Composition for diagnosing prostate cancer using methylation level of cpg regions and use thereof

Abstract

The present invention relates to a composition, a kit, and an information providing method for diagnosing prostate cancer using the methylation level of CpG regions. By using the composition, the kit, and the information providing method according to the present invention, it is possible to distinguish between prostate cancer and benign prostatic hyperplasia, and thereby diagnose prostate cancer not only quickly and accurately, but also early.

Description

Composition for diagnosing prostate cancer using CpG domain methylation levels and uses thereof

[0001] This invention relates to a composition for diagnosing prostate cancer using CpG domain methylation levels and the use thereof.

[0002] Prostate cancer is one of the most common cancers in men, and early detection significantly impacts treatment outcomes. Conversely, while Benign Prostatic Hyperplasia (BPH) is a benign condition, its symptoms are similar to those of prostate cancer, making clinical differentiation difficult. Currently, both conditions are diagnosed clinically using PSA (Prostate-Specific Antigen) testing; however, the low specificity of PSA can lead to unnecessary additional testing and overtreatment. Consequently, there is a demand for diagnostic methods based on more specific and sensitive molecular markers. Furthermore, tissue biopsy, the conventional diagnostic method for prostate cancer, is an invasive procedure that can place a burden on patients.

[0003] Accordingly, the inventors have discovered a marker capable of diagnosing prostate cancer with high accuracy while minimizing invasiveness using the patient's cell-free DNA (hereinafter, cfDNA), thereby completing the present invention.

[0004] One aspect provides a composition for diagnosing prostate cancer comprising a preparation for measuring the methylation level of one or more gene CpG regions selected from the group consisting of TNRC18 (Trinucleotide Repeat Containing 18), C2orf88 (Chromosome 2 Open Reading Frame 88), LDLRAD2 (Low-Density Lipoprotein Receptor-Related Protein-Associated Protein 2), TMEM241 (Transmembrane Protein 241), CITED4 (Cbp / p300-interacting transactivator with Glu / Asp-rich carboxy-terminal domain 4), ITGA9 (Integrin Subunit Alpha 9), FGF12 (Fibroblast Growth Factor 12), ZFP64 (Zinc Finger Protein 64), NODAL (Nodal Growth Differentiation Factor), and SLCO4A1-AS1 (SLCO4A1 Antisense RNA 1).

[0005] Another aspect provides a prostate cancer diagnostic kit comprising the above composition.

[0006] Another aspect provides a method for providing information for the diagnosis of prostate cancer, comprising: (a) a step of measuring the level of CpG region methylation of a gene in nucleic acid isolated from a biological sample of an individual; and (b) a step of comparing the measured level of CpG region methylation of the gene with the methylation level of a control sample of a patient with benign prostatic hyperplasia, wherein the gene is one or more selected from the group consisting of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, NODAL, and SLCO4A1-AS1.

[0007] Another aspect provides a method for providing information for diagnosing prostate cancer, comprising, in a computer-assisted system: (a) acquiring data on the CpG region methylation level of a gene in nucleic acids isolated from a biological sample; (b) inputting the acquired CpG region methylation level data into a pre-trained machine learning model; and (c) generating information for diagnosing prostate cancer based on the output value of the machine learning model, wherein the gene is one or more selected from the group consisting of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, NODAL, and SLCO4A1-AS1.

[0008] Another aspect provides a computer-readable recording medium containing a computer program for performing the above method.

[0009] Another aspect provides a method for treating prostate cancer comprising: (a) measuring the level of CpG domain methylation of a gene in nucleic acid isolated from a biological sample of an individual; (b) comparing the measured level of CpG domain methylation of the gene with the methylation level of a control sample of a patient with benign prostatic hyperplasia; (c) selecting an individual in which the measured level of CpG domain methylation of the gene is increased, i.e., hypermethylated, compared to the methylation level of the control sample of a patient with benign prostatic hyperplasia; and (d) administering cancer immunotherapy to the selected individual, wherein the gene is one or more selected from the group consisting of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, NODAL, and SLCO4A1-AS1.

[0010]

[0011] Other objects and advantages of this application will become more apparent from the following detailed description, together with the appended claims and drawings. Anything not described in this specification is omitted, as it can be sufficiently recognized and inferred by those skilled in the art of this application or a similar art field.

[0012] Each description and embodiment disclosed in this application may be applied to each other description and embodiment. That is, all combinations of the various elements disclosed in this application fall within the scope of this application. Furthermore, the scope of this application should not be considered limited by the specific descriptions provided below.

[0013] One aspect provides a composition for diagnosing prostate cancer comprising a preparation for measuring the methylation level of one or more gene CpG regions selected from the group consisting of TNRC18 (Trinucleotide Repeat Containing 18), C2orf88 (Chromosome 2 Open Reading Frame 88), LDLRAD2 (Low-Density Lipoprotein Receptor-Related Protein-Associated Protein 2), TMEM241 (Transmembrane Protein 241), CITED4 (Cbp / p300-interacting transactivator with Glu / Asp-rich carboxy-terminal domain 4), ITGA9 (Integrin Subunit Alpha 9), FGF12 (Fibroblast Growth Factor 12), ZFP64 (Zinc Finger Protein 64), NODAL (Nodal Growth Differentiation Factor), and SLCO4A1-AS1 (SLCO4A1 Antisense RNA 1).

[0014] The term "methylation" may refer to a change in gene expression patterns caused by the attachment of a methyl group to a base, and specifically, it may occur in cytosine in the CpG region of a base sequence.

[0015] The term "CpG region" may be used interchangeably with "CpG site" and refers to a genomic region where CpG dinucleotides appear at a high frequency. In the above CpG, C represents cytosine and G represents guanine, while p may denote the phosphodiester bond between cytosine and guanine. In many cases, CpG regions are found in the gene promoter or the 5' exon. The promoters of most human genes are located in CpG regions and exist in an unmethylated state.

[0016] The term "CpG domain methylation" refers to epigenetic methylation modifications of DNA occurring at the cytosine groups of the CpG domain. Mammalian DNA contains a base called 5-methylcytosine (5-mC), in which a methyl group is attached to the fifth carbon of a cytosine ring. Methylation of 5-methylcytosine occurs exclusively at the cytosine groups of CpGs, and methylation of the CpG domain interferes with the binding of transcription factors, which can suppress the expression of specific genes. Conversely, demethylation or hypomethylation may increase the expression of specific genes. Additionally, it inhibits the expression of transposons and genomic repetitive sequences. Since DNA methylation is detected even in the early stages of cancer development prior to the appearance of somatic mutations, it is useful for the early diagnosis of cancer.

[0017] The term "measurement of methylation level" refers to measuring the degree of methylation of a nucleic acid sequence, specifically measuring the methylation level occurring in the cytosine group of the CpG region.

[0018] In one embodiment, the methylation level of the CpG region of the gene may be increased, i.e., hypermethylated, in patients with prostate cancer compared to patients with benign prostatic hyperplasia, who are the control group. For example, the methylation level may include a similar level compared to patients with benign prostatic hyperplasia, or an increase of 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900%, and 1000% or more. The hypermethylation may be specific to prostate cancer.

[0019] According to one embodiment, the methylation levels of CpG regions in the TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, NODAL, and SLCO4A1-AS1 genes of a prostate cancer patient were quantified and, through comparison with a control group of a patient with benign prostatic hyperplasia, hypermethylated regions specific to prostate cancer were identified, thereby confirming that the CpG regions of the said genes can be utilized as biomarkers for the diagnosis of prostate cancer.

[0020] In one embodiment, the CpG region of the TNRC18 gene may be the 5396809th on chromosome 7, the CpG region of the C2orf88 gene may be one or more selected from the group consisting of the 191045615th and 191045611th on chromosome 2, the CpG region of the LDLRAD2 gene may be the 22141161st on chromosome 1, the CpG region of the TMEM241 gene may be the 20911499th on chromosome 18, the CpG region of the CITED4 gene may be one or more selected from the group consisting of the 41327272th and 41327274th on chromosome 1, the CpG region of the ITGA9 gene may be the 37494252nd on chromosome 3, and the CpG region of the FGF12 gene is It may be the 192125900th on chromosome 3, and the CpG region of the ZFP64 gene may be one or more selected from the group consisting of the 50721715th and 50721518th on chromosome 20, the CpG region of the NODAL gene may be the 72201209th on chromosome 10, and the CpG region of the SLCO4A1-AS1 gene may be the 61297922nd on chromosome 20.

[0021] In one embodiment, the composition may include the following marker or a combination of markers:

[0022] 1) Chr7; 5396809

[0023] 2) Chr7; 5396809 and Chr2; 191045615

[0024] 3) Chr7; 5396809, Chr2; 191045615 and Chr2; 191045611

[0025] 4) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611 and Chr1; 22141161

[0026] 5) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161 및 Chr18; 20911499

[0027] 6) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161, Chr18; 20911499 및 Chr1; 41327272

[0028] 7) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161, Chr18; 20911499, Chr1; 41327272 및 Chr1; 41327274

[0029] 8) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161, Chr18; 20911499, Chr1; 41327272, Chr1; 41327274 및 Chr3; 37494252

[0030] 9) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161, Chr18; 20911499, Chr1; 41327272, Chr1; 41327274, Chr3; 37494252 및 Chr3; 192125900

[0031] 10) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161, Chr18; 20911499, Chr1; 41327272, Chr1; 41327274, Chr3; 37494252, Chr3; 192125900 및 Chr20; 50721715

[0032] 11) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161, Chr18; 20911499, Chr1; 41327272, Chr1; 41327274, Chr3; 37494252, Chr3; 192125900, Chr20; 50721715 및 Chr20; 50721518

[0033] 12) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161, Chr18; 20911499, Chr1; 41327272, Chr1; 41327274, Chr3; 37494252, Chr3; 192125900, Chr20; 50721715, Chr20; 50721518 및 Chr10; 72201209

[0034] 13) Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161, Chr18; 20911499, Chr1; 41327272, Chr1; 41327274, Chr3; 37494252, Chr3; 192125900, Chr20; 50721715, Chr20; 50721518, Chr10; 72201209 및 Chr20; 61297922.

[0035] In the present invention, the nucleotide sequences of the human genome chromosome regions are expressed according to the February 2009 Human reference sequence (GRCh37); however, the specific sequences of the human genome chromosome regions may be slightly modified as genome sequence research results are updated, and the representation of the human genome chromosome regions in the present invention may differ as a result of such modifications. Therefore, it is evident that even if the human reference sequence is updated after the filing date of the present invention and the representation of the human genome chromosome regions expressed according to the February 2009 Human reference sequence (GRCh37) changes from the present invention, the scope of the present invention extends to the modified human genome chromosome regions. Such modifications are matters that anyone with ordinary knowledge in the technical field to which the present invention belongs can easily recognize.

[0036] In one embodiment, the formulation may include a primer pair for CpG region amplification or a CpG region-specific probe.

[0037] The term "primer" refers to a nucleic acid sequence having a free 3' hydroxyl group, capable of forming base pairs with a template complementary to a specific base sequence, and acting as a starting point for template strand replication. The term "amplification" refers to increasing the copy number of a target sequence or its complementary sequence, specifically increasing the copy number of a sequence containing a methylated CpG region of the said gene.

[0038] The primer can initiate DNA synthesis in the presence of a reagent for polymerization (i.e., DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates at an appropriate buffer solution and temperature. The PCR conditions and the lengths of the sense and antisense primers can be appropriately selected according to techniques known in the art. The primer may have 10 to 100, 15 to 100, 10 to 80, 10 to 50, 10 to 30, 10 to 20, 15 to 80, 15 to 50, 15 to 30, 15 to 20, 20 to 100, 20 to 80, 20 to 50, or 20 to 30 nt.

[0039] The term "probe" refers to a nucleic acid fragment, such as RNA or DNA, capable of forming a specific binding with a nucleic acid; specifically, it can specifically bind to the CpG region of a target gene.

[0040] The probe can be labeled to confirm the presence or absence of a specific nucleic acid sequence. The probe may be manufactured in the form of an oligonucleotide probe, a single-strand DNA probe, a double-strand DNA probe, an RNA probe, etc. The selection of an appropriate probe and hybridization conditions may be appropriately selected according to techniques known in the relevant art field. The probe may have 10 to 100, 15 to 100, 10 to 80, 10 to 50, 10 to 30, 10 to 20, 15 to 80, 15 to 50, 15 to 30, 15 to 20, 20 to 100, 20 to 80, 20 to 50, or 20 to 30 nt.

[0041] The primers and probes can preferably be designed according to the sequence of the CpG region measuring the methylation level, and the primers and probes can be designed specifically for methylation.

[0042] The above primer or probe may be chemically synthesized using a phosphoramidite solid support synthesis method or other widely known methods. Additionally, such nucleic acid sequences may be modified through various methods known in the art. Examples of such modifications may include methylation, capping, substitution with one or more homologues of natural nucleotides, or modification between nucleotides, for example, modification to an uncharged linkage (e.g., methyl phosphonate, phosphotriester, phosphoroamidate, carbamate, etc.) or a charged linkage (e.g., phosphorothioate, phosphorodithioate, etc.).

[0043] Additionally, the probe may be modified using a label that can provide a detectable signal directly or indirectly. Examples of such labels may include radioisotopes, fluorescent materials, or biotin. Examples of such fluorescent materials include, but are not limited to, FAM (6-Carboxyfluorescein), TET (2',7'-dichloro-6-carboxy-4,7-dichlorofluorescein), Texas Red, 6-JOE (6-carboxy-4',5'-dichloro-2',7'-dimethoxyfluorescein), HEX (2',4',5',7'-tetrachloro-6-carboxy-4,7-dichlorofluorescein), Cy3 (Cyanine 3), Cy5, rhodamine, or VIC. In addition, the probe may be labeled with a fluorescent substance at the 5' end and a quenching substance at the 3' end for visualization using the Fluorescent Resonance Energy Transfer (FRET) principle. The quenching substances may include 6-TAMRA (6-carboxytetramethylrhodamine), BHQ (Black Hole Quencher)-1, BHQ-2, and BHQ-3, but are not limited thereto. In one embodiment, the 5' end of the probe may be labeled with Cy5 and FAM, and the quenching substances may be labeled with BHQ-1 and BHQ-2 to quantify the methylation level of the CpG region.

[0044] The probe can quantify the methylation level of the CpG region from the PCR amplification product through the primer pair, and the methylation level of the gene CpG region can be measured with the primer pair and the probe.

[0045] The above primer pairs are, for example, teppo 5396809 on chromosome 7 for TNRC18, teppo 191045615 or 191045611 on chromosome 2 for C2orf88, teppo 22141161 on chromosome 1 for LDLRAD21, teppo 20911499 on chromosome 18 for TMEM241, teppo 41327272 or 41327274 on chromosome 1 for CITED4, teppo 37494252 on chromosome 3 for ITGA9, teppo 192125900 on chromosome 3 for FGF12, teppo 50721715 or 50721518 on chromosome 20 for ZFP64, and teppo 72201209 on chromosome 10 for NODAL In the case of SLCO4A1-AS1, the methylation level of the CpG region can be measured by amplifying the region containing the 61297922nd position of chromosome 20.

[0046] The above probe is, for example, 5396809 on chromosome 7 for TNRC18, 191045615 or 191045611 on chromosome 2 for C2orf88, 22141161 on chromosome 1 for LDLRAD21, 20911499 on chromosome 18 for TMEM241, 41327272 or 41327274 on chromosome 1 for CITED4, 37494252 on chromosome 3 for ITGA9, 192125900 on chromosome 3 for FGF12, 50721715 or 50721518 on chromosome 20 for ZFP64, and 72201209 on chromosome 10 for NODAL, SLCO4A1-AS1 specifically binds to the 61297922nd position of chromosome 20, allowing for the measurement of methylation levels in CpG regions.

[0047] In addition, the above preparation may include one or more selected from the group consisting of a compound or salt thereof that selectively modifies a CpG region to measure methylation levels, a methylation-sensitive restriction enzyme, a methylation-specific binding protein, a methylation-specific binding antibody or aptamer, a methylation-sensitive restriction endonuclase, a sequencing primer, a sequencing biolysis primer, and a sequencing bioligation primer.

[0048] Compounds that selectively modify the above CpG region can modify unmethylated cytosine or methylated cytosine. For example, bisulfite that modifies unmethylated cytosine induces a deamination reaction that converts only unmethylated cytosine into uracil without affecting methylated cytosine. A TET protein (ten-eleven translocation protein) that modifies methylated cytosine converts only methylated cytosine into uracil, enabling efficient detection.

[0049] The above-mentioned methylation-sensitive restriction enzyme is a restriction enzyme capable of specifically detecting methylation of the CpG region, and may be a restriction enzyme containing CG as its recognition site. Examples include, but are not limited to, SmaI, SacII, EagI, HpaII, MspI, BssHII, BstUI, NotI, etc. Depending on the methylation or demethylation of the cytosine at the restriction enzyme recognition site, the cleavage by the restriction enzyme varies, and this can be detected through PCR or Southern Blot analysis. Other methylation-sensitive restriction enzymes other than the above-mentioned restriction enzyme are well known in the art.

[0050] In one embodiment, nucleic acids isolated from blood, plasma, or serum may be used.

[0051] The nucleic acid used to detect CpG methylation is DNA, but is not limited thereto. Samples containing DNA or RNA containing DNA and mRNA may be used, wherein the DNA or RNA may be single-stranded or double-stranded, or samples containing DNA-RNA hybrids may be used. Nucleic acid mixtures may also be used. The nucleic acid sequence to be detected does not need to be a nucleic acid existing in a pure form, and the nucleic acid may be a small fraction of a large molecule, such as a portion of whole genomic DNA.

[0052] In one embodiment, the isolated nucleic acid may be cfDNA.

[0053] The term "cfDNA (cell-free DNA)" refers to genomic fragments of varying lengths present in the blood, primarily consisting of chromatin regions that are not protected by histone proteins. In the case of cancer patients, circulating tumor DNA (ctDNA), which is a DNA fragment derived from tumor cells, is mixed with cfDNA. In this case, the size of cfDNA is not limited to this range but can be approximately 80 bp to 10 kbp, approximately 100 bp to 1 kbp, or approximately 120 bp to 500 bp. Additionally, cfDNA can be approximately 150 bp to 200 bp in size.

[0054] According to one embodiment, the level of CpG region methylation of a gene in cfDNA was measured based on a liquid sample isolated from an individual, and the methylation marker provided information regarding the diagnosis of prostate cancer with excellent sensitivity, specificity, and / or accuracy. In this way, non-invasive diagnosis is made possible in that excellent diagnostic accuracy is demonstrated using a liquid sample.

[0055] In one embodiment, the composition may include a plurality of agents for measuring the methylation levels of at least two gene CpG regions selected from the group consisting of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, NODAL, and SLCO4A1-AS1. The composition may, for example, include agents for measuring the methylation levels of the CpG regions of the TNRC18 and C2orf88 genes. The composition may, for example, include agents for measuring the methylation levels of the CpG regions of the TNRC18, C2orf88, and LDLRAD2 genes. The composition may, for example, include agents for measuring the methylation levels of the CpG regions of the TNRC18, C2orf88, LDLRAD2, and TMEM241 genes. The above composition may include, for example, a preparation for measuring the gene CpG region methylation levels of TNRC18, C2orf88, LDLRAD2, TMEM241, and CITED4. The above composition may include a preparation for measuring the gene CpG region methylation levels of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, and ITGA9. The above composition may include a preparation for measuring the gene CpG region methylation levels of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, and FGF12. The above composition may include a preparation for measuring the gene CpG region methylation levels of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, and ZFP64. The above composition may include a preparation for measuring the gene CpG region methylation levels of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, and NODAL.The above composition may include a preparation for measuring the methylation levels of gene CpG regions of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, NODAL, and SLCO4A1-AS1.

[0056] According to one embodiment, when the aforementioned methylation marker combination is used, the sensitivity and accuracy of prostate cancer diagnosis are improved compared to a single methylation marker. Therefore, prostate cancer can be diagnosed more accurately and quickly by combining prostate cancer methylation markers.

[0057] Another aspect provides a prostate cancer diagnostic kit comprising the above composition.

[0058] In the above-mentioned prostate cancer diagnostic kit, the terms and elements mentioned that are identical to those already mentioned are as described above.

[0059] The term "Diagnosis" may mean confirming the existence or characteristics of a pathological condition. For the purposes of the present invention, the diagnosis may be to confirm whether prostate cancer has developed, and this includes distinguishing between prostate cancer and benign prostatic hyperplasia.

[0060] The above-described prostate cancer diagnostic kit is effective in diagnosing prostate cancer by measuring the gene CpG region methylation level by including the above-described composition.

[0061] The above kit may further include primer pairs, probes, or antisense oligonucleotides for the diagnosis of prostate cancer, as well as one or more other component compositions, solutions, or devices suitable for the analysis method.

[0062] Another aspect provides a method for providing information for the diagnosis of prostate cancer, comprising: (a) a step of measuring the level of CpG region methylation of a gene in nucleic acid isolated from a biological sample of an individual; and (b) a step of comparing the measured level of CpG region methylation of the gene with the methylation level of a control sample of a patient with benign prostatic hyperplasia, wherein the gene is one or more selected from the group consisting of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, NODAL, and SLCO4A1-AS1.

[0063] In the above method for providing information for the diagnosis of prostate cancer, the terms and elements mentioned that are identical to those already mentioned are as described above.

[0064] The term "individual" refers to an individual for whom the presence or absence of prostate cancer is to be confirmed or predicted. The individual may be a vertebrate, specifically a mammal, amphibian, reptile, bird, etc., and more specifically a mammal, for example, a human (Homo sapiens).

[0065] In one embodiment, the biological sample may be blood, plasma, or serum, and the isolated nucleic acid may be cfDNA.

[0066] In one embodiment, step (a) may include the step of treating a preparation for measuring the level of CpG region methylation of a gene, and the preparation may be a primer pair for amplifying the CpG region or a CpG region-specific probe.

[0067] In one embodiment, the step (a) of measuring the methylation level may be performed by a method selected from the group consisting of PCR, methylation specific PCR, real-time methylation specific PCR, PCR using a methylation DNA specific binding protein, DNA microarray, pyrosequencing, bisulfite sequencing, and next-generation sequencing (NGS).

[0068] In one embodiment, the step of (b) comparing methylation levels may be performed by an algorithm selected from the group consisting of random forest, logistic regression, support vector machine, decision tree, association rule mining, neural network, and deep learning.

[0069] In one embodiment, if the CpG region methylation level of the measured gene is increased, i.e., hypermethylated, compared to the methylation level of a control sample of a patient with benign prostatic hyperplasia, it can be determined that there is a high probability of developing prostate cancer or that the patient has prostate cancer.

[0070] In one embodiment, the information providing method may additionally include the step of (c) determining that the probability of developing prostate cancer is high or that the patient has developed prostate cancer when the level of methylation of the CpG region of the measured gene is increased, i.e., hypermethylated, compared to the level of methylation of a sample of a patient with benign prostatic hyperplasia who is a control group.

[0071] In addition, a threshold value may be established by pre-measuring the methylation level of the gene CpG region in a sample from a control group of patients with benign prostatic hyperplasia, and then making a determination by comparing that threshold value with the methylation level measured from the patient's sample. If a threshold value is established in advance by measuring the methylation level of the gene CpG region in a sample from a control group of patients with benign prostatic hyperplasia, information for diagnosing whether the patient has prostate cancer can be rapidly provided simply by measuring the methylation level of the gene CpG region in the patient's sample, without the need to measure the methylation level of the control group of patients with benign prostatic hyperplasia every time for each diagnosis.

[0072] Another aspect provides a method for providing information for diagnosing prostate cancer, comprising, in a computer-assisted system: (a) acquiring data on the CpG region methylation level of a gene in nucleic acids isolated from a biological sample; (b) inputting the acquired CpG region methylation level data into a pre-trained machine learning model; and (c) generating information for diagnosing prostate cancer based on the output value of the machine learning model, wherein the gene is one or more selected from the group consisting of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, NODAL, and SLCO4A1-AS1.

[0073] In the above method for providing information for the diagnosis of prostate cancer, the terms and elements mentioned that are identical to those already mentioned are as described above.

[0074] In step (a) above, the step of acquiring data on the CpG region methylation level may be performed by a method selected from the group consisting of enzymatic methylation sequencing (EM-seq), PCR, methylation specific PCR, real-time methylation specific PCR, PCR using methylation DNA-specific binding proteins, DNA microarray, pyrosequencing, bisulfite sequencing, and next-generation sequencing (NGS). The method may be performed as single-end sequencing, which performs sequencing at one end, or paired-end sequencing, which performs sequencing at both ends.

[0075] The data on the CpG region methylation level above refers to the data on the methylation level of a specific individual or entity.

[0076] Step (b) above is a step of inputting the CpG region methylation level data obtained in Step (a) above into a machine learning model.

[0077] In the present invention, any one of various types of machine learning models may be used. Alternatively, a combination of multiple models may be used. The machine learning model may be various models such as random forest, logistic regression, support vector machine, decision tree, association rule mining, neural network, and deep learning, or a combination thereof. The machine learning model may be configured to compare the input CpG region methylation level with a reference methylation level that is not from a cancer patient, and the reference methylation level may be selected according to the purpose, but is not limited thereto, may be a reference methylation level derived from a patient with benign prostatic hyperplasia.

[0078] A machine learning model can be trained using information on hypermethylation ratios through the comparison of CpG region methylation levels. The hypermethylated regions may be specific to prostate cancer.

[0079] Step (c) above is a step for generating information for diagnosing prostate cancer based on the output value produced by the machine learning model based on the methylation level input in Step (b), and can determine that the patient is a prostate cancer patient if the CpG region methylation level is more hypermethylated than the reference methylation level.

[0080] Another aspect provides a computer-readable recording medium containing a computer program for performing the above method.

[0081] In the above-mentioned recording medium, the terms and elements mentioned that are identical to those already mentioned are as described above.

[0082] The above method may be implemented in the form of software readable through various computer means and recorded on a computer-readable recording medium. Here, the recording medium may include program instructions, data files, data structures, etc., either individually or in combination. The program instructions recorded on the recording medium may be those specifically designed and configured for the method according to the above, or they may be those known to and available for use by a person skilled in the art of computer software.

[0083] For example, recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs (Compact Disk Read Only Memory) and DVDs (Digital Video Disks); magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM (Random Access Memory), and flash memory. Examples of program instructions may include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. Such hardware devices may be configured to operate as one or more software modules to perform the operation of the method according to the above, and vice versa.

[0084]

[0085] Another aspect provides a method for treating prostate cancer comprising: (a) measuring the level of CpG domain methylation of a gene in nucleic acid isolated from a biological sample of an individual; (b) comparing the measured level of CpG domain methylation of the gene with the methylation level of a group of patients with benign prostatic hyperplasia or a normal control sample; (c) selecting an individual in which the measured level of CpG domain methylation of the gene is increased, i.e., hypermethylated, compared to the methylation level of a control sample of patients with benign prostatic hyperplasia; and (d) administering cancer immunotherapy to the selected individual, wherein the gene is one or more selected from the group consisting of TNRC18, C2orf88, LDLRAD2, TMEM241, CITED4, ITGA9, FGF12, ZFP64, NODAL, and SLCO4A1-AS1.

[0086] The above treatment method can contribute to improving actual therapeutic efficacy by enabling early diagnosis of prostate cancer based on the methylation level of a specific gene's CpG region.

[0087] In the above-mentioned method for treating prostate cancer, the terms and elements mentioned that are identical to those already mentioned are as described above.

[0088] By using the composition, kit, and information provision method according to the present invention, prostate cancer can be diagnosed accurately and quickly, as well as at an early stage.

[0089] Figure 1 is a heatmap showing the methylation levels of CpG regions of methylation markers according to one pattern in prostate cancer patients (n=96) and control groups (n=99).

[0090] Figure 2 is a graph evaluating the performance of a model classifying prostate cancer and control groups by PSA level in terms of sensitivity, specificity, and accuracy.

[0091] Figure 3 is a graph evaluating the performance of a model that classifies prostate cancer and control groups using methylation markers according to daily patterns in terms of sensitivity, specificity, and accuracy.

[0092] The following examples will be explained in more detail. However, these examples are for illustrative purposes only and the scope of the present invention is not limited to these examples.

[0093]

[0094] Example 1. Plasma Separation and cfDNA Extraction

[0095] cfDNA was extracted from plasma to screen for methylation markers for the diagnosis of prostate cancer. Specifically, whole blood from prostate cancer patients was collected in cfDNA test tubes (Cell-free DNA BCT CE, Streck), and plasma was separated from the whole blood through two centrifugation steps. After centrifugation at 1,600 rcf for 10 minutes, only the supernatant plasma was separated, and after centrifugation at 3,000 rcf for 20 minutes, only the supernatant plasma was collected and stored in a new tube. Subsequently, cfDNA was extracted using a commercially available cfDNA extraction kit (QIAamp Circulating Nucleic Acid Kit, Qiagen) according to the manufacturer's manual. The quality of the cfDNA was verified using an automated electrophoresis device (4200 Tapestation, Agilent) and a fluorescence detector (Qubit 4 Fluorometer, Invitrogen).

[0096]

[0097] Example 2. Enzymatic Methyl-sequencing (EM-seq)

[0098] 2-1. Sequencing Library Production

[0099] Purified cfDNA was prepared into a methylation sequencing library using the NEBNext Enzymatic Methyl-seq Kit (New England Biolabs, Ipswich, MA, USA). Twist Biosciences' Targeted Methylation Workflow was used to target CpG methylation regions within the human genome corresponding to approximately 0.2 Mbp. This process was designed for precise analysis of methylation patterns and is optimized for detecting prostate cancer-specific methylation changes.

[0100]

[0101] 2-2. Sequencing

[0102] Each sequencing library was adjusted to a concentration of 2 nM and pooled into equal volumes. The pooled sequencing libraries underwent denaturation and cluster amplification according to the manufacturer's instructions (Illumina, San Diego, CA, USA). The cluster-amplified libraries were sequenced using the 150 bp paired-end method on an Illumina NovaSeq 6000 or NextSeq 550 platform, and to ensure data quality during the sequencing process, the data was monitored in real-time using RTA (Real-Time Analysis) v.1.12.4.2 or the latest version of the software.

[0103]

[0104] 2-3. Sequencing Data Processing

[0105] Sequencing data was precisely aligned to the human genome reference (hg19) using Bismark software (v0.22.3). All sequenced data were preprocessed using TrimmGalore (v0.6.6), which included removing an additional 10 bp from the 5' end of each read to remove low-quality sequences and minimize methylation bias. Duplicate reads were removed to reduce bias that may occur due to PCR amplification, and the methylation level of CpG sites within the target panel region was calculated using Bismark's methylation extractor function.

[0106]

[0107] Example 3. Establishment of a Prostate Cancer Diagnostic Model and Evaluation of Single Marker Performance

[0108] The methylation levels of CpG sites (n = 15,523) commonly found in both prostate cancer patients (n=96) and benign prostatic hyperplasia patients (n=99) were calculated, and CpG sites (n=2,277) with a difference in methylation levels (Fold Change) between the two diseases of 2 or more were extracted.

[0109] Based on this, a machine learning model (Random Forest) was utilized to analyze the cfDNA methylation patterns between the two diseases, and a model was established to classify patients with prostate cancer (n=96) and patients with benign prostatic hyperplasia (n=99). Out of a total of 195 samples, the data were separated into three sets (Train (n=108), Validation (n=28), Test (n=59)). Among the 2,277 CpG sites used for training, the CpG site with the highest Feature Importance was selected, and as a result, the single CpG marker shown in Table 1 was discovered.

[0110]

[0111]

[0112] Figure 1 is a heatmap showing the methylation levels of the CpG regions of selected methylation markers in patients with prostate cancer (n=96) and patients with benign prostatic hyperplasia (n=99). From the results in Figure 1, it was confirmed that the CpG regions of the selected methylation markers in prostate cancer were significantly hypermethylated compared to those in benign prostatic hyperplasia.

[0113]

[0114] Example 4. Performance evaluation of combination markers for prostate cancer diagnosis

[0115] Based on the single CpG marker discovered in Example 3 above, the diagnostic efficacy of combination markers for prostate cancer was evaluated. To this end, the efficacy of combination markers was evaluated by applying Marker 1 (5396809 CpG site in TNRC18) from Table 1 as an essential single marker, while sequentially adding Markers 2 through 13 from Table 1. The specific diagnostic efficacy of the combination or classification model is shown in Table 2. The performance of each classification model was verified through a machine learning model (Random Forest), and performance indicators such as Accuracy, ROC-AUC, Precision, Recall, and F1 Score were calculated. According to Table 2, it was confirmed that marker combinations exhibited higher AUC values ​​than single methylation markers. In particular, it was confirmed that combining three or more methylation markers resulted in an AUC value of 0.9 or higher.

[0116]

[0117]

[0118] Example 5. Comparison of efficacy with conventional prostate cancer diagnostic technology

[0119] We intended to evaluate the prostate cancer diagnostic efficacy of the combination marker of Example 4 above by comparing it with conventional prostate cancer diagnostic technology. To this end, the performance of a model classifying prostate cancer and control groups by PSA level, which is a conventional prostate cancer diagnostic technology, was evaluated using a machine learning model (Random Forest), and the results are shown in Figure 2. In addition, the performance of the classification model 13 (Chr7; 5396809, Chr2; 191045615, Chr2; 191045611, Chr1; 22141161, Chr18; 20911499, Chr1; 41327272, Chr1; 41327274, Chr3; 37494252, Chr3; 192125900, Chr20; 50721715, Chr20; 50721518, Chr10; 72201209 and Chr20; 61297922) of Example 4 above was evaluated using a machine learning model (Random Forest), and the results are shown in FIG. 3.

[0120] From the results of Figures 2 and 3, it was confirmed that the classification model using methylation markers according to one aspect has significantly superior sensitivity, specificity, and accuracy compared to the classification model using PSA levels.

[0121]

[0122] Synthesizing the above results, it was confirmed that all 13 methylation markers can be used specifically for the diagnosis of prostate cancer, and that their combination is even more useful for excellent prostate cancer diagnosis.

[0123] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

Claims

1. A composition for diagnosing prostate cancer comprising a preparation for measuring the methylation level of one or more gene CpG regions selected from the group consisting of TNRC18 (Trinucleotide Repeat Containing 18), C2orf88 (Chromosome 2 Open Reading Frame 88), LDLRAD2 (Low-Density Lipoprotein Receptor-Related Protein-Associated Protein 2), TMEM241 (Transmembrane Protein 241), CITED4 (Cbp / p300-interacting transactivator with Glu / Asp-rich carboxy-terminal domain 4), ITGA9 (Integrin Subunit Alpha 9), FGF12 (Fibroblast Growth Factor 12), ZFP64 (Zinc Finger Protein 64), NODAL (Nodal Growth Differentiation Factor), and SLCO4A1-AS1 (SLCO4A1 Antisense RNA 1).

2. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the TNRC18 gene is 5396809 on chromosome 7.

3. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the C2orf88 gene is one or more selected from the group consisting of 191045615 and 191045611 of chromosome 2.

4. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the LDLRAD2 gene is 22141161 on chromosome 1.

5. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the TMEM241 gene is 20911499 on chromosome 18.

6. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the CITED4 gene is one or more selected from the group consisting of 41327272 and 41327274 of chromosome 1.

7. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the ITGA9 gene is 37494252 on chromosome 3.

8. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the FGF12 gene is 192125900 on chromosome 3.

9. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the ZFP64 gene is one or more selected from the group consisting of 50721715 and 50721518 of chromosome 20.

10. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the NODAL gene is 72201209 of chromosome 10.

11. A composition for diagnosing prostate cancer according to claim 1, wherein the CpG region of the SLCO4A1-AS1 gene is 61297922 on chromosome 20.

12. A composition for diagnosing prostate cancer according to claim 1, wherein the preparation is a primer pair for amplifying a CpG region or a CpG region-specific probe.

13. A composition for diagnosing prostate cancer according to claim 1, wherein the nucleic acid isolated from blood, plasma, or serum is used.

14. A composition for diagnosing prostate cancer according to claim 13, wherein the isolated nucleic acid is cfDNA.

15. A prostate cancer diagnostic kit comprising the composition of Claim 1.

16. (a) a step of measuring the CpG region methylation level of a gene in nucleic acids isolated from a biological sample of an individual; and (b) a method for providing information for the diagnosis of prostate cancer, comprising the step of comparing the CpG region methylation level of the measured gene with the methylation level of a control sample of a patient with benign prostatic hyperplasia, wherein A method of providing information, wherein the above genes are one or more selected from the group consisting of TNRC18 (Trinucleotide Repeat Containing 18), C2orf88 (Chromosome 2 Open Reading Frame 88), LDLRAD2 (Low-Density Lipoprotein Receptor-Related Protein-Associated Protein 2), TMEM241 (Transmembrane Protein 241), CITED4 (Cbp / p300-interacting transactivator with Glu / Asp-rich carboxy-terminal domain 4), ITGA9 (Integrin Subunit Alpha 9), FGF12 (Fibroblast Growth Factor 12), ZFP64 (Zinc Finger Protein 64), NODAL (Nodal Growth Differentiation Factor), and SLCO4A1-AS1 (SLCO4A1 Antisense RNA 1).

17. A method for providing information according to claim 16, wherein step (a) comprises the step of treating a preparation for measuring the CpG region methylation level of a gene.

18. A method for providing information according to claim 17, wherein the formulation is a primer pair for amplifying a CpG region or a CpG region-specific probe.

19. In a computer-based system, (a) A step of obtaining data on the CpG region methylation levels of genes in nucleic acids isolated from biological samples; (b) inputting the data on the acquired CpG region methylation levels into a pre-trained machine learning model; and (c) A method for providing information for diagnosing prostate cancer, comprising the step of generating information for diagnosing prostate cancer based on the output value of the machine learning model above, A method of providing information, wherein the above genes are one or more selected from the group consisting of TNRC18 (Trinucleotide Repeat Containing 18), C2orf88 (Chromosome 2 Open Reading Frame 88), LDLRAD2 (Low-Density Lipoprotein Receptor-Related Protein-Associated Protein 2), TMEM241 (Transmembrane Protein 241), CITED4 (Cbp / p300-interacting transactivator with Glu / Asp-rich carboxy-terminal domain 4), ITGA9 (Integrin Subunit Alpha 9), FGF12 (Fibroblast Growth Factor 12), ZFP64 (Zinc Finger Protein 64), NODAL (Nodal Growth Differentiation Factor), and SLCO4A1-AS1 (SLCO4A1 Antisense RNA 1).

20. A computer-readable recording medium containing a computer program for performing the method of claim 19.

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