CN113930515A - Marker and detection kit for drug resistance of ovarian cancer chemotherapeutic drugs - Google Patents

Abstract

The invention discloses a marker and a detection kit for drug resistance of ovarian cancer chemotherapeutic drugs, wherein the marker comprises CRYAB, CLDN6 and/or CXCL 9. The marker of the invention can predict the curative effect of a patient on the chemotherapy scheme before the patient receives chemotherapy, and has good clinical application value.

Description

Marker and detection kit for drug resistance of ovarian cancer chemotherapeutic drugs

Technical Field

The invention belongs to the field of biological medicine, and particularly relates to a marker and a detection kit for drug resistance of ovarian cancer chemotherapeutic drugs.

Background

Ovarian cancer is the first malignancy to die in the female reproductive organs. In 2011, 21, 990 new cases of U.S. disease, and 15, 460 deaths. With continuous efforts by gynecological oncologists worldwide, treatment strategies to assist chemotherapy based on tumor cell debulking were successfully explored, but the 5-year survival rate of advanced ovarian cancer still lingers around 30%.

Platinum drugs are chemotherapy drugs used in the clinic since the last 70 th century. After application of a platinum-based chemotherapeutic regimen to ovarian cancer treatment, a significant improvement in the survival rate of the disease is observed, and is therefore referred to as a milestone in ovarian cancer chemotherapy. Since the last 90 s, the combined regimen of platinum and paclitaxel was established as the first-line regimen of ovarian cancer chemotherapy with complete remission rates approaching 75% in naive patients, as fully demonstrated by evidence-based medical research. However, 15-30% of patients still respond poorly to the initial treatment regimen; of patients who achieve complete remission, there is > 90% of patients with tumor recurrence.

It is currently believed that the development of resistance of tumor cells to chemotherapeutic drugs is one of the major reasons for the failure to further improve the 5-year survival rate of ovarian cancer. The drug resistance of tumor chemotherapy is divided into primary and acquired. About 15-20% of patients with ovarian cancer have primary resistance to chemotherapeutic drugs such as cisplatin, and most patients gradually develop resistance during chemotherapy. How to early discover and diagnose chemotherapy drug resistance and further improve the prognosis of ovarian cancer is one of the main problems which currently plague gynecological tumor clinics. Clinical evaluation criteria for platinum drug resistance in ovarian cancer patients include tumor progression during chemotherapy; tumor recurrence within 6 months after chemotherapy discontinuance; and the best response to first-line chemotherapy is measurable tumor-invariant, etc. The greatest feature of this standard is that until chemotherapy has progressed to a certain stage, or the tumor has recurred, the patient's sensitivity to the chemotherapeutic drug cannot be judged, so that this part of the patients enters resistance during first-line chemotherapy. Over the years, researchers engaged in clinical tumor therapeutics have long demanded whether to predict the drug resistance of tumors before patients receive chemotherapy or at the early stage of treatment, and to adjust chemotherapy schemes in time, so as to achieve the purpose of improving the tumor remission rate. Finding a marker associated with platinum-based drug resistance is a key approach to solving this problem.

Disclosure of Invention

The invention aims to provide a marker for predicting ovarian cancer chemotherapy response.

In order to achieve the purpose, the invention adopts the following technical scheme:

in one aspect, the invention provides the use of an agent that measures the level of expression of a biomarker in a sample, the biomarker comprising CRYAB, CLDN6 and/or CXCL9, in the manufacture of a product for predicting whether a subject having ovarian cancer is resistant to a chemotherapeutic drug.

In certain embodiments, the ovarian cancer comprises serous ovarian cancer, mucinous ovarian cancer, endometrioid cancer, clear cell cancer, undifferentiated cancer, transitional cell cancer, preferably serous ovarian cancer.

In certain embodiments, the reagents include reagents for measuring the expression level of a biomarker by digital imaging techniques, protein immunization techniques, dye techniques, nucleic acid sequencing techniques, nucleic acid hybridization techniques, chromatography techniques, mass spectrometry techniques.

In certain embodiments, the reagents comprise primers or probes that specifically bind to the biomarker genes; an antibody, peptide, aptamer, or compound that specifically binds to the biomarker protein.

In certain embodiments, the product comprises a kit, a chip.

In certain embodiments, the sample comprises: blood, serum, plasma, whole blood, urine, saliva, semen, milk, cerebrospinal fluid, tears, nasal epithelial cells, sputum, tissue, mucus, lymph, cytosol, ascites, pleural effusion, amniotic fluid, bladder irrigation fluid and bronchoalveolar lavage fluid, preferably tissue.

In certain embodiments, the predicting whether a subject with ovarian cancer is resistant to a chemotherapeutic drug further comprises the step of comparing the amount of the biomarker in a sample from the subject to a control sample or a predetermined value. In certain embodiments, an increase in the level of CRYAB expression, an increase in the level of CLDN6 expression, and/or a decrease in the level of CXCL9 expression in a sample from the subject, when compared to a control sample or predetermined value, is indicative of the subject being resistant to the chemotherapeutic drug.

In certain embodiments, the chemotherapeutic agent is a platinum chemotherapeutic agent.

In another aspect, the invention also provides a system for predicting whether a subject having ovarian cancer is resistant to a chemotherapeutic drug, the system comprising:

1) a detection unit: comprises a biomarker detection module;

2) an analysis unit: the expression level of the biomarker detected by the detection unit is used as an input variable, and the input variable is input into a model for predicting whether the ovarian cancer patient has drug resistance to chemotherapeutic drugs for analysis;

3) an evaluation unit: outputting the risk value of the chemotherapy drug resistance of the subject corresponding to the sample;

the biomarker comprises CRYAB, CLDN6 and/or CXCL 9.

In certain embodiments, the ovarian cancer comprises serous ovarian cancer, mucinous ovarian cancer, endometrioid cancer, clear cell cancer, undifferentiated cancer, transitional cell cancer, and preferably, the ovarian cancer is serous ovarian cancer;

in certain embodiments, the chemotherapeutic agent is a platinum chemotherapeutic agent.

In certain embodiments, the model that predicts whether an ovarian cancer patient is resistant to a chemotherapeutic drug is determined using one or more algorithms selected from the group consisting of: XGboost, random forest, glmnet, cforest, machine learning classification and regression trees, treebag, K-adjacency, neural networks, support vector machine radial, support vector machine linear, naive Bayes, or multi-layer perception.

In another aspect, the invention also provides the use of biomarkers comprising CRYAB, CLDN6 and/or CXCL9 in the construction of a system as described above.

The invention has the advantages of

The invention provides a biomarker for predicting whether a subject suffering from ovarian cancer is resistant to a chemotherapeutic drug, provides a therapeutic drug curative effect and reduces the toxic and side effects of the drug to the maximum extent, aims at different ovarian cancer patients, adopts different treatment schemes and dosages in a targeted manner, provides scientific basis for drug effect monitoring and disease prognosis of ovarian cancer, finally achieves clinical individualized treatment, and has great clinical value.

Drawings

FIG. 1 is a boxplot of differential CRYAB expression;

fig. 2 is a box line plot of differential expression of CLDN 6;

fig. 3 is a box line plot of CXCL9 differential expression;

FIG. 4 is a ROC plot of CRYAB and CLDN6 in combination predicting susceptibility of ovarian cancer patients to chemotherapeutic agents;

FIG. 5 is a ROC plot of CLDN6 and CXCL9 in combination predicting susceptibility of ovarian cancer patients to chemotherapeutic drugs;

FIG. 6 is a ROC plot of CRYAB and CXCL9 in combination predicting susceptibility of ovarian cancer patients to chemotherapeutic agents;

FIG. 7 is a ROC plot of CRYAB + CLDN6+ CXCL9 in combination to predict susceptibility of ovarian cancer patients to chemotherapeutic drugs.

Detailed Description

In the present invention, unless otherwise specified, scientific and technical terms used herein have the meanings that are commonly understood by those skilled in the art. Also, the procedures of molecular genetics, nucleic acid chemistry, etc. used herein are all conventional procedures widely used in the corresponding fields. Meanwhile, in order to better understand the present invention, the definitions and explanations of related terms are provided below.

The term "and/or" as used herein in phrases such as "a and/or B" is intended to include both a and B; a or B; a (alone); and B (alone). Likewise, the term "and/or" as used in phrases such as "A, B and/or C" is intended to encompass each of the following embodiments: A. b and C; A. b or C; a or C; a or B; b or C; a and C; a and B; b and C; a (alone); b (alone); and C (alone).

Biomarkers

The term "biomarker" refers to a biological molecule present in an individual at different concentrations that can be used to predict the disease state of the individual. Biomarkers can include, but are not limited to, nucleic acids, proteins, and variants and fragments thereof. A biomarker may be DNA comprising all or part of a nucleic acid sequence encoding the biomarker, or the complement of such a sequence. Biomarker nucleic acids useful in the present invention are considered to include DNA and RNA comprising all or part of any nucleic acid sequence of interest.

In the present invention, the biomarker comprises CRYAB, CLDN6 and/or CXCL 9.

In the present invention, biomarkers such as CRYAB (gene ID: 1410), CLDN6(gene ID: 9074), CXCL9(gene ID: 4283), including gene and its encoded protein and homologs, mutations, and isoforms thereof. The term encompasses full-length, unprocessed biomarkers, as well as any form of biomarker that results from processing in a cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of the biomarkers.

As used herein, the term "subject" may be any mammal, preferably a human, regardless of its age or gender. In certain embodiments, the subject has ovarian cancer. In certain embodiments, the subject has serous ovarian cancer.

Biomarker expression level measurement

Reference to measuring expression levels refers to determining the expression level of the expression product of a gene. The expression level can be determined at the nucleic acid level or at the protein level.

The determined gene expression level can be considered to provide an expression profile. By "expression profile" is meant a set of data relating to the expression levels of one or more related genes in an individual in a form that allows comparison with comparable expression profiles (e.g., from individuals with known prognoses) to help determine the prognosis and select an appropriate treatment for the individual patient.

Determination of the gene expression level may involve determining the presence or amount of mRNA in a cancer cell sample. Methods for doing so are well known to the skilled person. Gene expression levels can be determined in cancer cell samples using any conventional method, for example using nucleic acid microarrays or using nucleic acid synthesis (e.g., quantitative PCR).

Alternatively or additionally, the determination of the level of gene expression may involve determining the level of protein expressed from the gene in a sample comprising cancer cells obtained from the individual. Protein expression levels can be determined by any useful means, including the use of immunoassays. For example, expression levels can be determined by Immunohistochemistry (IHC), western blotting, ELISA, immunoelectrophoresis, immunoprecipitation, flow cytometry, mass cytometry, and immunostaining. Using any of these methods, the relative expression levels of the proteins of the biomarkers disclosed herein can be determined.

As an alternative embodiment, the expression level of the gene may also be detected using advanced sequencing methods. For example, Illumina can be used to detect biomarkers. Next generation Sequencing (e.g., Sequencing-By-Synthesis or TruSeq methods using, for example, the HiSeq, HiScan, genome Analyzer, or MiSeq systems). Biomarkers can also be detected using ion beam sequencing or other suitable semiconductor sequencing methods.

As an alternative embodiment, RNase profiling (mapping) can be used to quantify biomarkers using mass spectrometry. The isolated RNA may be enzymatically digested with an RNA endonuclease (RNase) having high specificity (e.g., RNase T1, which cleaves 3' to all unmodified guanosine residues) prior to analysis of the isolated RNA by MS or tandem MS (MS/MS) methods. The first method developed used reverse phase HPLC coupled directly to ESI-MS to perform on-line chromatographic separation of endonuclease digests. The presence of post-transcriptional modifications can be revealed by mass shifts from those expected based on the RNA sequence. Ions of abnormal mass/charge values can then be isolated for tandem MS sequencing, thereby locating the sequence position of the post-transcriptionally modified nucleoside.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has also been used as an analytical method to obtain information about post-transcriptionally modified nucleosides. MALDI-based methods can be distinguished from ESI-based methods by separation steps. In MALDI-MS, mass spectrometry is used to separate biomarkers.

The term "primer" as used herein refers to a nucleic acid sequence having a short free 3' -hydroxyl group, which is a short nucleic acid that can form a base pair with a complementary template and serves as an origin of replication for the template strand. The primers can prime DNA synthesis in the presence of reagents for polymerization (i.e., DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates in appropriate buffer solutions and temperatures. The PCR conditions and the lengths of the sense and antisense primers can be appropriately selected according to the techniques known in the art.

The term "probe" as used herein refers to a nucleic acid fragment (e.g., RNA or DNA) corresponding to several bases to several hundred bases that can specifically bind to mRNA, and the presence or absence and expression level of a particular mRNA can be confirmed by a tag. The probe may be prepared in the form of an oligonucleotide probe, a single-stranded DNA probe, a double-stranded DNA probe, or an RNA probe. Suitable probes and hybridization conditions may be appropriately selected according to techniques known in the art.

The term "antibody" as used herein is well known in the art and refers to a specific immunoglobulin directed against an antigenic site. The antibody of the present invention refers to an antibody that specifically binds to the biomarker protein of the present invention, and can be produced according to a conventional method in the art. Forms of antibodies include polyclonal or monoclonal antibodies, antibody fragments (such as Fab, Fab ', F (ab')2, and Fv fragments), single chain Fv (scfv) antibodies, multispecific antibodies (such as bispecific antibodies), monospecific antibodies, monovalent antibodies, chimeric antibodies, humanized antibodies, human antibodies, fusion proteins comprising an antigen binding site of an antibody, and any other modified immunoglobulin molecule comprising an antigen binding site, so long as the antibody exhibits the desired biological binding activity.

The term "peptide" as used herein has the ability to bind to a target substance to a high degree and does not undergo denaturation during heat/chemical treatment. Also, due to its small size, it can be used as a fusion protein by attaching it to other proteins. In particular, since it can be specifically attached to a high molecular protein chain, it can be used as a diagnostic kit and a drug delivery substance.

The term "aptamer" as used herein refers to a polynucleotide composed of a specific type of single-stranded nucleic acid (DNA, RNA or modified nucleic acid) which itself has a stable tertiary structure and has the property of being able to bind with high affinity and specificity to a target molecule. As described above, since the aptamer can specifically bind to an antigenic substance like an antibody, but is more stable and has a simple structure than a protein, and is composed of a polynucleotide that is easily synthesized, it can be used instead of an antibody.

The term "specifically binds" as used herein refers toNon-random binding reactions between two molecules (i.e., a binding molecule and a target molecule), such as between an antibody and the antigen to which it is directed. The binding affinity between two molecules can be described by the KD value. The KD value refers to the dissociation constant derived from the ratio of KD (the dissociation rate of a particular binding molecule-target molecule interaction; also known as koff) to ka (the association rate of a particular binding molecule-target molecule interaction; also known as kon), or KD/ka expressed as molarity (M). The smaller the KD value, the more tightly bound the two molecules and the higher the affinity. In certain embodiments, an antibody that specifically binds to (or is specific for) an antigen means that the antibody is present in an amount less than about 10^-5M, e.g. less than about 10^-6M、10^-7M、10^-8M、10^-9M or 10^-10M or less binds to the antigen with an affinity (KD). KD values can be determined by methods well known in the art, for example in a BIACORE instrument using Surface Plasmon Resonance (SPR).

As used herein, the term "sample" may be any biological sample containing nucleic acids or polypeptides derived from a patient or subject. Examples of such samples include fluids, tissues, cell samples, organs, biopsy samples, and the like. Samples can be collected according to conventional techniques and used directly for diagnosis or storage. In a specific embodiment of the invention, the sample is a tissue.

Kit and chip

In the present invention, "chip", also referred to as "array", refers to a solid support comprising attached nucleic acid or peptide probes. Arrays typically comprise a plurality of different nucleic acid or peptide probes attached to the surface of a substrate at different known locations. These arrays, also known as "microarrays," can generally be produced using either mechanosynthesis methods or light-guided synthesis methods that incorporate a combination of photolithography and solid-phase synthesis methods. The array may comprise a flat surface, or may be nucleic acids or peptides on beads, gels, polymer surfaces, fibers such as optical fibers, glass, or any other suitable substrate. The array may be packaged in a manner that allows for diagnostic or other manipulation of the fully functional device.

In certain non-limiting embodiments, the present invention provides kits for predicting whether a subject having ovarian cancer is resistant to a chemotherapeutic drug, comprising reagents for detecting the biomarkers set forth in the preceding section. The kit further includes instructions or support materials describing the use of the kit to determine whether the chemotherapeutic agent is likely to produce an anti-cancer effect in ovarian cancer, and/or a website or publication describing the same.

Types of kits include, but are not limited to, packaged biomarker-specific probes and primer sets (e.g., TaqMan probe/primer sets), arrays/microarrays, biomarker-specific antibodies, biomarker-specific beads, which further comprise one or more probes, primers, or other reagents for detecting one or more biomarkers of the invention.

In certain non-limiting embodiments, the present invention provides kits for predicting whether a subject with ovarian cancer is resistant to a chemotherapeutic drug, comprising means for detecting the presence of a nucleic acid biomarker.

In a specific non-limiting embodiment, the kit can comprise a pair of oligonucleotide primers suitable for Polymerase Chain Reaction (PCR) or nucleic acid sequencing for detecting the nucleic acid biomarker to be identified. A pair of primers may comprise nucleotide sequences complementary to the biomarkers described above and of sufficient length to selectively hybridize to the biomarkers. Alternatively, complementary nucleotides can selectively hybridize to specific regions sufficiently close to the 5 'and/or 3' positions of the biomarker for PCR and/or sequencing. Multiple biomarker-specific primers may be included in the kit to detect more than one biomarker simultaneously. The kit may further comprise one or more polymerases, reverse transcriptases, and nucleotide bases, wherein the nucleotide bases may also be detectably labeled.

In certain non-limiting embodiments, the length of the primer may be at least about 10 nucleotides or at least about 15 nucleotides or at least about 20 nucleotides, and/or up to about 200 nucleotides or up to about 150 nucleotides or up to about 100 nucleotides or up to about 75 nucleotides or up to about 50 nucleotides.

In another non-limiting embodiment, the oligonucleotide primers can be immobilized on a solid surface, substrate, or support, such as on a nucleic acid microarray, wherein the location at which each oligonucleotide primer binds to the solid surface or support is known and identifiable. The oligonucleotides may be immobilized to a substrate, such as glass, plastic, paper, nylon, or other type of membrane, filter, chip, bead, or any other suitable solid support. The polynucleotide may be synthesized directly on the substrate, or synthesized separately from the substrate and then immobilized on the substrate. Arrays are prepared using known methods.

In a specific non-limiting embodiment, the kit may comprise at least one nucleic acid probe suitable for in situ hybridization or in situ fluorescence hybridization for detecting the biomarker to be identified. Such kits typically comprise one or more oligonucleotide probes specific for various biomarkers. The means for testing multiple biomarkers may optionally be contained in a single kit.

In certain embodiments, a kit can include containers (including microtiter plates suitable for use in the automated practice of the present methods), each container having one or more of the various reagents (typically in concentrated form) used in the method, including, for example, pre-fabricated microarrays, buffers, appropriate nucleoside triphosphates (e.g., dATP, dCTP, dGTP, and dTTP, or rATP, rCTP, rGTP, and UTP), reverse transcriptases, DNA polymerases, RNA polymerases, and one or more probes and primers of the invention (e.g., poly (T) or random primers of appropriate length linked to a promoter that reacts with RNA polymerase).

In a non-limiting embodiment, the present invention provides a kit for predicting whether a subject with ovarian cancer is resistant to a chemotherapeutic drug, comprising means for detecting the level of a protein biomarker.

In non-limiting embodiments, the kit may comprise at least one antibody or antigen-binding fragment thereof for use in the immunoassay of the biomarker to be identified. Polyclonal and monoclonal antibodies that specifically target biomarkers can be prepared using conventional immunological techniques as are generally known to those skilled in the art. The immunodetection reagents of the kit may include a detectable label associated with or linked to a given antibody or antigen itself. Such detectable labels include, for example, chemiluminescent or fluorescent molecules (rhodamine, fluorescein, green fluorescent protein, luciferase, Cy3, Cy5, or ROX), radiolabels (3H, 35S, 32P, 14C, or 131I), or enzymes (alkaline phosphatase, horseradish peroxidase).

In another non-limiting embodiment, one or more biomarker-specific antibodies can be provided bound to a solid support, such as a column matrix, an array, or a well of a microtiter plate. Alternatively, the support may be provided as a separate element of the kit.

In certain non-limiting embodiments, when the array is used by the measurement means in the kit, the set of biomarkers described above may constitute at least 10% or at least 20% or at least 30% or at least 40% or at least 50% or at least 60% or at least 70% or at least 80% of the species of biomarkers present on the microarray.

In certain non-limiting embodiments, the kits of the present disclosure may comprise one or more probes, primers, antibodies, or other detection reagents for detecting the level of expression of a biomarker in a sample. For example, the kit may comprise an antibody or fragment thereof for detecting the protein expression level of a biomarker in a biological sample.

In certain non-limiting embodiments, the kits of the present disclosure may comprise one or more probes, primers, antibodies, or other detection reagents for detecting the level of CRYAB expression in a sample. For example, the kit may comprise an antibody or fragment thereof for detecting the protein expression level of CRYAB in a biological sample.

In certain non-limiting embodiments, the kits of the present disclosure may comprise one or more probes, primers, antibodies or other detection reagents for detecting the expression level of CLDN6 in a sample. For example, the kit may comprise an antibody or fragment thereof for detecting the level of expression of a protein of CLDN6 in a biological sample.

In certain non-limiting embodiments, kits of the present disclosure can comprise one or more probes, primers, antibodies, or other detection reagents for detecting the level of expression of CXCL9 in a sample. For example, the kit can comprise an antibody or fragment thereof for detecting the protein expression level of CXCL9 in a biological sample.

The kit may further comprise means for allowing a comparison between the biomarker levels in the cancer sample and the biomarker levels in the reference control sample. For example, but not limited to, the kits of the present disclosure comprise one or more probes, primers, antibodies, or other detection reagents for detecting a reference protein or mRNA, which can be used to normalize the expression levels of one or more biomarkers in a sample to allow comparison. Non-limiting examples of reference proteins such as housekeeping proteins include alpha-or beta-tubulin, actin, filaggrin, vinculin, and GADPH.

In certain non-limiting embodiments, the kit can further comprise instructions for using the kit to detect the biomarker of interest.

Prediction model

A "model" is any mathematical equation, algorithm, analytical or programmed process or statistical technique that takes one or more continuous or categorical inputs and calculates an output value, sometimes referred to as an "index," index value, "" predictor, "" predicted value, "" probability, "or" probability score. Non-limiting examples of "formulas" include sums, ratios, and regression operators, such as coefficients or indices, biomarker value conversion and normalization, rules and guidelines, statistical classification models, and neural networks trained on historical populations. Of particular interest in group (panel) and combinatorial constructs are structural and syntactic statistical classification algorithms, as well as risk index construction methods that utilize pattern recognition features, including established techniques such as cross-correlation, Principal Component Analysis (PCA), factor rotation, log regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (Support Vector Machines, SVMs), Random Forest (Random Forest, RF), recursive partition Trees (RPART), xgboost (xgb), and other related Decision tree classification techniques, shrunken centroids (sc), stepic, Nearest-Neighbor, Boosting, Decision Trees (Decision Trees), neural networks, bayesian networks, Support Vector Machines, and Hidden markov models (Hidden markov, etc. Many such algorithmic techniques are further implemented to perform feature (locus) selection and regularization (regularization) regularization, for example in ridge regression, lasso and elastic net, among others. Other techniques may be used in time to event hazard analysis (time to event hazard analysis), including Cox, Weibull, Kaplan-Meier, and Greenwood models, which are well known to those skilled in the art. Many of these techniques can be used in conjunction with biomarker selection techniques, such as forward selection, backward selection, or stepwise selection, complete enumeration of all potential biomarker sets or groups of a given size, genetic algorithms, or themselves can include biomarker selection methods. These can be used in conjunction with Information criteria, such as Akaike's Information Criterion (AIC) or Bayesian Information Criterion (BIC), to quantify the trade-off between other biomarkers and model improvement and to help minimize overfitting. The generated predictive models can be validated in other studies, or cross-validated in studies in which they were originally trained, using techniques such as Bootstrap, Leave-One-out (LOO) and 10-Fold cross-validation (10-Fold cross-validation) (10-Fold CV). At various steps, the false discovery rate may be estimated by value permutation according to techniques known in the art.

The following examples are presented to describe certain preferred embodiments of the invention and certain aspects of the invention and should not be construed as limiting the scope of the invention. The following examples are presented to further detail the embodiments of the present invention in conjunction with the attached tables and figures.

Example 1 differential expression of genes

GSE156699 was downloaded from the GEO database, samples of no reaction status were deleted, and 88 samples were retained for further analysis. Performing differential analysis on the genes by using an R language limma package to obtain 66 differential expression genes, wherein the screening standard is as follows: value <0.05, | logFC | > 1.

This database was derived from a retrospective case control study that uses clinical and genomic information to create models to predict the initial response of HGSC patients to standard treatment. HGSC patients were divided into responder groups (platinum drug sensitive groups) and non-responder groups (platinum drug resistant groups). Responders were patients who survived progression-free for at least 6 months after the first platinum-based drug treatment. Non-responders refer to patients who did not respond (platinum resistant) or progressed (platinum refractory) during treatment.

The expression levels of the differentially expressed genes CRYAB, CLDN6 and CXCL9 are shown in Table 1 and figures 1-3, compared with the platinum drug resistance group, the expression of CRYAB and CLDN6 is down-regulated in the platinum drug sensitive group, and the expression of CXCL9 is up-regulated in the platinum drug sensitive group.

TABLE 1 differentially expressed genes

Gene	logFC	t	P.Value	Up/Down
					CRYAB	-1.048	-3.153	0.002	DOWN
CLDN6	-1.359	-2.277	0.025	DOWN
					CXCL9	1.284	2.450	0.016	UP

Example 2 diagnostic efficacy

The Receiver Operating Curve (ROC) was plotted using the R package "pROC" (version 1.15.0) and the AUC values, sensitivity and specificity were analyzed, the results are shown in table 2 and fig. 4-7.

TABLE 2 biomarker/biomarker combination diagnostic potency data

Biomarker/biomarker combinations	AUC value
		CRYAB	0.671
CLDN6	0.641
		CXCL9	0.633
CRYAB+CLDN6	0.731
		CLDN6+CXCL9	0.721
CRYAB+CXCL9	0.721
		CRYAB+CLDN6+CXCL9	0.803

The results prove that the biomarker has good diagnostic efficacy in predicting the sensitivity of ovarian cancer patients to chemotherapeutic drugs, and the diagnostic efficacy of the biomarker combination is superior to that of a single biomarker.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. Use of an agent that measures the level of expression of a biomarker in a sample in the manufacture of a product for predicting whether a subject with ovarian cancer is resistant to a chemotherapeutic agent, wherein the biomarker comprises CRYAB, CLDN6 and/or CXCL9, preferably wherein the ovarian cancer comprises serous ovarian cancer, mucinous ovarian cancer, endometrioid cancer, clear cell cancer, undifferentiated cancer, transitional cell cancer, preferably wherein the ovarian cancer is serous ovarian cancer.

2. The use of claim 1, wherein the agent comprises an agent for measuring the level of expression of a biomarker by digital imaging techniques, protein immunization techniques, dye techniques, nucleic acid sequencing techniques, nucleic acid hybridization techniques, chromatography techniques, mass spectrometry techniques.

3. The use according to claim 1, wherein the reagent comprises a primer or probe that specifically binds to the biomarker gene; an antibody, peptide, aptamer, or compound that specifically binds to the biomarker protein.

4. The use according to claim 1, wherein the product comprises a kit or chip.

5. Use according to claim 1, characterized in that the sample comprises: blood, serum, plasma, whole blood, urine, saliva, semen, milk, cerebrospinal fluid, tears, nasal epithelial cells, sputum, tissue, mucus, lymph, cytosol, ascites, pleural effusion, amniotic fluid, bladder irrigation fluid, bronchoalveolar lavage fluid, preferably, the sample is a tissue.

6. The use of claim 1, wherein said predicting whether a subject with ovarian cancer is resistant to a chemotherapeutic agent further comprises the step of comparing the amount of said biomarker in a sample from said subject to a control sample or to a predetermined value, preferably wherein an increase in the level of CRYAB expression, an increase in the level of CLDN6 expression and/or a decrease in the level of CXCL9 expression in a sample from said subject, when compared to a control sample or to a predetermined value, indicates that the subject is resistant to a chemotherapeutic agent.

7. The use according to any one of claims 1 to 6, wherein the chemotherapeutic agent is a platinum chemotherapeutic agent.

8. A system for predicting whether a subject having ovarian cancer is resistant to a chemotherapeutic agent, the system comprising:

1) a detection unit: comprises a biomarker detection module;

2) an analysis unit: the expression level of the biomarker detected by the detection unit is used as an input variable, and the input variable is input into a model for predicting whether the ovarian cancer patient has drug resistance to chemotherapeutic drugs for analysis;

3) an evaluation unit: outputting the risk value of the chemotherapy drug resistance of the subject corresponding to the sample;

the biomarker comprises CRYAB, CLDN6 and/or CXCL 9;

preferably, the ovarian cancer comprises serous ovarian cancer, mucinous ovarian cancer, endometrioid cancer, clear cell cancer, undifferentiated cancer and transitional cell cancer, and preferably, the ovarian cancer is serous ovarian cancer;

preferably, the chemotherapeutic drug is a platinum chemotherapeutic drug.

9. The system of claim 8, wherein the model that predicts whether an ovarian cancer patient is resistant to a chemotherapeutic drug is determined using one or more algorithms selected from the group consisting of: XGboost, random forest, glmnet, cforest, machine learning classification and regression trees, treebag, K-adjacency, neural networks, support vector machine radial, support vector machine linear, naive Bayes, or multi-layer perception.

10. Use of a biomarker in the construction of a system according to claim 8 or 9, wherein the biomarker comprises CRYAB, CLDN6 and/or CXCL 9.

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