KR102011972B1 - Biomarkers for the treatment of ovarian cancer or prediction of recurrence after surgery - Google Patents

Abstract

The present invention relates to a biomarker for diagnosing ovarian cancer and a use thereof. The composition, kit, and method according to one aspect may not only diagnose ovarian cancer, but also based on the gene expression level of ovarian cancer tissue after surgery or treatment. By enabling the prediction of the prognosis, the ovarian cancer patient may be able to receive appropriate prognostic measures. In addition, by securing the gene profiling data of Asian ovarian cancer patients and analyzing the data, it is possible to overcome the problem of predicting the prognosis of ovarian cancer patients, which are made based on the data of conventional Westerners.

Description

Biomarkers for the treatment of ovarian cancer or prediction of recurrence after surgery}

A biomarker for the diagnosis of ovarian cancer and its use.

70% of patients with high-grade serous ovarian cancer die from cancer. Standard treatment is chemotherapy received after surgery. About 25% of patients relapse within 6 months after chemotherapy and have a 31% chance of surviving more than 5 years, requiring development of new therapies beyond standard therapy. The Cancer Genome Atlas (TCGA) researcher consortium produces and analyzes exome data, mRNA expression data and epigenetics data in approximately 300 ovarian cancer patients to develop new therapies. The characteristics of the patients were identified. However, the study has the problem that most of the patients are Europeans, and the analysis of these data did not find a list of patient clusters or gene expression markers with a significant difference in prognosis.

The present inventors completed the present invention by obtaining and analyzing data of Korean patients with various stages of ovarian cancer, and selecting a list of new expression genes in ovarian cancer.

One aspect is AIM2, CD2, CD244, CD27, CD274, CD40, CD68, CD8A, CSF2RB, CSF3R, CTLA4, CXCR3, EVI2B, FOXL2, GZMA, GZMB, HAVCR1, IDO1, IL1R2, IL2RA, IL32, IL6, IRF8, LAG3 To provide a composition for diagnosing ovarian cancer comprising an agent for measuring mRNA or protein expression levels of LTB, MNDA, PDCD1, PDCD1LG2, PRF1, RASGRP1, SELL, SLAMF1, SOD2, TAP1, TIGIT, TNFRSF6B or TNFSF15.

Another aspect is to provide a kit for diagnosing ovarian cancer comprising the composition.

Other aspects include AIM2, CD2, CD244, CD27, CD274, CD40, CD68, CD8A, CSF2RB, CSF3R, CTLA4, CXCR3, EVI2B, FOXL2, GZMA, GZMB, HAVCR1, IDO1, IL1R2, IL2RA, Measuring mRNA or protein expression levels of IL32, IL6, IRF8, LAG3, LTB, MNDA, PDCD1, PDCD1LG2, PRF1, RASGRP1, SELL, SLAMF1, SOD2, TAP1, TIGIT, TNFRSF6B or TNFSF15; And comparing the measured expression level with the expression level of a normal control group. It is to provide an information providing method for diagnosing ovarian cancer.

One aspect is AIM2, CD2, CD244, CD27, CD274, CD40, CD68, CD8A, CSF2RB, CSF3R, CTLA4, CXCR3, EVI2B, FOXL2, GZMA, GZMB, HAVCR1, IDO1, IL1R2, IL2RA, IL32, IL6, IRF8, LAG3 Provided is a composition for diagnosing ovarian cancer comprising an agent for measuring mRNA or protein expression levels of LTB, MNDA, PDCD1, PDCD1LG2, PRF1, RASGRP1, SELL, SLAMF1, SOD2, TAP1, TIGIT, TNFRSF6B or TNFSF15.

As used herein, the term “diagnosis” refers to identifying the presence or characteristic of pathophysiology. Diagnosis in one aspect is to determine the presence and / or extent of ovarian cancer, ie progression, and includes the prognosis after treatment or surgery, ie the possibility of relapse.

As used herein, the term "marker" is a substance that can be diagnosed by distinguishing a normal group from an individual with ovarian cancer, and a polypeptide, a protein, or an increase or decrease in an individual having an ovarian cancer of the present invention. Organic biomolecules such as nucleic acids, genetic lipids, glycolipids, glycoproteins or sugars. In particular, the present specification may be a protein or gene that is changed in tissue isolated from an individual having an ovarian cancer of the present invention.

As used herein, the term “agent that measures the expression level of an mRNA or protein” refers to AIM2, CD2, CD244, CD27, CD274, CD40, CD68, CD8A, CSF2RB, CSF3R, CTLA4, markers that increase or decrease in ovarian cancer patients. , CXCR3, EVI2B, FOXL2, GZMA, GZMB, HAVCR1, IDO1, IL1R2, IL2RA, IL32, IL6, IRF8, LAG3, LTB, MNDA, PDCD1, PDCD1LG2, PRF1, RASGRP1, SELL, SLAMF1, SODSF6 TAPN TAPN Or a molecule that can be used for detection and / or quantification of a marker by confirming the expression level of mRNA or protein of TNFSF15. Specifically, the agent for measuring the mRNA expression level of the gene may be, for example, a primer pair, probe or antisense nucleotide that specifically binds to the gene. At this time, since the nucleic acid information of the genes are known in Genebank, those skilled in the art can design primers or probes that specifically amplify specific regions of these genes based on the sequences.

As used herein, the term "primer pair" is a primer pair that includes all combinations of primer pairs consisting of forward and reverse primers that recognize a target gene sequence, and specifically provides assay results with specificity and sensitivity. When the nucleic acid sequence of a primer is a sequence that is inconsistent with a non-target sequence present in a sample, high specificity can be given when the primer amplifies only a target gene sequence containing a complementary primer binding site and does not cause nonspecific amplification. .

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 means a substance that can specifically confirm the presence of the target substance in the sample through the binding. do. The type of probe molecule is a material commonly used in the art, but is not limited. Preferably, the probe molecule may be peptide nucleic acid (PNA), locked nucleic acid (LNA), peptide, polypeptide, protein, RNA or DNA. More specifically, the probes include those derived from or similar to organisms or produced in vitro as biomaterials, for example enzymes, proteins, antibodies, microorganisms, flora and fauna, neurons, DNA, and RNA. DNA may include cDNA, genomic DNA, oligonucleotides, RNA may include genomic RNA, mRNA, oligonucleotides, and examples of proteins may include antibodies, antigens, enzymes, peptides, and the like.

As used herein, the term “antisense oligonucleotide” refers to DNA or RNA or derivatives thereof that contain a nucleic acid sequence complementary to a sequence of a particular mRNA, which binds to the complementary sequence within the mRNA and inhibits translation of the mRNA into a protein. It works. Antisense oligonucleotide sequence refers to a DNA or RNA sequence that is complementary to the mRNA of said genes and capable of binding to said mRNA. This may inhibit the essential activity of the translation of the gene mRNA, translocation into the cytoplasm, maturation or any other overall biological function. The length of the antisense oligonucleotide may be 6 to 100 bases, preferably 8 to 60 bases, more preferably 10 to 40 bases. The antisense oligonucleotides may be synthesized in vitro in a conventional manner to be administered in vivo or to allow the antisense oligonucleotides to be synthesized in vivo. One example of synthesizing antisense oligonucleotides in vitro is using RNA polymerase I. One example of allowing antisense RNA to be synthesized in vivo is to allow the antisense RNA to be transcribed using a vector whose origin is in the opposite direction of the multicloning site (MCS). The antisense RNA is preferably such that the translation stop codon is present in the sequence so that it is not translated into the peptide sequence.

In addition, the agent for measuring the protein expression level of the marker may be, for example, an antibody that specifically binds to a protein or fragment of a protein encoded by the marker gene, the antibody may be a polyclonal antibody or a monoclonal antibody. Can be.

As used herein, the term “antibody” may mean a specific protein molecule directed against an antigenic site. For the purposes of the present invention, an antibody refers to an antibody that specifically binds to a marker protein and includes all polyclonal antibodies, monoclonal antibodies and recombinant antibodies. Generating antibodies can be readily prepared using techniques well known in the art. The antibodies herein also include functional fragments of antibody molecules as well as complete forms having two full length light chains and two full length heavy chains. Functional fragments of antibody molecules mean fragments having at least antigen binding function, and include Fab, F (ab '), F (ab') 2 and Fv.

Another aspect provides a kit for diagnosing ovarian cancer comprising the composition. Specific contents of the composition are as described above. Specifically, the kit is AIM2, CD2, CD244, CD27, CD274, CD40, CD68, CD8A, CSF2RB, CSF3R, CTLA4, CXCR3, EVI2B, FOXL2, GZMA, GZMB, HAVCR1, IDO1, IL1R2, IL2RA , Expression level of IL32, IL6, IRF8, LAG3, LTB, MNDA, PDCD1, PDCD1LG2, PRF1, RASGRP1, SELL, SLAMF1, SOD2, TAP1, TIGIT, TNFRSF6B or TNFSF15 By measuring, ovarian cancer can be diagnosed. The kit may include an agent for measuring the mRNA expression level of an ovarian cancer diagnostic marker gene, eg, a primer pair, a probe or an antisense nucleotide that specifically binds the gene, and an agent for measuring the expression level of a protein. For example, it may include an antibody or an immunobinding fragment of the antibody that specifically binds to the marker protein.

The kit of one embodiment may be a reverse transcription polymerase chain reaction (RT-PCR) kit, a DNA chip kit, an enzyme-linked immunosorbent assay (ELISA) kit, a protein chip kit, a rapid kit, or a multiple reaction monitoring (MRM) kit. have.

In addition, the ovarian cancer diagnostic kit may further comprise one or more other component compositions, solutions or devices suitable for the analysis method. For example, the diagnostic kit may be a diagnostic kit including essential elements necessary to perform reverse transcriptase. The reverse transcription polymerase kit contains each primer pair specific for the marker gene. The primer is a nucleotide having a sequence specific to the nucleic acid sequence of each gene, and is about 7 bp to 50 bp in length, more preferably about 10 bp to 30 bp in length. It may also include primers specific for the nucleic acid sequence of the control gene. Other reverse transcriptase kits include test tubes or other suitable containers, reaction buffers (pH and magnesium concentrations vary), enzymes such as deoxynucleotides (dNTPs), Taq-polymerase and reverse transcriptase, DNAse, RNAse inhibitor DEPC- It may include DEPC-water, sterile water, and the like. In addition, the DNA chip kit may include, for example, a substrate to which a cDNA or oligonucleotide corresponding to a gene or fragment thereof is attached, and reagents, agents, enzymes, and the like for preparing a fluorescent probe. The substrate may also comprise cDNA or oligonucleotide corresponding to the control gene or fragment thereof. Also, for example, the kit can be a diagnostic kit containing the necessary elements necessary to perform LISA. ELISA kits include antibodies specific for the protein. Antibodies are antibodies that have high specificity and affinity for each marker protein and have little cross-reactivity to other proteins. They are monoclonal antibodies, polyclonal antibodies, or recombinant antibodies. The ELISA kit can also include antibodies specific for the control protein. Other ELISA kits can bind reagents that can detect bound antibodies, such as labeled secondary antibodies, chromophores, enzymes (eg conjugated with the antibody) and substrates or antibodies thereof. Other materials and the like. In addition, for example, the kit may be a rapid kit including essential elements necessary for performing a rapid test in which an analysis result is known. Rapid kits include antibodies specific for a protein. Antibodies are antibodies that have high specificity and affinity for each marker protein and have little cross-reactivity to other proteins. They are monoclonal antibodies, polyclonal antibodies, or recombinant antibodies. Rapid kits may also include antibodies specific for the control protein. Other rapid kits include reagents that can detect bound antibodies, such as nitrocellulose membranes to which specific and secondary antibodies are immobilized, membranes bound to beads to which antibodies are bound, absorbent pads, and sample pads. Substances and the like. Also, for example, the kit can be a multiple reaction monitoring (MRM) kit, which is an MS / MS mode that contains the necessary elements necessary to perform mass spectrometry. Whereas Selected Ion Monitoring (SIM) is a method of using ions generated by collisions once in the source portion of the mass spectrometer, MRM selects a particular ion one more time from the broken ion to detect another continuously connected source of MS. It is a method of using the ions obtained from the collision after passing through it once more. Through the multiple reaction monitoring (MRM) analysis method, it is possible to compare the protein expression level of the normal control group or the same individual with that of the ovarian cancer, and also from the ovarian cancer marker gene to the protein. By determining whether the expression level is increased or decreased, it is possible to diagnose the onset of ovarian cancer.

Other aspects include AIM2, CD2, CD244, CD27, CD274, CD40, CD68, CD8A, CSF2RB, CSF3R, CTLA4, CXCR3, EVI2B, FOXL2, GZMA, GZMB, HAVCR1, IDO1, IL1R2, IL2RA, Measuring mRNA or protein expression levels of IL32, IL6, IRF8, LAG3, LTB, MNDA, PDCD1, PDCD1LG2, PRF1, RASGRP1, SELL, SLAMF1, SOD2, TAP1, TIGIT, TNFRSF6B or TNFSF15; And comparing the measured expression level with the expression level of a normal control group. Provides an information providing method for diagnosing ovarian cancer comprising a.

In one embodiment, the method of providing information comprises AIM2, CD2, CD244, CD27, CD274, CD40, CD68, CD8A, CSF2RB, CSF3R, CTLA4, CXCR3, EVI2B, FOXL2, GZMA, GZMB, HAVCR1, IDO1, Measuring mRNA or protein expression levels of IL1R2, IL2RA, IL32, IL6, IRF8, LAG3, LTB, MNDA, PDCD1, PDCD1LG2, PRF1, RASGRP1, SELL, SLAMF1, SOD2, TAP1, TIGIT, TNFRSF6B or TNFSF15. . Specifically, the expression level of the mRNA or protein can be known by measuring the amount of mRNA expression of the marker gene or the amount of protein encoded by the gene. Isolating mRNA or protein from a subject's sample can be performed appropriately by one of skill in the art according to methods known in the art. For example, the ovarian cancer tissue of a subject may be homogenized with a protein extraction buffer or a nucleic acid extraction buffer, and then centrifuged, and the obtained supernatant may be used as a sample of the subject. The subject refers to a subject for predicting prognosis after diagnosis of ovarian cancer or treatment of ovarian cancer, that is, recurrence. The subject may comprise a vertebrate, mammal, or human ( Homo sapiens ). For example, the human can be Korean. In addition, the biological sample may be tissue, cells, whole blood, serum, plasma, saliva, sputum, cerebrospinal fluid or urine. Measurement of the mRNA expression level may be, for example, reverse transcriptase polymerase (RT-PCR), competitive reverse transcriptase polymerase (competitive RT-PCR), real time quantitative RT-PCR, Quantitative RT-PCR, RNase protection method, Northern blotting or DNA chip technology can be used. In addition, the measurement of the protein expression level, for example, Western blotting, immunohistochemical staining, immunoprecipitation assay, complement fixation assay or immunofluorescence assay (immunofluorescence) can be used.

The information providing method of one embodiment includes comparing the measured expression level with the expression level of a normal control. Specifically, the mRNA or protein of the marker gene in the patient sample by measuring the expression level of the mRNA or protein of the marker gene in the sample of ovarian cancer patients, and by measuring the expression level of mRNA or protein of the same marker gene of the normal control group According to the degree of increasing or decreasing the expression level of ovarian cancer can be measured and the degree, and if the ovarian cancer patient is a patient after treatment or surgery, it can be predicted whether recurrence.

In addition to being able to diagnose ovarian cancer, one aspect of biomarkers enables predictive prognosis based on the gene expression of ovarian cancer patient tissue after surgery or treatment, thereby enabling patients with ovarian cancer to receive appropriate prognostic measures. Can be. In addition, by securing the gene profiling data of Asian ovarian cancer patients and analyzing the data, it is possible to overcome the problem of predicting the prognosis of ovarian cancer patients, which are made based on the data of conventional Westerners.

1 is a schematic diagram showing a biomarker screening method for prognostic prediction of ovarian cancer.
Figure 2 is a diagram showing a network of genes with a large difference in expression according to ovarian cancer prognosis.

Hereinafter, preferred examples are provided to aid in understanding the present invention. However, the following examples are merely provided to more easily understand the present invention, and the contents of the present invention are not limited by the following examples.

[ Preparation ]

Patient samples used in the study were patients who had undergone ovarian cancer surgery at Bundang Cha Hospital, obtained approval of the Institutional Review Board to obtain tissues of the patients, and the information of each sample is shown in Table 1 below. Of the 54 patients shown in Table 1 below, 17 had a good prognosis, 29 had ovarian cancer recurrence, and 8 had a poor prognosis because they did not respond to chemotherapy.

Characteristic division Number of patients (54) age <40 3 (40 to 50) 15 (50 to 60) 23 (43.4%) (60-70) 6 (70-80) 7 ordnance I 4 II 2 III 44 (81.5%) IV 3 NA One

[ Example ]

Example  1. Data generation and primary gene list screening

For 54 patients obtained in the above preparation, whole-mRNA sequencing (RNAseq) data was generated using Illumina HiSeq2500. Thereafter, 471 genes with large expression differences were selected from the whole genome expression data according to the prognosis of ovarian cancer, and mRNA expression was analyzed by mRNA expression profiling. Specifically, mRNA expression was analyzed using the mRNA sequencing pipeline of The Cancer Genome Atlas (TCGA). In addition, MapSplice v2.1.6 was used to map to Transcript and the amount of mRNA for each of the 471 genes was measured using RSEM-1.2.5 and NCBI hg19 RefSeq. The molecular biology pathways with reduced expression in 37 ovarian cancer patients with poor prognosis and the genes whose expression changed statistically are shown in Table 2 below.

KEGG Path p-value Calibrated p-value Gene list Cytokine-cytokine receptor interaction 6.08E-08 1.13E-15 IFNG, CSF3, CD27, CCL4L2, CCL4, IL1B, IL2RA, TNFSF15, TNFRSF6B, CCR5, CXCR1, IL6, CXCR6, FASLG, MET, CXCL5, CXCL9, TNFRSF1B, CXCR2, CSF3R, ILB10 IL1R2, CXCL3, LTB, IL21R, CXCR3, CCL18, IL2RB, TNFRSF14, CSF2RB NK cell mediated cytotoxicity 4.06E-15 7.52E-13 CD244, ITGAL, IFNG, KIR2DL4, HCST, FCER1G, FASLG, ICAM2, RAC2, LCP2, TNFRSF10C, PRF1, ITGB2, GZMB, VAV1, KIR2DS4, CD48, FCGR3B, ZAP70, KLRD1, MICB, KLRK1, LCK Hematopoietic cell lineage 1.55E-11 2.85E-09 CD38, IL1R2, CD2, CD3D, CD8B, CD5, CD8A, IL6, CSF3, FCGR1A, IL2RA, IL1B, CD7, CD3G, CSF3R, CD3E Chemokine signaling pathway 2.44E-10 4.46E-08 CCL5, VAV1, CXCL3, CCL18, GNAI1, FGR, CXCR3, DOCK2, HCK, CCL4, NCF1, WAS, CCL4L2, ITK, CXCR1, CCR5, CXCL5, CXCR6, RAC2, CXCR2, CXCL9 T cell receptor signaling pathway 4.54E-10 8.27E-08 CD8A, PDCD1, LCK, ZAP70, CD8B, RASGRP1, CD3D, VAV1, CD3E, CD3G, LCP2, GRAP2, IFNG, PTPRC, CTLA4, ITK Primary immunodeficiency 1.07E-09 1.93E-07 PTPRC, IL2RG, CD3E, CD3D, CD40, LCK, ZAP70, CD8B, CD8A, TAP1 Cell adhesion molecules 7.06E-08 1.27E-05 CD274, CD8B, CD8A, PDCD1, SPN, ITGB2, CD2, CD40, PDCD1LG2, ICAM2, ITGB7, SELL, PTPRC, ITGAL, CTLA4 Complement and coagulation cascades 6.85E-05 1.21E-02 C4BPA, TFPI, FGA, C1QA, SERPINA1, C1QC, C1QB, FGB Toll like receptor signaling pathway 2.07E-04 3.63E-02 TLR8, IL6, IL1B, CCL4, CXCL9, LBP, TLR2, CCL5, CD40

As shown in Table 2, most molecular biological pathways are related to the immune response, it was confirmed that the expression of the gene associated with the immune response significantly changed. Then, for the 471 genes, a gene network was constructed using HumanNet (web server) and Cytoscape v.3. Specifically, the color of the gene node of the gene network is expressed as a log (fold-change) indicating a difference in gene expression, and the darker the light blue, the lesser the expression in the group of patients with poor prognosis, and the deeper the yellow, the prognosis is Genes with increased expression were shown in poor patient groups. In addition, the connection of two genes in a network means that they have the same function. Then, the relationship between the network and the genes shown in Table 2 was analyzed.

As a result, as shown in Figure 2, genes with reduced expression (light blue nodes in Figure 2) in patients with poor prognosis are closely related to each other in a network (Area Under a ROC curve: 0.74, p-value: 1.15e-55) You can see that it works like a functional module. That is, since the genes of Table 2 are genes related to most immune functions, the network of FIG. 2 is considered to be an immune module operating for the treatment of ovarian cancer.

Example  2. Expression gene Marker  Selection

Of the genes selected in Example 1, an expression gene marker list satisfying the following three criteria was selected. Specifically, in order to select genes with large expression differences according to the prognosis of the patients, the statistical significance of the expression differences was measured using the edgeR method. Genes with large difference in expression are genes having a fold change of 2 or more and a false discovery rate of less than 0.05. The function of the 471 genes was analyzed using KEGG molecular biology pathway, and statistical significance was used using Fisher's exact test. In addition, the human functional gene analysis network used the server HumanNet ( http://www.functionalnet.org/humannet/ ) provided on the web.

First, among the top 50 genes that were statistically significant, 11 genes that were important for cell function as membrane receptors and high as markers were selected first. Second, 23 genes that can represent the characteristics of the immune response were selected from among the genes showing differences in expression, in order to select patients who are expected to be suitable for immunotherapy as well as patients who are or are not suitable for standard treatment. Third, eight genes known as immuno checkpoint genes were selected, and the characteristics of 37 genes selected by the three conditions are shown in Table 3 below. In this case, the selected genes may be satisfied by overlapping the three conditions. For example, a gene such as CD274 or GZMB is a gene included in the top 50, and at the same time represents an immune response or corresponds to an immunocheckpoint gene.

gene log (fold-change) p-value false discovery rate Characteristic AIM2 -1.83 1.68.E-04 1.19.E-02 Immune checkpoint CD2 -1.56 2.76.E-04 1.64.E-02 T cell activity CD244 -1.39 1.26.E-03 4.17.E-02 T cell activity inhibition CD27 -1.45 3.58.E-04 1.97.E-02 T cell activity CD274 -2.18 9.06.E-08 5.56.E-05 Expression difference CD40 -1.17 3.74.E-05 4.61.E-03 APC active CD68 -1.10 8.10.E-04 3.29.E-02 Macrophage CD8A -2.15 7.24.E-07 2.43.E-04 CD8 + T Cells CSF2RB -2.11 6.25.E-07 2.33.E-04 Expression Difference Higher Genes CSF3R -2.03 3.77.E-07 1.62.E-04 Expression Difference Higher Genes CTLA4 -1.53 4.98.E-04 2.42.E-02 CD4 + T Cells, Immune Checkpoints CXCR3 -1.42 5.66.E-04 2.63.E-02 Dendritic cells EVI2B -1.13 9.02.E-04 3.42.E-02 Neutrophil Granulocytes FOXL2 2.43 1.17.E-03 4.02.E-02 Cancer genes GZMA -1.94 2.55.E-05 3.53.E-03 Cytolytic Activity GZMB -2.45 1.99.E-07 1.09.E-04 Expression Difference Higher Gene, Dendritic Cell HAVCR1 -4.62 4.17.E-08 3.06.E-05 Expression Difference Higher Genes IDO1 -2.51 3.75.E-06 8.44.E-04 Immune checkpoint IL1R2 -3.22 5.81.E-10 9.98.E-07 Expression Difference Higher Genes IL2RA -2.24 6.36.E-07 2.33.E-04 Expression Difference Higher Genes IL32 -1.27 1.72.E-04 1.20.E-02 CD4 + T Cells IL6 -3.77 1.12.E-08 1.01.E-05 Expression Difference Higher Genes IRF8 -1.68 1.77.E-06 4.94.E-04 Dendritic cells LAG3 -1.50 4.11.E-04 2.12.E-02 T cell suppression LTB -3.35 1.23.E-10 3.53.E-07 Expression Difference Higher Genes MNDA -1.24 3.93.E-04 2.07.E-02 Neutrophil Granulocytes PDCD1 -1.87 1.63.E-05 2.50.E-03 Immune checkpoint PDCD1LG2 -1.55 1.74.E-04 1.20.E-02 APC Suppression PRF1 -2.19 1.09.E-06 3.42.E-04 Cytolytic Acitivity RASGRP1 -1.45 7.11.E-07 2.43.E-04 Expression Difference Higher Genes SELL -1.50 1.70.E-04 1.19.E-02 Neutrophil Granulocytes SLAMF1 -1.54 3.51.E-04 1.97.E-02 APC, T cell activity SOD2 -1.33 9.94.E-05 8.30.E-03 Expression Difference Higher Genes TAP1 -1.05 4.31.E-04 2.17.E-02 MHC TIGIT -1.60 9.49.E-05 8.21.E-03 T cell suppression, immune checkpoint TNFRSF6B -3.53 1.64.E-12 2.82.E-08 Expression Difference Higher Genes TNFSF15 -1.51 9.55.E-04 3.56.E-02 APC Suppression

Thus, through expression profiling of genetic markers selected by one aspect of the selection method, it is possible to effectively predict the treatment prognosis of ovarian cancer patients.

The foregoing description of the present invention is intended for illustration, and it will be understood by those skilled in the art that the present invention may be easily modified in other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive.

Claims (1)

For the treatment or prediction of recurrence after surgery or treatment of ovarian cancer comprising agents measuring the mRNA or protein expression levels of CD274, CD40, CD68, CXCR3, EVI2B, FOXL2, GZMB, IRF8, MNDA, PDCD1LG2, SELL, SLAMF1, TAP1 and TNFSF15 Composition.

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