasized and enables more precise diagnosis, leading to the present invention.

[0006] One object of the present invention is to provide a composition, a kit, and a method for providing information for predicting prognosis of cancer, particularly prostate cancer.

[0007] Another object of the present invention is to provide a composition, a kit, and a method for providing information for predicting the possibility of recurrence and / or metastasis of cancer, particularly prostate cancer.

[0008] However, the technical problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

[0009] Hereinafter, various embodiments described herein will be described with reference to the drawings. In the following description, various specific details, such as specific configurations, compositions, and processes, are set forth to provide a thorough understanding of the present invention. However, certain embodiments may be practiced without one or more of these specific details, or in conjunction with other known methods and configurations. In other instances, well-known processes and manufacturing techniques have not been described in specific detail so as not to unnecessarily obscure the present invention. Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, configuration, composition, or characteristic described in connection with the embodiment is included in one or more embodiments of the present invention. Thus, the appearances of "in one embodiment" or "an embodiment" in various places throughout this specification do not necessarily refer to the same embodiment of the present invention. Additionally, the particular features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments.

[0010] Unless otherwise specifically defined herein, all scientific and technical terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0011]

[0012] As used herein, the term "polynucleotide," when used in singular or plural, generally refers to a single polyribonucleotide or polydeoxyribonucleotide. This may be unmodified RNA or DNA, or modified RNA or DNA. Thus, for example, polynucleotides as defined herein include, but are not limited to, single-stranded and double-stranded DNA, DNA including single-stranded and double-stranded regions, single-stranded and double-stranded RNA, and RNA including single-stranded and double-stranded regions, and hybrid molecules including DNA and RNA that may be single-stranded but are more typically double-stranded or may include single-stranded and double-stranded regions. Furthermore, as used herein, the term "polynucleotide" refers to a three-stranded region comprising RNA, DNA, or both RNA and DNA. The strands within such a region may be derived from the same molecule or from different molecules. These regions may encompass the entirety of one or more molecules, but more typically encompass only a portion of the molecule. One molecule in the double helix region is an oligonucleotide. The term "polynucleotide" specifically includes cDNA. The term also encompasses DNA (including cDNA) and RNA containing one or more modified bases. Thus, DNA or RNA having a main chain modified for stability or other reasons is a "polynucleotide" as the term is used herein. Furthermore, DNA or RNA containing modified bases, such as inosine or tritiated bases, are included within the term "polynucleotide" as defined herein. In general, the term "polynucleotide" encompasses all chemically, enzymatically, or metabolically modified forms of unmodified polynucleotides, as well as chemical forms of DNA and RNA that are specific to viruses and cells.

[0013] As used herein, the term "oligonucleotide" refers to relatively short polynucleotides, including but not limited to single-stranded deoxyribonucleotides, single-stranded or double-stranded ribonucleotides, RNA:DNA hybrids, and double-stranded DNA. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are synthesized chemically using commercially available automated oligonucleotide synthesizers. In addition, oligonucleotides can be prepared by various other methods, such as in vitro recombinant DNA-mediated techniques or by expression of DNA in cells and organisms.

[0014] The term "primer" as used herein refers to a fragment that recognizes a target gene sequence, including a pair of forward and reverse primers, but preferably a pair of primers that provide analysis results with specificity and sensitivity. High specificity can be achieved when the nucleic acid sequence of the primer is a sequence that does not match the non-target sequence present in the sample, thereby amplifying only the target gene sequence containing the complementary primer binding site and not causing non-specific amplification.

[0015] The term "probe" as used herein refers to a substance that can specifically bind to a target substance to be detected in a sample, and refers to a substance that can specifically confirm the presence of the target substance in the sample through said binding. The type of probe is not limited to a substance commonly used in the art, but is preferably PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA, or DNA, and most preferably PNA. More specifically, the probe includes a biomaterial derived from or similar to a living organism or manufactured in vitro, and may be, for example, an enzyme, a protein, an antibody, a microorganism, an animal or plant cell and organ, a nerve cell, DNA, and RNA. DNA includes cDNA, genomic DNA, and oligonucleotides, RNA includes genomic RNA, mRNA, and oligonucleotides, and examples of proteins include antibodies, antigens, enzymes, peptides, etc.

[0016] As used herein, the term "Locked nucleic acids (LNA)" refers to a nucleic acid analog containing a 2'-O, 4'-C methylene bridge [J Weiler, J Hunziker and J Hall Gene Therapy (2006) 13, 496-502]. LNA nucleosides contain common nucleic acid bases of DNA and RNA and can form base pairs according to the Watson-Crick base pairing rule. However, due to the 'locking' of the molecule caused by the methylene bridge, LNA does not form an ideal shape in Watson-Crick binding. When LNA is included in a DNA or RNA oligonucleotide, LNA can pair with a complementary nucleotide chain more quickly and increase the stability of the double helix.

[0017] The term "antisense" as used herein refers to an oligomer having a sequence of nucleotide bases and an intersubunit backbone that allows the antisense oligomer to hybridize to a target sequence within RNA by Watson-Crick base pairing, typically forming an mRNA and RNA:oligomer heteroduplex within the target sequence. The oligomer may have exact sequence complementarity or approximate sequence complementarity to the target sequence.

[0018] As used herein, the term "antibody" refers to a substance that specifically binds to an antigen and causes an antigen-antibody reaction. For the purposes of the present invention, an antibody refers to an antibody that specifically binds to the protein. Antibodies of the present invention include polyclonal antibodies, monoclonal antibodies, and recombinant antibodies. The antibodies can be readily produced using techniques well known in the art. For example, polyclonal antibodies can be produced by methods well known in the art, including a process of injecting an antigen of the protein into an animal and collecting blood from the animal to obtain serum containing the antibody. Such polyclonal antibodies can be produced from any animal, such as a goat, rabbit, sheep, monkey, horse, pig, cow, or dog. In addition, monoclonal antibodies can be produced using the hybridoma method (see Kohler and Milstein (1976) European Journal of Immunology 6:511-519), which is well known in the art, or the phage antibody library technology (see Clackson et al, Nature, 352:624-628, 1991; Marks et al, J. Mol. Biol., 222:58, 1-597, 1991). Antibodies produced by the above methods can be separated and purified using methods such as gel electrophoresis, dialysis, salt precipitation, ion exchange chromatography, and affinity chromatography. In addition, the antibodies of the present invention include not only complete forms having two full-length light chains and two full-length heavy chains, but also functional fragments of antibody molecules. A functional fragment of an antibody molecule refers to a fragment that possesses at least an antigen-binding function, and includes Fab, F(ab'), F(ab')2, and Fv.

[0019] As used herein, the term "oligopeptide" refers to a peptide consisting of 2 to 20 amino acids and may include, but is not limited to, dipeptides, tripeptides, tetrapeptides, and pentapeptides.

[0020] The term "PNA (Peptide Nucleic Acid)" in this specification refers to an artificially synthesized polymer similar to DNA or RNA, and was first introduced in 1991 by Professors Nielsen, Egholm, Berg, and Buchardt of the University of Copenhagen, Denmark. While DNA has a phosphate-ribose sugar backbone, PNA has a repeating N-(2-aminoethyl)-glycine backbone linked by peptide bonds, which greatly increases its binding affinity and stability to DNA or RNA, and is used in molecular biology, diagnostic analysis, and antisense therapy. PNA is described in detail in the literature [Nielsen PE, Egholm M, Berg RH, Buchardt O (December 1991). "Sequence-selective recognition of DNA by strand displacement with a thymine-substituted polyamide". Science 254 (5037): 1497-1500].

[0021] The term "aptamer" as used herein refers to an oligonucleic acid or peptide molecule, and the general content of aptamers is described in detail in the literature [Bock LC et al., Nature 355(6360):5646(1992); Hoppe-Seyler F, Butz K "Peptide aptamers: powerful new tools for molecular medicine". J Mol Med. 78(8):42630(2000); Cohen BA, Colas P, Brent R. "An artificial cell-cycle inhibitor isolated from a combinatorial library". Proc Natl Acad Sci USA. 95(24): 142727(1998)].

[0022] As used herein, the term "gene expression" refers to the conversion of DNA gene sequence information into transcript RNA (either the initial non-spliced ​​RNA transcript or mature mRNA) or encoded protein product. Gene expression can be monitored by measuring the levels of either the total RNA or protein product of the gene, or their subsequent levels.

[0023] In this specification, with respect to RNA transcripts, the term "overexpression" is used to refer to the level of a transcript determined by measuring the level of all transcripts measured in a sample or a particular reference set of mRNAs and normalizing to the level of a reference mRNA.

[0024] As used herein, the term "gene amplification" refers to the process by which multiple copies of a gene or gene fragment are formed in a specific cell or cell line. The replicated region (amplified DNA) is also referred to as an "amplicon." Furthermore, the amount of mRNA produced, i.e., the level of gene expression, generally increases in proportion to the number of copies of each gene being expressed.

[0025] As used herein, the term "protein expression" refers to the process of converting a gene-encoded protein into a protein product. Protein expression can be monitored by measuring protein product levels.

[0026] As used herein, the term "prognosis" refers to a broad concept encompassing the course of a disease, including cancer migration and invasion within tissues, metastasis to other tissues, recurrence, and death due to the disease, as well as the possibility of complete recovery. For the purposes of the present invention, prognosis refers to the course of the disease or survival prognosis of a patient with solid cancer, specifically prostate cancer. Using the method of the present invention, the survival prognosis of prostate cancer can be easily determined, making it easy to determine whether to use additional treatment methods. Ultimately, the survival rate after the onset of prostate cancer can be improved.

[0027] More specifically, the prognosis to be predicted in the present invention includes the possibility of recurrence, metastasis, or a combination thereof after surgical operation.

[0028] As used herein, the term "metastasis" refers to the process by which cancer cells detach from the primary tumor (the initial cancer) and migrate to other parts of the body, forming new tumors there. Metastasis is a critical stage that increases the lethality of cancer, and it is known that more than 90% of cancer deaths are due to metastasis. The present invention enables a more precise diagnosis of cancer metastasis, particularly in prostate cancer.

[0029] As used herein, the term "recurrence" refers to the recurrence of cancer that appeared to be cured for a certain period of time after treatment. This occurs when cancer cells remaining after treatment regrow over time and form a new tumor, making prior diagnosis crucial. Depending on the location of the recurrence, cancer is categorized into local recurrence, regional recurrence, and distant recurrence. In the present invention, the likelihood of cancer recurrence may relate to local recurrence, but is not limited thereto.

[0030] As used herein, the term "prediction" refers to the act of predicting the course and outcome of a disease. More specifically, prognostic prediction can be interpreted as any act of predicting the course of a disease after treatment, taking into account the patient's physiological and environmental conditions, which may vary depending on the patient's condition.

[0031] The term "beneficial response" as used herein means improvement in any measure of patient status, such as overall survival, long-term survival, recurrence-free survival, and distant recurrence-free survival, which are commonly used in the art. Recurrence-free survival (RFS) refers to the time (in months) from surgery to the first local recurrence, regional recurrence, or distant recurrence. Distant recurrence-free survival (DRFS) or distant metastasis-free survival (DMFS) refers to the time (in months) from surgery to the first distant recurrence. Recurrence refers to RFS and / or DFRS. The term "long-term" survival as used herein refers to survival of at least 3 years, or at least 5 years, or at least 8 years, or at least 10 years after surgery or other treatment.

[0032] As used herein, the term "tumor" refers to any neoplastic cell growth and proliferation, whether malignant or benign, and any precancerous condition and cancerous cells and tissues.

[0033] As used herein, the terms “cancer” and “cancerous” refer to the physiological condition in mammals that is generally characterized by uncontrolled cell proliferation. Examples of cancer include, but are not limited to, prostate cancer, breast cancer, mammary cancer, glioma, thyroid cancer, lung cancer, liver cancer, pancreatic cancer, head and neck cancer, stomach cancer, colon cancer, urothelial cancer, kidney cancer, testicular cancer, penile cancer, uterine cancer, cervical cancer, endometrial cancer, cervical cancer, fallopian tube cancer, vaginal cancer, ovarian cancer, melanoma, skin cancer, blood cancer, bone cancer, skin cancer, brain cancer, endocrine cancer, parathyroid cancer, ureter cancer, urethral cancer, bronchial cancer, bladder cancer, bone marrow cancer, leukemia, brain tumor, bowel cancer, esophageal cancer, Ewing's sarcoma, tongue cancer, lymphoma, kaposi sarcoma, mesothelioma, multiple myeloma, neuroblastoma, osteosarcoma, and retinoblastoma.

[0034] As used herein, the term "pathology" of cancer encompasses any phenomenon that impairs the patient's health. Examples include, but are not limited to, abnormal or uncontrolled cell proliferation, metastasis, interference with the normal function of adjacent cells, release of abnormal levels of cytokines or other secreted products, suppression or exacerbation of inflammatory or immune responses, neoplasms, premalignant tumors, malignant tumors, and infiltration of surrounding or distant tissues or organs, such as lymph nodes.

[0035] In this specification, nucleic acid sequencing including DNA or RNA may be next generation sequencing (NGS). Nucleic acid sequencing may be used interchangeably with base sequencing, sequence analysis, or sequencing. The NGS may be used interchangeably with massive parallel sequencing or second-generation sequencing. The NGS is a technique for simultaneously sequencing a large number of nucleic acid fragments, and may fragment the entire genome in a chip-based or polymerase chain reaction (PCR)-based paired end format, and perform ultra-high-speed sequence analysis based on hybridization of the fragments. The above NGS may be performed by, for example, a 454 platform (Roche), GS FLX Titanium, Illumina MiSeq, Illumina HiSeq, Illumina HiSeq 2500, Illumina Genome Analyzer, Solexa platform, SOLiD System (Applied Biosystems), Ion Proton (Life Technologies), Complete Genomics, Helicos Biosciences Heliscope, Pacific Biosciences' single molecule real-time (SMRT) technology, or a combination thereof. The above nucleic acid sequencing may be a nucleic acid sequencing method for analyzing only a region of interest. The above nucleic acid sequencing may include, for example, NGS-based targeted sequencing, targeted deep sequencing, or panel sequencing.

[0036] In this specification, "Next Generation Sequencing (NGS)" refers to a method that divides the genome into countless fragments, decodes and reassembles the genetic information of each fragment, and then analyzes the entire base sequence. It has the advantage of being able to analyze the base sequence of the genome at high speed, and is also called high-throughput sequencing, massive parallel sequencing, or second-generation sequencing. Compared to NGS, Sanger sequencing can also read the entire human genome, but it can only target known genes, which limits the scope of testing, and requires repeated experiments to examine multiple genes. For example, compared to Sanger sequencing, which requires approximately 3 million segments, next-generation sequencing is a significantly improved analysis method in terms of time and cost. Massively parallel base sequence analysis enabled by next-generation sequencing (NGS) technology is another way to access the enumeration of RNA transcripts in tissue samples, and RNA-sequencing utilizes this. It is currently the most powerful analytical tool used for transcriptome analysis, including differences in gene expression levels between different physiological conditions or changes occurring during development or disease progression. Specifically, RNA-sequencing can be used to study phenomena such as gene expression changes, alternative splicing events, allele-specific gene expression, gene fusion events, de novo transcripts, and chimeric transcripts, including RNA editing.

[0037] As used herein, "prostate cancer" refers to a malignant tumor arising within the prostate gland, such as a condition classified as malignant by biopsy. Clinical diagnostic techniques for prostate cancer, such as prostate-specific antigen (PSA) and digital rectal examination, are well-known in the medical field. Those skilled in the art will understand that prostate cancer refers to all malignancies of prostate tissue, including malignant tumors and sarcomas.

[0038] In this specification, the term "subject" may refer to a patient who has developed or is suspected of developing prostate cancer, and is in need of or is expected to require appropriate treatment for prostate cancer, but is not limited thereto.

[0039] In this specification, "biological sample" means any sample that can confirm the patient's genetic or protein information, and may be, but is not limited to, blood, plasma, serum, etc., and is not limited to any type that can confirm the level of gene or protein expression.

[0040] In this specification, "diagnostic device" means equipment capable of diagnosing diseases externally based on substances produced in the human body, such as blood, saliva, and urine, and includes, for example, a detection unit; a calculation unit; and an output unit, and is not limited as long as it is in a form capable of analyzing the level of gene or protein expression from the substance.

[0041]

[0042] According to one embodiment of the present invention, the present invention relates to a composition for predicting the prognosis of cancer and a composition for predicting the possibility of metastasis or recurrence of cancer.

[0043] In the present invention, the composition includes ALDH1A3, ANO7, CRACR2B, ENO1, EPS8L1, HSPA1A, ITGB4, KLK3, KLK4, MALAT1, NPM1, PLA2G2A, SLC45A3, SPINT2, ZFP36, ACTA2, ACTB, ACTG1, ALDOA, ARID1A, B2M, BCAM, C3, CD63, CHCHD10, CKB, CLU, COL1A2, COL6A1, COX6A1, CST3, DDT, DENND2B, EEF1A1, EEF1G, EEF2, EGR1, EHMT1, FAM193B, FAU, FOS, FOSB, FTH1, FTL, GAPDH, GBF1, GFAP, GSTP1, H2AJ, H2BC12, H3-3A, H3-3B, H4C12, HLA-DRB1, HNRNPA1, HNRNPC, HNRNPH1, HSF4, HSPA8, HSPB1, HSPB6, ITM2B, JUNB, KLF13, LENG8, LTBP4, MAN2C1, MBP, MGP, MIB2, MIF, MYL6, MYO15B, NCOR2, NDRG1, NDUFA1, NPDC1, NPIPA6, NPIPA9, NR4A1, OAZ1, PEBP1, PER1, PKD1, PKM, PPIA, PSAP, RACK1, RERE, SH2B3, SPARC, SPON2, SREBF1, TCEAL4, TMSB10, TMSB4X, TNK2, TNS2, TPT1, TRIM3, TUBA1B, WDR1 and ZNF414 It may include a formulation that measures the expression level of at least one gene selected from the group or a protein encoded thereby.

[0044] In the present invention, the composition comprises ACLY, ACOX1, ACSL4, ACVR1, AKT1, AXIN1, AXIN2, BCL2L1, BMP4, BMPR1A, BRCA1, BTK, CASP3, CASP8, CASP9, CCNA2, CCNB2, CCND1, CCR5, CD40, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CS, CTNNB1, CXCR4, DVL1, DVL2, EP300, FYN, GADD45A, GNB1, GPX4, GRB2, GSK3B, H3C12, H3C13, H4C6, HMOX1, HRAS, IKBKB, IKBKG, IL1B, IL6, INS, JAG1, JAK1, JAK2, JAK3, JUN, The composition may further include an agent for measuring the expression level of at least one gene selected from the group consisting of KRAS, LCK, LEF1, LPCAT3, LYN, MAML1, MAML2, MAML3, MAPK1, MAPK3, MCM7, MLST8, MTOR, NCOA4, NFKB1, NFKBIA, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRAS, PIK3CA, PIK3R1, PLCG2, PTEN, RBPJ, RELA, RHEB, RHOA, RPTOR, RUNX1, SDHB, SMAD2, SMAD3, SMAD4, SOS1, SRC, STAT1, STAT3, SYK, TFRC, TGFB1, TNF, TNFRSF1A, TP53, TRAF6, TSC2 and TYK2 or a protein encoded by the same.

[0045] Among the genes in the present invention, ACTB, H3-3B, ACLY, ACOX1, ACSL4, ACVR1, AKT1, AXIN1, AXIN2, BCL2L1, BMP4, BMPR1A, BRCA1, BTK, CASP3, CASP8, CASP9, CCNA2, CCNB2, CCND1, CCR5, CD40, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CS, CTNNB1, CXCR4, DVL1, DVL2, EP300, FYN, GADD45A, GNB1, GPX4, GRB2, GSK3B, H3C12, H3C13, H4C6, HMOX1, HRAS, IKBKB, IKBKG, IL1B, IL6, INS, JAG1, JAK1, JAK2, The genes JAK3, JUN, KRAS, LCK, LEF1, LPCAT3, LYN, MAML1, MAML2, MAML3, MAPK1, MAPK3, MCM7, MLST8, MTOR, NCOA4, NFKB1, NFKBIA, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRAS, PIK3CA, PIK3R1, PLCG2, PTEN, RBPJ, RELA, RHEB, RHOA, RPTOR, RUNX1, SDHB, SMAD2, SMAD3, SMAD4, SOS1, SRC, STAT1, STAT3, SYK, TFRC, TGFB1, TNF, TNFRSF1A, TP53, TRAF6, TSC2, and TYK2 are characterized as signal transduction pathway-related genes.

[0046] The published Gene ID information (ensembl_gene_id) of the signal transduction pathway related genes of the present invention is as follows:

[0047] ACTB(ensembl_gene_id: ENSG00000075624), H3-3B(ensembl_gene_id: ENSG00000132475), ACLY(ensembl_gene_id: ENSG00000131473), ACOX1(ensembl_gene_id: ENSG00000161533), ACSL4(ensembl_gene_id: ENSG00000068366), ACVR1(ensembl_gene_id: ENSG00000115170), AKT1(ensembl_gene_id: ENSG00000142208), AXIN1(ensembl_gene_id: ENSG00000103126), AXIN2(ensembl_gene_id: ENSG00000168646), BCL2L1(ensembl_gene_id: ENSG00000171552), BMP4(ensembl_gene_id: ENSG00000125378), BMPR1A(ensembl_gene_id: ENSG00000107779), BRCA1(ensembl_gene_id: ENSG00000012048), BTK(ensembl_gene_id: ENSG00000010671), CASP3(ensembl_gene_id: ENSG00000164305), CASP8(ensembl_gene_id: ENSG00000064012), CASP9(ensembl_gene_id: ENSG00000132906), CCNA2(ensembl_gene_id: ENSG00000145386), CCNB2(ensembl_gene_id: ENSG00000157456), CCND1(ensembl_gene_id: ENSG00000110092), CCR5(ensembl_gene_id: ENSG00000160791), CD40(ensembl_gene_id: ENSG00000101017), CDK1(ensembl_gene_id: ENSG00000170312), CDK2(ensembl_gene_id: ENSG00000123374),CDK4(ensembl_gene_id: ENSG00000135446), CDKN1A(ensembl_gene_id: ENSG00000124762), CDKN2A(ensembl_gene_id: ENSG00000147889), CHEK1(ensembl_gene_id: ENSG00000149554), CS(ensembl_gene_id: ENSG00000062485), CTNNB1(ensembl_gene_id: ENSG00000168036), CXCR4(ensembl_gene_id: ENSG00000121966), DVL1(ensembl_gene_id: ENSG00000107404), DVL2(ensembl_gene_id: ENSG00000004975), EP300(ensembl_gene_id: ENSG00000100393), FYN(ensembl_gene_id: ENSG00000010810), GADD45A(ensembl_gene_id: ENSG00000116717), GNB1(ensembl_gene_id: ENSG00000078369), GPX4(ensembl_gene_id: ensembl_gene_id: ENSG00000167468), GRB2(ensembl_gene_id: ENSG00000177885), GSK3B(ensembl_gene_id: ENSG00000082701), H3C12(ensembl_gene_id: ENSG00000197153), H3C13(ensembl_gene_id: ENSG00000183598), H4C6(ensembl_gene_id: ENSG00000274618), HMOX1(ensembl_gene_id: ENSG00000100292), HRAS(ensembl_gene_id: ENSG00000174775), IKBKB(ensembl_gene_id: ENSG00000104365), IKBKG(ensembl_gene_id: ENSG00000269335), IL1B(ensembl_gene_id: ENSG00000125538),IL6(ensembl_gene_id: ENSG00000136244), INS(ensembl_gene_id: ENSG00000254647), JAG1(ensembl_gene_id: ENSG00000101384), JAK1(ensembl_gene_id: ENSG00000162434), JAK2(ensembl_gene_id: ENSG00000096968), JAK3(ensembl_gene_id: ENSG00000105639), JUN(ensembl_gene_id: ENSG00000177606), KRAS(ensembl_gene_id: ENSG00000133703), LCK(ensembl_gene_id: ENSG00000182866), LEF1(ensembl_gene_id: ENSG00000138795), LPCAT3(ensembl_gene_id: ENSG00000111684), LYN(ensembl_gene_id: ENSG00000254087), MAML1(ensembl_gene_id: ENSG00000161021), MAML2(ensembl_gene_id: ENSG00000184384), MAML3(ensembl_gene_id: ENSG00000196782), MAPK1(ensembl_gene_id: ENSG00000100030), MAPK3(ensembl_gene_id: ENSG00000102882), MCM7(ensembl_gene_id: ENSG00000166508), MLST8(ensembl_gene_id: ENSG00000167965), MTOR(ensembl_gene_id: ENSG00000198793), NCOA4(ensembl_gene_id: ENSG00000266412), NFKB1(ensembl_gene_id: ENSG00000109320), NFKBIA(ensembl_gene_id: ENSG00000100906), NOTCH1(ensembl_gene_id: ENSG00000148400),NOTCH2(ensembl_gene_id: ENSG00000134250), NOTCH3(ensembl_gene_id: ENSG00000074181), NOTCH4(ensembl_gene_id: ENSG00000204301), NRAS(ensembl_gene_id: ENSG00000213281), PIK3CA(ensembl_gene_id: ENSG00000121879), PIK3R1(ensembl_gene_id: ENSG00000145675), PLCG2(ensembl_gene_id: ENSG00000197943), PTEN(ensembl_gene_id: ENSG00000171862), RBPJ(ensembl_gene_id: ENSG00000168214), RELA(ensembl_gene_id: ENSG00000173039), RHEB(ensembl_gene_id: ENSG00000106615), RHOA(ensembl_gene_id: ENSG00000067560), RPTOR(ensembl_gene_id: ENSG00000141564), RUNX1(ensembl_gene_id: ENSG00000159216), SDHB(ensembl_gene_id: ENSG00000117118), SMAD2(ensembl_gene_id: ENSG00000175387), SMAD3(ensembl_gene_id: ENSG00000166949), SMAD4(ensembl_gene_id: ENSG00000141646), SOS1(ensembl_gene_id: ENSG00000115904), SRC(ensembl_gene_id: ENSG00000197122), STAT1(ensembl_gene_id: ENSG00000115415), STAT3(ensembl_gene_id: ENSG00000168610), SYK(ensembl_gene_id: ENSG00000165025), TFRC(ensembl_gene_id: ENSG00000072274),TGFB1 (ensembl_gene_id: ENSG00000105329), TNF (ensembl_gene_id: ENSG00000232810), TNFRSF1A (ensembl_gene_id: ENSG00000067182), TP53 (ensembl_gene_id: ENSG00000141510), TRAF6 (ensembl_gene_id: ENSG00000175104), TSC2 (ensembl_gene_id: ENSG00000103197) and TYK2 (ensembl_gene_id: ENSG00000105397);

[0048] In the present invention, the signal transduction pathway may be a signal transduction pathway related to cancer death, cancer proliferation inhibition, cancer metastasis or recurrence, and may be specifically a signal transduction pathway related to prostate cancer death, proliferation inhibition, metastasis or recurrence, but is not limited thereto.

[0049] In the present invention, the "signal transduction pathway-related gene" refers to a gene that is involved in the cancer-related signal transduction pathway described above or can obtain information on activation or inactivation of the pathway through its expression pattern.

[0050] In the present invention, the signal transduction pathway is an adipogenesis pathway, an apoptosis pathway Ⅱ, an E2F targets pathway, an estrogen response early pathway, an estrogen response late pathway, a NOTCH signaling pathway, a WNT-Beta Catenin signaling pathway, a MAPK signaling pathway, an ErbB signaling pathway, a Ras signaling pathway, a Rap1 signaling pathway, a chemokine signaling pathway, an NF-kappa B signaling pathway, a cell cycle pathway, a p53 signaling pathway, an mTOR signaling pathway. PI3K-Akt signaling pathway, Apoptosis pathway Ⅰ, Ferroptosis pathway, Necroptosis pathway, Cellular senescence pathway, Wnt signaling pathway, Notch signaling pathway, TGF-beta signaling pathway, JAK-STAT signaling pathway, T cell receptor signaling pathwayIt may be at least one signaling pathway selected from the group consisting of, but not limited to, B cell receptor signaling pathway, TNF signaling pathway, Transcriptional misregulation pathway in cancer, Prostate cancer pathway, ESR Mediated signaling pathway, Estrogen Dependent gene expression pathway, and Androgen Receptor Network pathway in Prostate cancer.

[0051] In the composition of the present invention, the agent for measuring the expression level of the gene may include, but is not limited to, one or more selected from the group consisting of a primer, a probe, and an antisense nucleotide that specifically bind to the gene.

[0052] In the composition of the present invention, the agent for measuring the expression level of the protein encoded by the gene may include, but is not limited to, one or more selected from the group consisting of antibodies, oligopeptides, ligands, PNA (peptide nucleic acid), and aptamers that specifically bind to the protein.

[0053] In the present invention, the cancer may be at least one selected from the group consisting of prostate cancer, breast cancer, mammary cancer, glioma, thyroid cancer, lung cancer, liver cancer, pancreatic cancer, head and neck cancer, stomach cancer, colon cancer, urothelial cancer, kidney cancer, testicular cancer, penile cancer, uterine cancer, cervical cancer, endometrial cancer, cervical cancer, fallopian tube cancer, vaginal cancer, ovarian cancer, melanoma, skin cancer, blood cancer, bone cancer, skin cancer, brain cancer, endocrine cancer, parathyroid cancer, ureteral cancer, urethral cancer, bronchial cancer, bladder cancer, bone marrow cancer, leukemia, brain tumor, intestinal cancer, esophageal cancer, Ewing's sarcoma, tongue cancer, lymphoma, kaposi sarcoma, mesothelioma, multiple myeloma, neuroblastoma, osteosarcoma, and retinoblastoma, and may be specifically prostate cancer, but is not limited thereto.

[0054]

[0055] According to another embodiment of the present invention, the present invention relates to a kit for predicting cancer prognosis, a kit for predicting the possibility of cancer metastasis or recurrence, comprising the composition.

[0056] In the present invention, the kit may be, but is not limited to, an NGS kit, an RT-PCR kit, a DNA chip kit, an RNA sequencing kit, an ELISA kit, a protein chip kit, or a rapid kit.

[0057] The kit of the present invention may further comprise one or more other component compositions, solutions or devices suitable for the analysis method.

[0058] For example, in the present invention, the kit may further include essential elements necessary for performing a reverse transcription polymerase reaction. The reverse transcription polymerase reaction kit includes a pair of primers specific for a gene encoding a marker protein. The primers are nucleotides having a sequence specific for the nucleic acid sequence of the gene, and may have a length of about 7 bp to 50 bp, more preferably about 10 bp to 30 bp. It may also include a primer specific for the nucleic acid sequence of a control gene. In addition, the reverse transcription polymerase reaction kit may include a test tube or other appropriate container, a reaction buffer (with various pH and magnesium concentrations), deoxynucleotides (dNTPs), an enzyme such as Taq polymerase and reverse transcriptase, DNase, RNase inhibitor DEPC-water, sterile water, etc.

[0059] Additionally, the kit of the present invention may include essential elements necessary for performing a DNA chip. The DNA chip kit may include a substrate to which cDNA or oligonucleotides corresponding to a gene or fragment thereof are attached, and reagents, preparations, enzymes, etc. for producing a fluorescently labeled probe. The substrate may also include cDNA or oligonucleotides corresponding to a control gene or fragment thereof.

[0060] Additionally, the kit of the present invention may include essential components necessary for performing an ELISA. The ELISA kit includes an antibody specific for the protein. The antibody is an antibody with high specificity and affinity for the marker protein and little cross-reactivity with other proteins, and may be a monoclonal antibody, polyclonal antibody, or recombinant antibody. The ELISA kit may also include an antibody specific for a control protein. In addition, the ELISA kit may include reagents capable of detecting bound antibodies, such as labeled secondary antibodies, chromophores, enzymes (e.g., conjugated to antibodies), and their substrates or other substances capable of binding to antibodies.

[0061] In the kit of the present invention, a fixative for the antigen-antibody binding reaction may be a nitrocellulose membrane, a PVDF membrane, a well plate synthesized from polyvinyl resin or polystyrene resin, a glass slide glass, etc., but is not limited thereto.

[0062] In addition, in the kit of the present invention, the label of the secondary antibody is preferably a conventional chromogen that undergoes a color development reaction, and labels such as fluorescein and dyes such as HRP (horseradish peroxidase), alkaline phosphatase, colloid gold, FITC (poly L-lysine-fluorescein isothiocyanate), and RITC (rhodamine-B-isothiocyanate) can be used, but are not limited thereto.

[0063] In addition, in the kit of the present invention, it is preferable to use a chromogenic substrate for inducing color development according to a marker that undergoes a color development reaction, and TMB (3,3',5,5'-tetramethyl bezidine), ABTS [2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)], OPD (o-phenylenediamine), etc. can be used. At this time, it is more preferable that the chromogenic substrate is provided in a state dissolved in a buffer solution (0.1 M NaAc, pH 5.5). A chromogenic substrate such as TMB is decomposed by HRP used as a marker of a secondary antibody conjugate to generate a chromogenic precipitate, and the presence or absence of the marker proteins is detected by visually confirming the degree of deposition of this chromogenic precipitate.

[0064] In the kit of the present invention, the washing solution preferably contains phosphate buffer, NaCl, and Tween 20, and a buffer solution (PBST) composed of 0.02 M phosphate buffer, 0.13 M NaCl, and 0.05% Tween 20 is more preferred. After the antigen-antibody binding reaction, the washing solution reacts the antigen-antibody complex with a secondary antibody, and then adds an appropriate amount to the fixative and washes 3 to 6 times. The reaction stopping solution can preferably be a sulfuric acid solution (H2SO4).

[0065]

[0066] According to another embodiment of the present invention, there is provided a method for providing information on predicting cancer prognosis, and a method for providing information on predicting the possibility of cancer metastasis or recurrence.

[0067] In the present invention, the method comprises the steps of: ALDH1A3, ANO7, CRACR2B, ENO1, EPS8L1, HSPA1A, ITGB4, KLK3, KLK4, MALAT1, NPM1, PLA2G2A, SLC45A3, SPINT2, ZFP36, ACTA2, ACTB, ACTG1, ALDOA, ARID1A, B2M, BCAM, C3, CD63, CHCHD10, CKB, CLU, COL1A2, COL6A1, COX6A1, CST3, DDT, DENND2B, EEF1A1, EEF1G, EEF2, EGR1, EHMT1, FAM193B, FAU, FOS, FOSB, FTH1, FTL, GAPDH, GBF1, GFAP, GSTP1, H2AJ, H2BC12, H3-3A, H3-3B, H4C12, HLA-DRB1, HNRNPA1, HNRNPC, HNRNPH1, HSF4, HSPA8, HSPB1, HSPB6, ITM2B, JUNB, KLF13, LENG8, LTBP4, MAN2C1, MBP, MGP, MIB2, MIF, MYL6, MYO15B, NCOR2, NDRG1, NDUFA1, NPDC1, NPIPA6, NPIPA9, NR4A1, OAZ1, PEBP1, PER1, PKD1, PKM, PPIA, PSAP, RACK1, RERE, SH2B3, SPARC, SPON2, SREBF1, TCEAL4, TMSB10, TMSB4X, TNK2, TNS2, TPT1, TRIM3, TUBA1B, WDR1 and It may include a step of measuring the expression level of at least one gene selected from the group consisting of ZNF414 or a protein encoded by the same.

[0068] In the present invention, the method comprises the steps of: ACLY, ACOX1, ACSL4, ACVR1, AKT1, AXIN1, AXIN2, BCL2L1, BMP4, BMPR1A, BRCA1, BTK, CASP3, CASP8, CASP9, CCNA2, CCNB2, CCND1, CCR5, CD40, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CS, CTNNB1, CXCR4, DVL1, DVL2, EP300, FYN, GADD45A, GNB1, GPX4, GRB2, GSK3B, H3C12, H3C13, H4C6, HMOX1, HRAS, IKBKB, IKBKG, IL1B, IL6, INS, JAG1, JAK1, JAK2, The method may further include a step of measuring the expression level of at least one gene selected from the group consisting of JAK3, JUN, KRAS, LCK, LEF1, LPCAT3, LYN, MAML1, MAML2, MAML3, MAPK1, MAPK3, MCM7, MLST8, MTOR, NCOA4, NFKB1, NFKBIA, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRAS, PIK3CA, PIK3R1, PLCG2, PTEN, RBPJ, RELA, RHEB, RHOA, RPTOR, RUNX1, SDHB, SMAD2, SMAD3, SMAD4, SOS1, SRC, STAT1, STAT3, SYK, TFRC, TGFB1, TNF, TNFRSF1A, TP53, TRAF6, TSC2 and TYK2 or a protein encoded by the same.

[0069] The measurement of the expression level according to the present invention is a process of confirming the presence and degree of expression of mRNA of the gene group, and can be performed by measuring the expression amount of the corresponding gene from mRNA extracted from a sample of a subject. Analysis methods for measuring the expression level include, but are not limited to, RT-PCR, competitive RT-PCR, real-time RT-PCR, RNase protection assay (RPA), northern blotting, DNA microarray chips, etc., and can be performed using any appropriate method commonly used in the art.

[0070] Preferably, the agent for measuring the expression level of mRNA according to the present invention is an antisense oligonucleotide, primer, or probe. Based on the base sequence of the gene group, a primer or probe that specifically amplifies a specific region of these genes can be designed. Since the base sequence of the gene group according to the present invention is registered in GenBank and is known in the art, those skilled in the art can design an antisense oligonucleotide, primer, or probe that can specifically amplify a specific region of these genes based on the base sequence.

[0071] According to the present invention, the expression level of each gene or transcript of each sample can be confirmed by using the number of reads mapped through RNA sequencing as a method for calculating the differential expression level of genes. However, there may be an error for each sample in defining the expression level by the number of mapped reads, and it is difficult to view it as an objective value. Therefore, a normalization process was performed as a method for deriving a more objective value.

[0072] In the present invention, the cancer may be at least one selected from the group consisting of prostate cancer, breast cancer, mammary cancer, glioma, thyroid cancer, lung cancer, liver cancer, pancreatic cancer, head and neck cancer, stomach cancer, colon cancer, urothelial cancer, kidney cancer, testicular cancer, penile cancer, uterine cancer, cervical cancer, endometrial cancer, cervical cancer, fallopian tube cancer, vaginal cancer, ovarian cancer, melanoma, skin cancer, blood cancer, bone cancer, skin cancer, brain cancer, endocrine cancer, parathyroid cancer, ureteral cancer, urethral cancer, bronchial cancer, bladder cancer, bone marrow cancer, leukemia, brain tumor, intestinal cancer, esophageal cancer, Ewing's sarcoma, tongue cancer, lymphoma, kaposi sarcoma, mesothelioma, multiple myeloma, neuroblastoma, osteosarcoma, and retinoblastoma, and specifically, may be prostate cancer.

[0073] The method for providing information on cancer prognosis of the present invention can not only predict the cancer prognosis of a subject, but can also be used to predict the possibility of cancer metastasis and, further, the possibility of cancer recurrence of the subject.

[0074]

[0075] According to another embodiment of the present invention, a cancer prognosis diagnostic device is provided, comprising: (a) a detection unit that measures and normalizes the expression level of a group of genes or a group of proteins encoded by the same in a biological sample obtained from a subject; and (b) an output unit that predicts and outputs a cancer prognosis.

[0076] In the present invention, the detection unit detects ALDH1A3, ANO7, CRACR2B, ENO1, EPS8L1, HSPA1A, ITGB4, KLK3, KLK4, MALAT1, NPM1, PLA2G2A, SLC45A3, SPINT2, ZFP36, ACTA2, ACTB, ACTG1, ALDOA, ARID1A, B2M, BCAM, C3, CD63, CHCHD10, CKB, CLU, COL1A2, COL6A1, COX6A1, CST3, DDT, DENND2B, EEF1A1, EEF1G, EEF2, EGR1, EHMT1, FAM193B, FAU, FOS, FOSB, FTH1, FTL, GAPDH, GBF1, GFAP, GSTP1, H2AJ, H2BC12, H3-3A, H3-3B, H4C12, HLA-DRB1, HNRNPA1, HNRNPC, HNRNPH1, HSF4, HSPA8, HSPB1, HSPB6, ITM2B, JUNB, KLF13, LENG8, LTBP4, MAN2C1, MBP, MGP, MIB2, MIF, MYL6, MYO15B, NCOR2, NDRG1, NDUFA1, NPDC1, NPIPA6, NPIPA9, NR4A1, OAZ1, PEBP1, PER1, PKD1, PKM, PPIA, PSAP, RACK1, RERE, SH2B3, SPARC, SPON2, SREBF1, TCEAL4, TMSB10, TMSB4X, TNK2, TNS2, TPT1, TRIM3, TUBA1B, The expression level of at least one gene selected from the group consisting of WDR1 and ZNF414 or a protein encoded by the same can be measured.

[0077] In the present invention, the detection unit can additionally measure the expression level of a signal transduction pathway-related gene or a protein encoded by the same from the biological sample obtained from the subject.

[0078] In the present invention, the signal transduction pathway may be a signal transduction pathway related to cancer death, cancer proliferation inhibition, cancer metastasis or recurrence, and may be specifically a signal transduction pathway related to prostate cancer death, proliferation inhibition, metastasis or recurrence, but is not limited thereto.

[0079] In the present invention, the "signal transduction pathway-related gene" refers to a gene that is involved in the cancer-related signal transduction pathway described above or can obtain information on activation or inactivation of the pathway through its expression pattern.

[0080] In the present invention, the signal transduction pathway is an adipogenesis pathway, an apoptosis pathway Ⅱ, an E2F targets pathway, an estrogen response early pathway, an estrogen response late pathway, a NOTCH signaling pathway, a WNT-Beta Catenin signaling pathway, a MAPK signaling pathway, an ErbB signaling pathway, a Ras signaling pathway, a Rap1 signaling pathway, a chemokine signaling pathway, an NF-kappa B signaling pathway, a cell cycle pathway, a p53 signaling pathway, an mTOR signaling pathway. PI3K-Akt signaling pathway, Apoptosis pathway Ⅰ, Ferroptosis pathway, Necroptosis pathway, Cellular senescence pathway, Wnt signaling pathway, Notch signaling pathway, TGF-beta signaling pathway, JAK-STAT signaling pathway, T cell receptor signaling pathwayIt may be at least one signaling pathway selected from the group consisting of, but not limited to, B cell receptor signaling pathway, TNF signaling pathway, Transcriptional misregulation pathway in cancer, Prostate cancer pathway, ESR Mediated signaling pathway, Estrogen Dependent gene expression pathway, and Androgen Receptor Network pathway in Prostate cancer.

[0081] In the present invention, the cancer may be at least one selected from the group consisting of prostate cancer, breast cancer, mammary cancer, glioma, thyroid cancer, lung cancer, liver cancer, pancreatic cancer, head and neck cancer, stomach cancer, colon cancer, urothelial cancer, kidney cancer, testicular cancer, penile cancer, uterine cancer, cervical cancer, endometrial cancer, cervical cancer, fallopian tube cancer, vaginal cancer, ovarian cancer, melanoma, skin cancer, blood cancer, bone cancer, skin cancer, brain cancer, endocrine cancer, parathyroid cancer, ureteral cancer, urethral cancer, bronchial cancer, bladder cancer, bone marrow cancer, leukemia, brain tumor, intestinal cancer, esophageal cancer, Ewing's sarcoma, tongue cancer, lymphoma, kaposi sarcoma, mesothelioma, multiple myeloma, neuroblastoma, osteosarcoma, and retinoblastoma, and specifically, may be prostate cancer.

[0082] The present invention can predict the prognosis of cancer, particularly prostate cancer. Furthermore, it can predict whether a prostate cancer patient is at high risk for metastasis or recurrence.

[0083] Figure 1 is a diagram showing an ROC curve that verifies the performance of a RandomForest model using only DEG markers according to one embodiment of the present invention using TestSet.

[0084] FIG. 2 is a diagram showing an ROC curve that confirms the performance of a RandomForest model using only DEG markers according to one embodiment of the present invention with a validation set that was not used for model development.

[0085] FIG. 3 is a diagram showing an ROC curve that verifies a RandomForest model using only signal transmission pathway-related markers according to one embodiment of the present invention using TestSet.

[0086] FIG. 4 is a diagram showing an ROC curve that verifies a RandomForest model using only signal transmission pathway-related markers according to one embodiment of the present invention using a validation set that was not used in model development.

[0087] FIG. 5 is a diagram showing an ROC curve that verifies a RandomForest model using a combination of DEG markers and signal transduction pathway-related markers according to one embodiment of the present invention using TestSet.

[0088] FIG. 6 is a diagram showing an ROC curve that verifies a RandomForest model using a combination of DEG markers and signal transduction pathway-related markers according to one embodiment of the present invention using a validation set that was not used in model development.

[0089] Hereinafter, the present invention will be described in more detail through examples. These examples are intended solely to illustrate the present invention more specifically, and it will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples, in accordance with the gist of the present invention.

[0090]

[0091] Example

[0092]

[0093] Selection of target prostate cancer patients and preparation of test tissues

[0094] To screen genes associated with prognosis in prostate cancer, tissue samples were obtained from patients who underwent surgery after a diagnosis of prostate cancer but did not develop clinical recurrence (BCR) or metastasis (METS; no evidence of disease) (NED), patients with biochemical recurrence (BCR), and patients with metastasis (METZ). RNA was extracted from representative formalin-fixed paraffin-embedded (FFPE) blocks. The goal was to obtain a list of genes whose expression levels were differentially expressed among patients who underwent surgery after a diagnosis of prostate cancer (NED), patients with biochemical recurrence (BCR), and patients with metastasis (METZ). In-house Korean prostate cancer patient data were utilized. The sample data from this in-house Korean prostate cancer patient data were broadly divided into three patient groups: NED, BCR, and METS. Patients who underwent surgery for localized or locally advanced prostate cancer and were followed up were selected if they met the following three criteria. The first group of patients is the BCR and no metastases / recurrence group (NED) for 7 years after radical prostatectomy. The second group of patients is the BCR group (BCR) group, which is a group of patients who have experienced biochemical recurrence (blood PSA level increased by 0.2 ng / ml or more on two consecutive occasions after radical prostatectomy) but have not developed metastases within the 5-year follow-up period. The third group of patients is the METS group, which is a group of patients who have developed metastases within the 5-year follow-up period.

[0095]

[0096] RNA-sequencing

[0097] Before performing NGS analysis, the DQ score (DCgen Quality score, DQ score) system, which is a system that selects high-quality NGS libraries suitable for NGS data production by synthesizing the QC information generated, was used to select high-quality NGS libraries suitable for NGS data production. If it was confirmed to have a certain level of RNA quality, a cDNA library was created by selecting high-quality NGS libraries. Only specific genes were detected using gene panel probes. All candidate genes were sequenced using next-generation sequencing (NGS) equipment and aligned to the human genome. They were mapped to the public human genome reference using an alignment algorithm called STAR. The expression level of each gene was measured from the information of the aligned analysis sequences.

[0098]

[0099] Screening of markers related to prognosis, metastasis, or recurrence of prostate cancer based on RNA-sequencing expression information.

[0100] To discover the optimal biomarker combination, a step-by-step screening process was performed through 1) in-house data sample matching, 2) extraction of metastasis-related markers, 3) extraction of key markers related to prostate cancer signal transduction, and 4) extraction of housekeeping genes.

[0101] The patient samples used in the development were collected by matching the high clinical risk of patients with confirmed recurrence (estimated by tumor size, PSA level, and Gleason score) with clinical information. This is because when samples with low clinical risk are used to build a model, clinical indicators contribute most to recurrence, so only gene expression levels can be used as variables when building a model. At this time, in order to differentiate it from models that use existing clinical information, sample matching was performed using a statistical methodology called propensity score matching (PSM) so that the distributions of 1) cancer stage, 2) pre-treatment blood PSA level, and 3) Gleason score were similar. PSM matching was performed using the MatchIT package in R. Before matching, the necessity of matching was confirmed when the difference in variables by institution was lower than p-value ≤ 0.05 when performing ANOVA test and Kruskal test. PSM matching was performed with the nearest criterion of 0.8 as the tolerance level of the MatchIT package. To enable analysis of differences in data range on a common scale, z-score (standardized score) was applied to log2(TPM) values.

[0102] To obtain a list of genes whose expression levels are increased or decreased in patients with metastasis compared to NED patients, Limma, a differentially expressed gene (DEG) analysis tool, was used. The selection criteria were genes with a p-value of 0.05 or less, which indicates the significance of the difference and the actual difference in expression levels, and a difference in the absolute log2 (fold-change) of 0.585 or more. Genes whose expression levels differed depending on whether the patients had metastasis were selected. To verify these genes, additional genes showing differences in expression levels were selected by combining factors that influence cancer metastasis, such as ISUP grade and T-stage grade, and then an additional step of comparative analysis was performed. As shown in Table 1, 103 significant genes were identified, and the results of GSEA analysis showed that the selected genes had a higher interpretability related to biological mechanisms and a high correlation with prostate cancer metastasis than other factors. Ultimately, they were selected as the optimal combination of biomarkers for diagnosing prostate cancer metastasis.

[0103] gene_idgene_namelogFCP valueabs_logFCENSG00000004776HSPB6-0.8513781890.0020388780.851378189ENSG00000008710PKD1-0.5895362560.0079586420.589536256ENSG00000034510TMSB102.3188484155.0705E-112.318848415ENSG00000061938TNK2-0.6400866053.68972E-060.640086605ENSG00000067225PKM0.7070830491.53094E-050.707083049ENSG00000071127WDR10.597693092.06496E-070.59769309ENSG00000072310SREBF1-1.0386935450.000904511.038693545ENSG00000074800ENO10.8408507721.09025E-060.840850772ENSG00000075624ACTB3.4541195034.9876E-073.454119503ENSG00000084207GSTP10.7984585260.0025045180.798458526ENSG00000087086FTL0.9297589720.0229501870.929758972ENSG00000089220PEBP11.1517138243.422E-081.151713824ENSG00000090006LTBP4-0.8605539470.0097468520.860553947ENSG00000092199HNRNPC0.8912723791.91104E-070.891272379ENSG00000092841MYL60.8960464630.0028728480.896046463ENSG00000099977DDT0.8145820991.6297E-070.814582099ENSG00000101439CST30.7306515080.0002006040.730651508ENSG00000102878HSF4-0.7322745884.08602E-050.732274588ENSG00000104419NDRG10.9650266810.0055515870.965026681ENSG00000104904OAZ10.7386965961.20419E-050.738696596ENSG00000106211HSPB11.3872529130.000292921.387252913ENSG00000107281NPDC1-0.7818102260.0001577340.781810226ENSG00000107796ACTA21.1626671480.0001375471.162667148ENSG00000107862GBF1-0.7104547070.0174457170.710454707ENSG00000109971HSPA80.6750120373.33441E-110.675012037ENSG00000110171TRIM3-0.6976552940.0001224930.697655294ENSG00000111077TNS2-0.5904710790.0202271820.590471079ENSG00000111252SH2B3-0.9691366311.60892E-050.969136631ENSG00000111341MGP0.930021750.0006324780.93002175ENSG00000111640GAPDH1.1427423671.94777E-071.142742367ENSG00000111775COX6A10.6287831891.26053E-080.628783189ENSG00000113140SPARC0.8944042514.76476E-060.894404251ENSG00000117713ARID1A-1.0602781452.75276E-061.060278145ENSG00000120738EGR1-1.003498730.0063606881.00349873ENSG00000120885CLU2.7572438214.42145E-092.757243821ENSG00000123358NR4A1-5.0655581434.05657E-085.065558143ENSG00000123416TUBA1B0.8373781471.41634E-090.837378147ENSG00000125356NDUFA10.7995192431.89763E-070.799519243ENSG00000125730C31.0921543384.1011E-061.092154338ENSG00000125740FOSB-1.9059772740.0119815821.905977274ENSG00000128016ZFP36-2.1417986873.02412E-052.141798687ENSG00000131037EPS8L1-0.8789790930.0013747910.878979093ENSG00000131095GFAP3.7262478098.48121E-053.726247809ENSG00000132470ITGB4-0.6640210830.0025619270.664021083ENSG00000132475H3-3B1.0012056787.65756E-051.001205678ENSG00000133112TPT11.3075101660.000456711.307510166ENSG00000133142TCEAL40.8178104370.0054334950.817810437ENSG00000133250ZNF414-1.3950452160.0020668921.395045216ENSG00000135404CD630.5853294441.74459E-070.585329444ENSG00000135486HNRNPA11.5398965414.3155E-051.539896541ENSG00000136156ITM2B0.6296112820.0019876250.629611282ENSG00000140400MAN2C1-0.6150563320.0052688650.615056332ENSG00000142156COL6A1-0.6780234970.0017879110.678023497ENSG00000142515KLK3-10.477635060.00076448310.47763506ENSG00000142599RERE-0.8704199429.36718E-070.870419942ENSG00000146067FAM193B-0.6146478220.0129232320.614647822ENSG00000146205ANO7-1.6140528545.37859E-101.614052854ENSG00000149806FAU0.7753578053.30801E-080.775357805ENSG00000149925ALDOA1.0156746924.37283E-071.015674692ENSG00000156508EEF1A11.6551875941.78296E-061.655187594ENSG00000158715SLC45A3-1.8094760132.57772E-051.809476013ENSG00000159674SPON2-2.3728628330.0077123962.372862833ENSG00000163041H3-3A0.6358115140.0044394810.635811514ENSG00000164692COL1A20.723872762.36013E-100.72387276ENSG00000166165CKB1.0428677220.0013148761.042867722ENSG00000166444DENND2B-0.6566261110.0121911650.656626111ENSG00000166710B2M2.5397947210.0190058312.539794721ENSG00000167615LENG8-1.0788582911.90007E-071.078858291ENSG00000167642SPINT20.5895419910.0007815660.589541991ENSG00000167658EEF22.2063793460.0159636562.206379346ENSG00000167749KLK4-0.9506628670.0026706560.950662867ENSG00000167996FTH11.9426348294.94462E-081.942634829ENSG00000169045HNRNPH11.9304231837.37271E-051.930423183ENSG00000169926KLF13-0.6457839590.002370950.645783959ENSG00000170345FOS-2.5798835510.0029992962.579883551ENSG00000171223JUNB-0.7706724610.0035091790.770672461ENSG00000177685CRACR2B-0.7860919892.27557E-050.786091989ENSG00000179094PER1-1.0401991430.0003174631.040199143ENSG00000181090EHMT1-0.6611780770.0004329130.661178077ENSG00000181163NPM10.8078896680.0022681780.807889668ENSG00000183889NPIPA6-0.5937876460.0484294860.593787646ENSG00000184009ACTG11.1384500369.60393E-121.138450036ENSG00000184254ALDH1A3-1.4962454352.27668E-081.496245435ENSG00000187244BCAM-0.7292334430.0117935630.729233443ENSG00000188257PLA2G2A0.9364135590.0353842230.936413559ENSG00000196126HLA-DRB11.0292104189.16255E-091.029210418ENSG00000196262PPIA1.4535063756.82079E-141.453506375ENSG00000196498NCOR2-0.61240550.0292152590.6124055ENSG00000197530MIB2-0.754085940.0013834460.75408594ENSG00000197746PSAP0.6054375895.69857E-050.605437589ENSG00000197903H2BC121.0202134087.21446E-081.020213408ENSG00000197971MBP4.2092817670.00013514.209281767ENSG00000204389HSPA1A0.6518087150.0065809410.651808715ENSG00000204628RACK10.6854785770.0305744790.68547 8577ENSG00000205542TMSB4X1.5784523811.52324E-051.578452381ENSG0000023 3024NPIPA9-0.6435793950.0168807530.643579395ENSG00000240972MIF1.19512 90250.0026090811.195129025ENSG00000246705H2AJ-0.6075997250.0134350110. 607599725ENSG00000250479CHCHD100.6923582850.0075903320.692358285ENSG0 0000251562MALAT1-1.0694685120.0048273871.069468512ENSG00000254772EEF1 G0.9411978157.8788E-050.941197815ENSG00000266714MYO15B-1.564944639.48 967E-061.56494463ENSG00000273542H4C120.7734277858.7048E-070.773427785.

[0104]

[0105] Screening and analysis of biomarkers related to signaling pathways associated with prostate cancer metastasis

[0106] Pathway analysis was performed to determine the biological mechanisms associated with the biomarkers selected through the screening above. A list of signal transduction pathways containing keywords related to prostate cancer and its development was selected, and a list of pathways related to prostate cancer was selected from the results of a GSEA analysis based on genes showing differences in expression levels. The MSigDB database, a related database, was used to extract a list of genes included in the signal transduction pathways. To identify the central genes that are strongly connected to other signals and have the most interactions in the signal transduction pathway analysis, the STRING DB, a protein-protein interaction database, was used. Based on the protein-protein interaction database, the top 10 central genes with the highest centrality in each signal transduction pathway and the highest number of connections between two other genes were selected, resulting in a final selection of 103 central genes.

[0107] As a result of the analysis, as shown in Table 2 below, among the signaling pathways closely related to prostate cancer metastasis, 33 signaling pathways with the highest centrality were discovered, and among the biomarkers acting on the signaling pathways, the combination of biomarkers with the most close correlation was derived.

[0108] DB source genes (signaling pathway) genes (selected genes)MSigDBProstate cancerAKT1, CASP9, CCND1, CDK2, CDKN1A, CTNNB1, EP300, GRB2, GSK3B, HRAS, IKBKB, IKBKG, . INS, KRAS, LEF1, MAPK1, MAPK3, MTOR, NFKB1, NFKBIA, NRAS, PIK3CA, PIK3R1, PTEN, RELA, SOS1, TP53MSigDBTranscriptional misregulation in cancerBCL2L1, CCNA2, CD40, CDKN1A, GADD45A, H3-3B, H3C12, H3C13, IL6, NFKB1, RELA, RUNX1, TP53MSigDBTNF signaling pathwayAKT1, CASP3, CASP8, IKBKB, IKBKG, IL1B, IL6, JAG1, JUN, MAPK1, MAPK3, NFKB1, NFKBIA, PIK3CA, PIK3R1, RELA, TNF, . TNFRSF1AMSigDBB cell receptor signaling pathwayAKT1, BTK, GRB2, GSK3B, HRAS, IKBKB, IKBKG, JUN, KRAS, LYN, MAPK1, MAPK3, NFKB1, NFKBIA, NRAS, PIK3CA, PIK3R1, PLCG2, RELA, SOS1, SYKMSigDBT cell receptor signaling pathwayAKT1, CDK4, FYN, GRB2, GSK3B, HRAS, IKBKB, IKBKG, JUN, KRAS, LCK, MAPK1, MAPK3, NFKB1, NFKBIA, NRAS, PIK3CA, PIK3R1, RELA, RHOA, SOS1, TNFMSigDBJAK-STAT signaling pathwayAKT1, BCL2L1, CCND1, CDKN1A, EP300, GRB2, HRAS, IL6, JAK1, JAK2, JAK3, MTOR, PIK3CA,PIK3R1, SOS1, STAT1, STAT3, TYK2MSigDBTGF-beta signaling pathwayACVR1, BMP4, BMPR1A, EP300, MAPK1, MAPK3, RHOA, SMAD2, SMAD3, SMAD4, TFRC, TGFB1, TNFMSigDBNotch signaling pathwayDVL1, DVL2, EP300, JAG1, MAML1, MAML2, MAML3, NOTCH1, NOTCH2, NOTCH3, NOTCH4, RBPJMSigDBWnt signaling pathwayAXIN1, AXIN2, CCND1, CTNNB1, DVL1, DVL2, EP300, GSK3B, JUN, LEF1, RHOA, SMAD3, SMAD4, TP53MSigDBCellular senescenceAKT1, CCNA2, CCNB2, CCND1, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, GADD45A, HRAS, IL6, KRAS, MAPK1, MAPK3, MTOR, NFKB1, NRAS, PIK3CA, PIK3R1, PTEN, RELA, RHEB, SMAD2, SMAD3, TGFB1, TP53, TSC2MSigDBNecroptosisCASP8, IL1B, JAK1, JAK2, JAK3, STAT1, STAT3, TNF, TNFRSF1A, TYK2MSigDBFerroptosisACSL4, GPX4, HMOX1, LPCAT3, NCOA4, TFRC, TP53MSigDBApoptosisⅠACTB, AKT1, BCL2L1, CASP3, CASP8, CASP9, GADD45A, HRAS, IKBKB, IKBKG, JUN, KRAS, MAPK1, MAPK3, NFKB1, NFKBIA, NRAS, PIK3CA, PIK3R1, RELA, TNF, TNFRSF1A, TP53MSigDBPI3K-Akt signaling pathwayAKT1, BCL2L1, BRCA1, CASP9, CCND1, CDK2, CDK4, CDKN1A,GNB1, GRB2, GSK3B, HRAS, IKBKB, IKBKG, IL6, INS, JAK1, JAK2, JAK3, KRAS, MAPK1, MAPK3, MLST8, MTOR, NFKB1, NRAS, PIK3CA, PIK3R1, PTEN, RELA, RHEB, RPTOR, SOS1, SYK, TP53, TSC2MSigDBmTOR signaling pathwayAKT1, DVL1, DVL2, GRB2, GSK3B, HRAS, IKBKB, INS, KRAS, MAPK1, MAPK3, MLST8, MTOR, NRAS, PIK3CA, PIK3R1, PTEN, RHEB, RHOA, RPTOR, SOS1, TNF, TNFRSF1A, TSC2MSigDBp53 signaling pathwayBCL2L1, CASP3, CASP8, CASP9, CCNB2, CCND1, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, GADD45A, PTEN, TP53, TSC2MSigDBCell cycleCCNA2, CCNB2, CCND1, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, EP300, GADD45A, GSK3B, MCM7, SMAD2, SMAD3, SMAD4, TGFB1, TP53MSigDBNF-kappa B signaling pathwayBCL2L1, BTK, CD40, GADD45A, IKBKB, IKBKG, IL1B, LCK, LYN, NFKB1, NFKBIA, PLCG2, RELA, SYK, TNF, TNFRSF1A, TRAF6MSigDBChemokine signaling pathwayAKT1, CCR5, CXCR4, GNB1, GRB2, GSK3B, HRAS, IKBKB, IKBKG, JAK2, JAK3, KRAS, LYN, MAPK1, MAPK3, NFKB1, NFKBIA, NRAS, PIK3CA, PIK3R1, PLCG2, RELA, RHOA, SOS1, SRC, STAT1,STAT3MSigDBRap1 signaling pathwayACTB, AKT1, CTNNB1, HRAS, INS, KRAS, MAPK1, MAPK3, NRAS, PIK3CA, PIK3R1, RHOA, SRCMSigDBRas signaling pathwayAKT1, BCL2L1, GNB1, GRB2, HRAS, IKBKB, IKBKG, INS, KRAS, MAPK1, MAPK3, NFKB1, NRAS, PIK3CA, PIK3R1, PLCG2, RELA, RHOA, SOS1MSigDBErbB signaling pathwayAKT1, CDKN1A, GRB2, GSK3B, HRAS, JUN, KRAS, MAPK1, MAPK3, MTOR, NRAS, PIK3CA, PIK3R1, PLCG2, SOS1, SRCMSigDBMAPK signaling pathwayAKT1, CASP3, GADD45A, GRB2, HRAS, IKBKB, IKBKG, IL1B, INS, JUN, KRAS, MAPK1, MAPK3, NFKB1, NRAS, RELA, SOS1, TGFB1, TNF, TNFRSF1A, TP53, TRAF6MSigDBWNT beta CATENIN signalingAXIN1, AXIN2, CTNNB1, DVL2, JAG1, LEF1, MAML1, NOTCH1, NOTCH4, RBPJ, TP53MSigDBNOTCH signalingCCND1, DVL1, DVL2, EP300, JAG1, MAML1, MAML2, MAML3, NOTCH1, NOTCH2, NOTCH3, NOTCH4, RBPJMSigDBEstrogen Response LATECCND1, JAK1, JAK2MSigDBEstrogen Response EARLYCCND1, JAK2MSigDBE2F TargetsBRCA1, CCNB2, CDK1, CDK4, CDKN1A, CDKN2A, CHEK1, MCM7, TFRC, TP53MSigDBApoptosisⅡBCL2L1, BRCA1, CASP3, CASP8, CASP9,CCND1, CDK2, CDKN1A, CTNNB1, GADD45A, GPX4, HMOX1, IL1B, IL6, JUN, LEF1, RELA, TNFMSigDBAdipogenesisACLY, ACOX1, CS, GADD45A, GPX4, LPCAT3, SDHBMSigDBESR mediated signalingAKT1, AXIN1, CCND1, EP300, GNB1, H3-3B, H3C12, H3C13, H4C6, HRAS, JUN, KRAS, MAPK1, MAPK3, NRAS, PIK3CA, PIK3R1, RUNX1, SRCMSigDBEstrogen dependent gene expressionAXIN1, CCND1, EP300, H3-3B, H3C12, H3C13, H4C6, JUN, RUNX1MSigDBAndrogen receptor network in prostate cancerAKT1, BRCA1, CASP3, CASP8, CASP9, CCND1, CDK1, CDK2, CDK4, CHEK1, GRB2, HRAS, JAK1, JUN, MAPK1, MAPK3, MTOR, PIK3CA, PTEN, RHEB, RPTOR, SMAD2, SMAD3, SOS1, STAT1, STAT3, TP53, TSC2,

[0109]

[0110] 수별 창도 방탄소년단

[0111] The housekeeping gene list was prepared using the HRT Atlas v1.0 database, and only overlapping gene lists from the external public data, TCGA, and in-house data were used. The list extraction step was performed under appropriate experimental conditions and to provide evidence of good sample quality. We aimed to prioritize the selection of a list of genes with high expression levels that could minimize the influence of experimental noise and other factors and ensure stable expression levels. In addition, we analyzed the variability of each gene to further select genes with low variability that ensure consistent expression patterns and reproducibility of experimental results. In each dataset, genes satisfying high expression and low variance were selected, and the top 10 reference genes were selected by assigning rank scores to 23 genes. The top 10 genes were derived by checking all statistical values, including minimum and maximum values ​​of gene expression levels and coefficient of variation, and consist of PPP2R1A, LAMTOR1, RNF167, ENSA, AP2M1, RNF10, BANF1, SLC25A3, APH1A, and DNAJB2.

[0112] Finally, 103 genes exhibiting differential expression levels and 103 core genes selected from 33 signaling pathways were selected. Among the genes exhibiting differential expression levels and genes selected from signaling pathways, H3-3B and ACTB were identified as common biomarkers. Therefore, a total of 204 genes were derived as the final biomarker combination for diagnosing prostate cancer prognosis, metastasis, and / or recurrence.

[0113]

[0114] Validation of prostate cancer prognosis prediction

[0115] Clinical validation was conducted on 172 prostate cancer patients who had been followed for more than five years. For all patients, risk scores were calculated using the prognostic tool described above, and the product's performance was compared with that of existing products. In order to improve the prediction ability during the verification process, a random forest algorithm classification model was applied, and 103 DEG markers showing differences in gene expression levels (ALDH1A3, ANO7, CRACR2B, ENO1, EPS8L1, HSPA1A, ITGB4, KLK3, KLK4, MALAT1, NPM1, PLA2G2A, SLC45A3, SPINT2, ZFP36, ACTA2, ACTB, ACTG1, ALDOA, ARID1A, B2M, BCAM, C3, CD63, CHCHD10, CKB, CLU, COL1A2, COL6A1, COX6A1, CST3, DDT, DENND2B, EEF1A1, EEF1G, EEF2, EGR1, EHMT1, FAM193B, FAU, FOS, FOSB, FTH1, FTL, GAPDH, GBF1, GFAP, GSTP1, H2AJ, H2BC12, H3-3A, H3-3B, H4C12, HLA-DRB1, HNRNPA1, HNRNPC, HNRNPH1, HSF4, HSPA8, HSPB1, HSPB6, ITM2B, JUNB, KLF13, LENG8, LTBP4, MAN2C1, MBP, MGP, MIB2, MIF, MYL6, MYO15B, NCOR2, NDRG1, NDUFA1, NPDC1, NPIPA6, NPIPA9, NR4A1, OAZ1, PEBP1, PER1, PKD1, PKM, PPIA, PSAP, RACK1, RERE, SH2B3, SPARC, SPON2, SREBF1, TCEAL4, TMSB10, TMSB4X, TNK2, TNS2, Results using only markers related to signaling pathways (ACTB, H3-3B, ACLY, ACOX1, ACSL4, TPT1, TRIM3, TUBA1B, WDR1, and ZNF414) (see Figs. 1 and 2)ACVR1, AKT1, AXIN1, AXIN2, BCL2L1, BMP4, BMPR1A, BRCA1, BTK, CASP3, CASP8, CASP9, CCNA2, CCNB2, CCND1, CCR5, CD40, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CS, CTNNB1, CXCR4, DVL1, DVL2, EP300, FYN, GADD45A, GNB1, GPX4, GRB2, GSK3B, H3C12, H3C13, H4C6, HMOX1, HRAS, IKBKB, IKBKG, IL1B, IL6, INS, JAG1, JAK1, JAK2, JAK3, JUN, KRAS, LCK, LEF1, LPCAT3, The results using only LYN, MAML1, MAML2, MAML3, MAPK1, MAPK3, MCM7, MLST8, MTOR, NCOA4, NFKB1, NFKBIA, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRAS, PIK3CA, PIK3R1, PLCG2, PTEN, RBPJ, RELA, RHEB, RHOA, RPTOR, RUNX1, SDHB, SMAD2, SMAD3, SMAD4, SOS1, SRC, STAT1, STAT3, SYK, TFRC, TGFB1, TNF, TNFRSF1A, TP53, TRAF6, TSC2, and TYK2) (see Figs. 3 and 4) and the results using DEG markers and signal transduction pathway-related markers (see Figs. 5 and 6) were confirmed. Referring to Figure 1, it was confirmed that when the DEG marker was used alone, the AUC value was 0.892, indicating excellent diagnostic performance. Referring to Figure 5, it was confirmed that when the signal transmission pathway-related marker was applied in an overlapping manner with the DEG marker, the AUC value was greatly improved to 0.904, indicating that the performance of the model was significantly improved (see Table 3 below).

[0116] Feature SetDatasetSensitivitySpecificityPPVNPVF1ACCAUCDEG + pathwayRF_testSet0.85290.81940.69050.92190.76320.83020.904RF_validSet0.56720.80950 .65520.74560.6080.71510.8203DEGRF_testSet0.67650.97220.920.86420.77970.87740.8922R F_validSet0.46270.95240.86110.73530.60190.76160.8183PathwayRF_testSet0.70590.61110 .46150.81480.55810.64150.6801RF_validSet0.44780.77140.55560.68640.49590.64530.6748

[0117] As can be seen from the above results, when using a combination of 103 DEG markers and 103 signaling pathway-related markers of the present invention, it was confirmed that it is possible to predict the prognosis of prostate cancer, which was a limitation of the existing prostate cancer diagnostic model, and further, to more precisely determine the possibility of metastasis and recurrence of prostate cancer. Therefore, it is expected that this will greatly contribute to improving the survival rate of prostate cancer patients.

[0118]

[0119] While specific aspects of the present invention have been described in detail above, it should be apparent to those skilled in the art that these specific descriptions are merely preferred embodiments and do not limit the scope of the present invention. Therefore, the substantial scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. ALDH1A3, ANO7, CRACR2B, ENO1, EPS8L1, HSPA1A, ITGB4, KLK3, KLK4, MALAT1, NPM1, PLA2G2A, SLC45A3, SPINT2, ZFP36, ACTA2, ACTB, ACTG1, ALDOA, ARID1A, B2M, BCAM, C3, CD63, CHCHD10, CKB, CLU, COL1A2, COL6A1, COX6A1, CST3, DDT, DENND2B, EEF1A1, EEF1G, EEF2, EGR1, EHMT1, FAM193B, FAU, FOS, FOSB, FTH1, FTL, GAPDH, GBF1, GFAP, GSTP1, H2AJ, H2BC12, H3-3A, H3-3B, H4C12, HLA-DRB1, HNRNPA1, HNRNPC, HNRNPH1, HSF4, HSPA8, HSPB1, HSPB6, ITM2B, JUNB, KLF13, LENG8, LTBP4, MAN2C1, MBP, MGP, MIB2, MIF, MYL6, MYO15B, NCOR2, NDRG1, NDUFA1, NPDC1, NPIPA6, NPIPA9, NR4A1, OAZ1, PEBP1, PER1, PKD1, PKM, PPIA, PSAP, RACK1, RERE, SH2B3, SPARC, SPON2, SREBF1, TCEAL4, TMSB10, TMSB4X, TNK2, TNS2, TPT1, TRIM3, TUBA1B, WDR1 and ZNF414 A composition for predicting cancer prognosis, comprising a preparation measuring the expression level of at least one gene selected from the group or a protein encoded thereby.

2. In paragraph 1, The composition comprises ACLY, ACOX1, ACSL4, ACVR1, AKT1, AXIN1, AXIN2, BCL2L1, BMP4, BMPR1A, BRCA1, BTK, CASP3, CASP8, CASP9, CCNA2, CCNB2, CCND1, CCR5, CD40, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CS, CTNNB1, CXCR4, DVL1, DVL2, EP300, FYN, GADD45A, GNB1, GPX4, GRB2, GSK3B, H3C12, H3C13, H4C6, HMOX1, HRAS, IKBKB, IKBKG, IL1B, IL6, INS, JAG1, JAK1, JAK2, JAK3, JUN, KRAS, A composition further comprising an agent for measuring the expression level of at least one gene or a protein encoded by the gene selected from the group consisting of LCK, LEF1, LPCAT3, LYN, MAML1, MAML2, MAML3, MAPK1, MAPK3, MCM7, MLST8, MTOR, NCOA4, NFKB1, NFKBIA, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRAS, PIK3CA, PIK3R1, PLCG2, PTEN, RBPJ, RELA, RHEB, RHOA, RPTOR, RUNX1, SDHB, SMAD2, SMAD3, SMAD4, SOS1, SRC, STAT1, STAT3, SYK, TFRC, TGFB1, TNF, TNFRSF1A, TP53, TRAF6, TSC2 and TYK2.

3. In paragraph 2, The ACTB, H3-3B, ACLY, ACOX1, ACSL4, ACVR1, AKT1, AXIN1, AXIN2, BCL2L1, BMP4, BMPR1A, BRCA1, BTK, CASP3, CASP8, CASP9, CCNA2, CCNB2, CCND1, CCR5, CD40, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CS, CTNNB1, CXCR4, DVL1, DVL2, EP300, FYN, GADD45A, GNB1, GPX4, GRB2, GSK3B, H3C12, H3C13, H4C6, HMOX1, HRAS, IKBKB, IKBKG, IL1B, IL6, INS, JAG1, JAK1, JAK2, JAK3, A composition characterized in that the JUN, KRAS, LCK, LEF1, LPCAT3, LYN, MAML1, MAML2, MAML3, MAPK1, MAPK3, MCM7, MLST8, MTOR, NCOA4, NFKB1, NFKBIA, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRAS, PIK3CA, PIK3R1, PLCG2, PTEN, RBPJ, RELA, RHEB, RHOA, RPTOR, RUNX1, SDHB, SMAD2, SMAD3, SMAD4, SOS1, SRC, STAT1, STAT3, SYK, TFRC, TGFB1, TNF, TNFRSF1A, TP53, TRAF6, TSC2 and TYK2 genes are signal transduction pathway related genes.

4. In paragraph 3, The above signal transduction pathways include the Adipogenesis pathway, the Apoptosis pathway Ⅱ, the E2F targets pathway, the Estrogen Response Early pathway, the Estrogen Response Late pathway, the NOTCH signaling pathway, the WNT-Beta Catenin signaling pathway, the MAPK signaling pathway, the ErbB signaling pathway, the Ras signaling pathway, the Rap1 signaling pathway, the Chemokine signaling pathway, the NF-kappa B signaling pathway, the Cell cycle pathway, the p53 signaling pathway, the mTOR signaling pathway. PI3K-Akt signaling pathway, Apoptosis pathway Ⅰ, Ferroptosis pathway, Necroptosis pathway, Cellular senescence pathway, Wnt signaling pathway, Notch signaling pathway, TGF-beta signaling pathway, JAK-STAT signaling pathway, T cell receptor signaling pathway,A composition, wherein at least one signal transduction pathway is selected from the group consisting of the B cell receptor signaling pathway, the TNF signaling pathway, the Transcriptional misregulation pathway in cancer, the Prostate cancer pathway, the ESR Mediated signaling pathway, the Estrogen Dependent gene expression pathway, and the Androgen Receptor Network pathway in Prostate cancer.

5. In paragraph 1, A composition wherein the cancer is at least one selected from the group consisting of prostate cancer, breast cancer, mammary cancer, glioma, thyroid cancer, lung cancer, liver cancer, pancreatic cancer, head and neck cancer, stomach cancer, colon cancer, urothelial cancer, kidney cancer, testicular cancer, penile cancer, uterine cancer, cervical cancer, endometrial cancer, cervical cancer, fallopian tube cancer, vaginal cancer, ovarian cancer, melanoma, skin cancer, blood cancer, bone cancer, skin cancer, brain cancer, endocrine cancer, parathyroid cancer, ureteral cancer, urethral cancer, bronchial cancer, bladder cancer, bone marrow cancer, leukemia, brain tumor, intestinal cancer, esophageal cancer, Ewing's sarcoma, tongue cancer, lymphoma, kaposi sarcoma, mesothelioma, multiple myeloma, neuroblastoma, osteosarcoma, and retinoblastoma.

6. A kit for predicting cancer prognosis comprising a composition according to any one of claims 1 to 5.

7. In a biological sample isolated from the subject, ALDH1A3, ANO7, CRACR2B, ENO1, EPS8L1, HSPA1A, ITGB4, KLK3, KLK4, MALAT1, NPM1, PLA2G2A, SLC45A3, SPINT2, ZFP36, ACTA2, ACTB, ACTG1, ALDOA, ARID1A, B2M, BCAM, C3, CD63, CHCHD10, CKB, CLU, COL1A2, COL6A1, COX6A1, CST3, DDT, DENND2B, EEF1A1, EEF1G, EEF2, EGR1, EHMT1, FAM193B, FAU, FOS, FOSB, FTH1, FTL, GAPDH, GBF1, GFAP, GSTP1, H2AJ, H2BC12, H3-3A, H3-3B, At least one gene selected from the group consisting of H4C12, HLA-DRB1, HNRNPA1, HNRNPC, HNRNPH1, HSF4, HSPA8, HSPB1, HSPB6, ITM2B, JUNB, KLF13, LENG8, LTBP4, MAN2C1, MBP, MGP, MIB2, MIF, MYL6, MYO15B, NCOR2, NDRG1, NDUFA1, NPDC1, NPIPA6, NPIPA9, NR4A1, OAZ1, PEBP1, PER1, PKD1, PKM, PPIA, PSAP, RACK1, RERE, SH2B3, SPARC, SPON2, SREBF1, TCEAL4, TMSB10, TMSB4X, TNK2, TNS2, TPT1, TRIM3, TUBA1B, WDR1 and ZNF414, or A method for providing information for predicting cancer prognosis, comprising the step of measuring the expression level of a protein encoded thereby.

8. In paragraph 7, The above method is ACLY, ACOX1, ACSL4, ACVR1, AKT1, AXIN1, AXIN2, BCL2L1, BMP4, BMPR1A, BRCA1, BTK, CASP3, CASP8, CASP9, CCNA2, CCNB2, CCND1, CCR5, CD40, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CS, CTNNB1, CXCR4, DVL1, DVL2, EP300, FYN, GADD45A, GNB1, GPX4, GRB2, GSK3B, H3C12, H3C13, H4C6, HMOX1, HRAS, IKBKB, IKBKG, IL1B, IL6, INS, JAG1, JAK1, JAK2, JAK3, JUN, KRAS, A method further comprising the step of measuring the expression level of at least one gene or a protein encoded by the gene selected from the group consisting of LCK, LEF1, LPCAT3, LYN, MAML1, MAML2, MAML3, MAPK1, MAPK3, MCM7, MLST8, MTOR, NCOA4, NFKB1, NFKBIA, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRAS, PIK3CA, PIK3R1, PLCG2, PTEN, RBPJ, RELA, RHEB, RHOA, RPTOR, RUNX1, SDHB, SMAD2, SMAD3, SMAD4, SOS1, SRC, STAT1, STAT3, SYK, TFRC, TGFB1, TNF, TNFRSF1A, TP53, TRAF6, TSC2 and TYK2.

9. In paragraph 7, A method according to claim 1, wherein the cancer is at least one selected from the group consisting of prostate cancer, breast cancer, mammary cancer, glioma, thyroid cancer, lung cancer, liver cancer, pancreatic cancer, head and neck cancer, stomach cancer, colon cancer, urothelial cancer, kidney cancer, testicular cancer, penile cancer, uterine cancer, cervical cancer, endometrial cancer, cervical cancer, fallopian tube cancer, vaginal cancer, ovarian cancer, melanoma, skin cancer, blood cancer, bone cancer, skin cancer, brain cancer, endocrine cancer, parathyroid cancer, ureteral cancer, urethral cancer, bronchial cancer, bladder cancer, bone marrow cancer, leukemia, brain tumor, intestinal cancer, esophageal cancer, Ewing's sarcoma, tongue cancer, lymphoma, kaposi sarcoma, mesothelioma, multiple myeloma, neuroblastoma, osteosarcoma, and retinoblastoma. 10.ALDH1A3, ANO7, CRACR2B, ENO1, EPS8L1, HSPA1A, ITGB4, KLK3, KLK4, MALAT1, NPM1, PLA2G2A, SLC45A3, SPINT2, ZFP36, ACTA2, ACTB, ACTG1, ALDOA, ARID1A, B2M, BCAM, C3, CD63, CHCHD10, CKB, CLU, COL1A2, COL6A1, COX6A1, CST3, DDT, DENND2B, EEF1A1, EEF1G, EEF2, EGR1, EHMT1, FAM193B, FAU, FOS, FOSB, FTH1, FTL, GAPDH, GBF1, GFAP, GSTP1, H2AJ, H2BC12, H3-3A, H3-3B, H4C12, HLA-DRB1, HNRNPA1, HNRNPC, HNRNPH1, HSF4, HSPA8, HSPB1, HSPB6, ITM2B, JUNB, KLF13, LENG8, LTBP4, MAN2C1, MBP, MGP, MIB2, MIF, MYL6, MYO15B, NCOR2, NDRG1, NDUFA1, NPDC1, NPIPA6, NPIPA9, NR4A1, OAZ1, PEBP1, PER1, PKD1, PKM, PPIA, PSAP, RACK1, RERE, SH2B3, SPARC, SPON2, SREBF1, TCEAL4, TMSB10, TMSB4X, TNK2, TNS2, TPT1, TRIM3, TUBA1B, WDR1 and ZNF414 A composition for predicting the possibility of cancer metastasis or recurrence, comprising a preparation measuring the expression level of at least one gene selected from the group or a protein encoded thereby.

11. In paragraph 10, The composition comprises ACLY, ACOX1, ACSL4, ACVR1, AKT1, AXIN1, AXIN2, BCL2L1, BMP4, BMPR1A, BRCA1, BTK, CASP3, CASP8, CASP9, CCNA2, CCNB2, CCND1, CCR5, CD40, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CS, CTNNB1, CXCR4, DVL1, DVL2, EP300, FYN, GADD45A, GNB1, GPX4, GRB2, GSK3B, H3C12, H3C13, H4C6, HMOX1, HRAS, IKBKB, IKBKG, IL1B, IL6, INS, JAG1, JAK1, JAK2, JAK3, JUN, KRAS, A composition further comprising an agent for measuring the expression level of at least one gene or a protein encoded by the gene selected from the group consisting of LCK, LEF1, LPCAT3, LYN, MAML1, MAML2, MAML3, MAPK1, MAPK3, MCM7, MLST8, MTOR, NCOA4, NFKB1, NFKBIA, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRAS, PIK3CA, PIK3R1, PLCG2, PTEN, RBPJ, RELA, RHEB, RHOA, RPTOR, RUNX1, SDHB, SMAD2, SMAD3, SMAD4, SOS1, SRC, STAT1, STAT3, SYK, TFRC, TGFB1, TNF, TNFRSF1A, TP53, TRAF6, TSC2 and TYK2.

12. A kit for predicting the possibility of cancer metastasis or recurrence, comprising a composition according to any one of claims 10 and 11.

13. In a biological sample isolated from the subject, ALDH1A3, ANO7, CRACR2B, ENO1, EPS8L1, HSPA1A, ITGB4, KLK3, KLK4, MALAT1, NPM1, PLA2G2A, SLC45A3, SPINT2, ZFP36, ACTA2, ACTB, ACTG1, ALDOA, ARID1A, B2M, BCAM, C3, CD63, CHCHD10, CKB, CLU, COL1A2, COL6A1, COX6A1, CST3, DDT, DENND2B, EEF1A1, EEF1G, EEF2, EGR1, EHMT1, FAM193B, FAU, FOS, FOSB, FTH1, FTL, GAPDH, GBF1, GFAP, GSTP1, H2AJ, H2BC12, H3-3A, H3-3B, At least one gene selected from the group consisting of H4C12, HLA-DRB1, HNRNPA1, HNRNPC, HNRNPH1, HSF4, HSPA8, HSPB1, HSPB6, ITM2B, JUNB, KLF13, LENG8, LTBP4, MAN2C1, MBP, MGP, MIB2, MIF, MYL6, MYO15B, NCOR2, NDRG1, NDUFA1, NPDC1, NPIPA6, NPIPA9, NR4A1, OAZ1, PEBP1, PER1, PKD1, PKM, PPIA, PSAP, RACK1, RERE, SH2B3, SPARC, SPON2, SREBF1, TCEAL4, TMSB10, TMSB4X, TNK2, TNS2, TPT1, TRIM3, TUBA1B, WDR1 and ZNF414, or A method for providing information for predicting the possibility of cancer metastasis or recurrence, comprising the step of measuring the expression level of a protein encoded thereby.

14. In paragraph 13, The above method is ACLY, ACOX1, ACSL4, ACVR1, AKT1, AXIN1, AXIN2, BCL2L1, BMP4, BMPR1A, BRCA1, BTK, CASP3, CASP8, CASP9, CCNA2, CCNB2, CCND1, CCR5, CD40, CDK1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CS, CTNNB1, CXCR4, DVL1, DVL2, EP300, FYN, GADD45A, GNB1, GPX4, GRB2, GSK3B, H3C12, H3C13, H4C6, HMOX1, HRAS, IKBKB, IKBKG, IL1B, IL6, INS, JAG1, JAK1, JAK2, JAK3, JUN, KRAS, A method further comprising the step of measuring the expression level of at least one gene or a protein encoded by the gene selected from the group consisting of LCK, LEF1, LPCAT3, LYN, MAML1, MAML2, MAML3, MAPK1, MAPK3, MCM7, MLST8, MTOR, NCOA4, NFKB1, NFKBIA, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NRAS, PIK3CA, PIK3R1, PLCG2, PTEN, RBPJ, RELA, RHEB, RHOA, RPTOR, RUNX1, SDHB, SMAD2, SMAD3, SMAD4, SOS1, SRC, STAT1, STAT3, SYK, TFRC, TGFB1, TNF, TNFRSF1A, TP53, TRAF6, TSC2 and TYK2.

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