CN114015780A - Marker for ovarian cancer diagnosis or prognosis risk assessment - Google Patents

Marker for ovarian cancer diagnosis or prognosis risk assessment Download PDF

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CN114015780A
CN114015780A CN202111513909.3A CN202111513909A CN114015780A CN 114015780 A CN114015780 A CN 114015780A CN 202111513909 A CN202111513909 A CN 202111513909A CN 114015780 A CN114015780 A CN 114015780A
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marker
expression level
ovarian cancer
gene
risk
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尉春艳
张熙
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Second Affiliated Hospital School of Medicine of Xian Jiaotong University
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57449—Specifically defined cancers of ovaries
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Abstract

The invention discloses a marker for ovarian cancer diagnosis or prognosis risk assessment. In particular, the invention provides a prognostic risk model for ovarian cancer, which realizes risk stratification of ovarian cancer patient prognosis from a molecular level. The invention also provides a marker for diagnosing ovarian cancer.

Description

Marker for ovarian cancer diagnosis or prognosis risk assessment
Technical Field
The invention belongs to the field of biomedicine, and particularly relates to a marker for ovarian cancer diagnosis or prognosis risk assessment.
Background
Ovarian cancer (OV) is the third leading cause of gynecological cancer most commonly in women, and is also the fifth leading cause of cancer death in women worldwide. Due to early non-specific symptoms, most women with ovarian cancer are diagnosed with late stage, with a 5-year survival rate of 25%.
Ovarian cancer is characterized by high incidence, high mortality and poor prognosis. Poor tumor differentiation, high disease stage, residual disease after cell-reducing surgery, big age, smoking, excessive weight and lack of physical activity are all related to poor prognosis of ovarian cancer. Although most patients initially respond well to chemotherapy, some patients relapse and develop chemotherapy resistance. Therefore, it is of great clinical significance to identify potential predictors that can improve the prognosis of ovarian cancer patients.
Disclosure of Invention
It is a first object of the present invention to provide a prognostic risk model for ovarian cancer.
It is a second object of the present invention to provide a marker for diagnosing ovarian cancer.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides markers for diagnosing ovarian cancer or assessing the risk of prognosis of ovarian cancer, the markers comprising AKAP12, ALDOC, ANGPTL4, CITED2, PPP1R15A, and/or PRDX 5.
Further, the markers also include ISG20 and/or TGFBI.
Further, the marker is a combination of AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI.
In a second aspect, the invention provides a reagent for detecting the level of expression of a marker according to the first aspect of the invention in a sample.
Further, the reagent includes a reagent capable of detecting the expression level of mRNA of the marker.
Alternatively, the detecting of the expression level of the mRNA of the marker is performed using any one of methods selected from the group consisting of polymerase chain reaction, real-time fluorescence quantitative reverse transcription polymerase chain reaction, competitive polymerase chain reaction, nuclease protection assay, in situ hybridization, nucleic acid microarray, northern blot, and DNA chip.
Further, the reagent includes a reagent capable of detecting the expression level of the protein encoded by the marker.
Alternatively, the expression level of the protein encoded by the detection marker is performed using any one selected from the group consisting of multiplex proximity extension assay, enzyme-linked immunosorbent, radioimmunoassay, sandwich assay, western blot, immunoprecipitation, immunohistochemical staining, fluoroimmunoassay, enzyme substrate color development, antigen-antibody aggregation, fluorescence activated cell sorting, mass spectrometry, MRM assay, assay using a panel of multiplex amine-specific stable isotope reagents, or protein chip assay.
Further, the reagent comprises:
a primer or probe that specifically binds to the marker gene;
an antibody, peptide, aptamer, or compound that specifically binds to the marker protein.
In a third aspect, the invention provides a model for the prognostic risk assessment of ovarian cancer, using the expression level of a marker according to the first aspect of the invention as an input variable.
Further, the model calculates a risk score using the following equation:
risk score ═ 0.045 × AKAP12 expression level) + (0.099 × ALDOC expression level) + (0.109 × ANGPTL4 expression level) + (0.096 × CITED2 expression level) - (0.306 × ISG20 expression level) + (0.046 × PPP1R15A expression level) - (0.169 × PRDX5 expression level) + (0.045 × TGFBI expression level)
The invention provides an ovarian cancer prognosis risk assessment system, which comprises:
1) a detection unit: a detection module comprising a marker according to the first aspect of the invention;
2) an analysis unit: inputting the expression level of the marker detected by the detection unit as an input variable into the model of the third aspect of the invention for analysis;
3) an evaluation unit: judging whether the sample corresponds to the risk of ovarian cancer prognosis of the subject.
A fifth aspect of the invention provides a use as claimed in any one of:
(1) use of an agent according to the second aspect of the invention in the manufacture of a product for the diagnosis of ovarian cancer;
(2) use of an agent according to the second aspect of the invention in the manufacture of a product for use in assessing the prognostic risk of ovarian cancer.
(3) Use of a marker according to the first aspect of the invention in the construction of a model according to the third aspect of the invention;
(4) use of a marker according to the first aspect of the invention in the construction of a system according to the fourth aspect of the invention.
Further, the product comprises a kit and a chip.
A sixth aspect of the present invention provides a computer readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer readable storage medium is located to perform the model of the third aspect of the present invention.
Drawings
FIG. 1 is a forest plot of the results of a one-factor Cox analysis of hypoxia genes;
FIG. 2 is the result of LASSO Cox analysis, wherein 2A is a plot of Lasso regression model coefficients and 2B is a plot of cross-validation results;
FIG. 3 is a risk score, OS status and heat map of hypoxia genes in the TCGA dataset, wherein 3A is a risk score map of hypoxia genes, 3B is a statistical map of OS status, and 3C is a heat map of gene expression;
FIG. 4 is a risk score, OS status and heat map of the hypoxic gene in the GEO dataset, wherein 4A is a risk score map of the hypoxic gene, 4B is a statistical map of the OS status, and 4C is a heat map of gene expression;
FIG. 5 is a graph of the results of Kaplan-Meier survival analysis, where 5A is a statistical plot of survival for patients with high and low risk scores in the TCGA dataset and 5B is a statistical plot of survival for patients with high and low risk scores in the GEO dataset;
FIG. 6 is a ROC plot for evaluating the efficiency of prediction of the gene of the present invention in survival, wherein 6A is a time-dependent ROC curve in the TCGA data set and 6B is a time-dependent ROC curve in the GEO data set;
FIG. 7 is a graph of the results of a one-way Cox analysis, wherein 7A is a graph of the results of an experiment using a one-way Cox analysis hypoxia risk signal in evaluating the independent prognostic value of OV patient OS in a TCGA dataset and 7B is a graph of the results of an experiment using a one-way Cox analysis hypoxia risk signal in evaluating the independent prognostic value of OV patient OS in a GEO dataset;
FIG. 8 is a graph of the results of a multi-factor Cox analysis, wherein 8A is a graph of the results of an experiment using a multi-factor Cox analysis hypoxia risk signal in a TCGA dataset to assess the independent prognostic value of an OV patient's OS, and 8B is a graph of the results of an experiment using a multi-factor Cox analysis hypoxia risk signal in a GEO dataset to assess the independent prognostic value of an OV patient's OS;
FIG. 9 is an electrophoretogram of PCR products;
FIG. 10 is a graph of internal reference GAPDH gene real-time amplification and product dissolution, wherein 10A is the gene real-time amplification graph and 10B is the product dissolution graph;
FIG. 11 is a graph of real-time gene amplification and product solubilization of an internal reference ACTB gene, wherein 11A is a graph of real-time gene amplification and 11B is a graph of product solubilization;
FIG. 12 is a graph showing the real-time amplification and product lysis profiles of AKAP12 gene, wherein 12A is a graph showing the real-time amplification of gene and 12B is a graph showing the product lysis;
FIG. 13 is a graph of ALDOC gene real-time amplification and product solubilization, wherein 13A is a graph of gene real-time amplification and 13B is a graph of product solubilization;
FIG. 14 is a graph of the real-time amplification of the ANGPTL4 gene and a graph of the product lysis, wherein 14A is the gene real-time amplification graph and 14B is the product lysis graph;
FIG. 15 is a graph of CITED2 gene real-time amplification and product dissolution, wherein 15A is the gene real-time amplification graph and 15B is the product dissolution graph;
FIG. 16 is a PPP1R15A gene real-time amplification graph and a product dissolution graph, wherein 16A is a gene real-time amplification graph and 16B is a product dissolution graph;
FIG. 17 is a graph of real-time amplification and product solubilization of PRDX5 gene, wherein 17A is a graph of real-time amplification of gene and 17B is a graph of product solubilization;
FIG. 18 is a graph showing the results of differential expression of genes according to the present invention.
Detailed Description
Marker substance
The invention provides markers for diagnosing ovarian cancer or assessing the prognostic risk of ovarian cancer.
In the present invention, the term "marker" means a compound, preferably a gene, which is differentially present (i.e. increased or decreased) in a biological sample from a subject or a group of subjects having a first phenotype (e.g. having a disease) compared to a biological sample from a subject or a group of subjects having a second phenotype (e.g. no disease). The term "marker" generally refers to the presence/concentration/amount of one gene or the presence/concentration/amount of two or more genes.
In the present invention, the marker comprises AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5 and/or TGFBI. Markers such as AKAP12(A-kinase amplifying protein 12, gene ID: 9590), ALDOC (aldose, free-bisphopport C, gene ID: 230), ANGPTL4 (angioplastic like 4, gene ID: 51129), CITED2(Cbp/p300 interacting promoter with Glu/Asp rich carboxyl-terminal domain 2, gene ID: 10370), ISG20 (interactive stimulated expression gene 20, gene ID: 3669), PPP1R15A (protein phosphorus 1 regulated reagent 15A, gene ID: 23645), PRDX5 (DX 5, gene ID: 25824), TGI (gene expression factor 7045); including genes and their encoded proteins and homologs, mutations, and isoforms. The term encompasses full-length, unprocessed markers, as well as any form of marker that results from processing in a cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of the marker. The gene ID is available at https:// www.ncbi.nlm.nih.gov/gene/.
The term "subject" means any animal, also human and non-human animals. The term "non-human animal" includes all vertebrates, e.g., mammals, such as non-human primates (particularly higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cattle, and any domestic or pet animal; and non-mammals, such as chickens, amphibians, reptiles, and the like.
Markers can be differentially present at any level, but are typically present at levels that are increased by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more; or generally at a level that is reduced by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% (i.e., absent).
Preferably, the markers are present at levels that are statistically significant (i.e., p-value less than 0.05 and/or q-value less than 0.10, as determined using the Welch's T-Test or the Wilcoxon rank-sum Test.
In the present invention, the term "specimen" or "test specimen" refers to a biological specimen obtained or derived from an individual of interest, the source of which may be a fresh, frozen and/or preserved organ or tissue sample or solid tissue resulting from a biopsy or primer; blood or any blood component. The term "sample" or "test sample" includes a biological sample that has been manipulated in any manner after it has been obtained, such as by reagent treatment, stabilization, or enrichment for certain components (e.g., proteins or polynucleotides), or embedding in a semi-solid or solid matrix for sectioning purposes. In one embodiment of the invention, tissue components are used as the sample.
Primer, probe, antibody, peptide, aptamer
The term "primer" as used herein is a strand of short nucleic acid sequence that recognizes the target gene sequence, and includes a pair of forward and reverse primers. In particular, the "primers" include a pair of primers that provide an assay result of specificity and sensitivity. The primer is believed to provide a high degree of specificity when used to amplify a target gene sequence, but it does not cause amplification of non-target sequences that are not identical or complementary to the target gene sequence.
The term "probe" as used herein refers to a substance that specifically binds to a target to be detected in a sample. By this binding, the probe can determine the presence of the target in the sample. Any probe can be used in the present disclosure as long as it is generally used in the art. In particular, the probe may be a PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA or DNA, most preferably a PNA. In particular, the probe is a biological material, which may be derived from an organism or may be synthesized in vitro, or a mimetic thereof. For example, the probe may be an enzyme, protein, antibody, microorganism, animal or plant cell or organ, neuron, DNA or RNA. DNA may include cDNA, genomic DNA, and oligonucleotides. Likewise, genomic RNA, mRNA, and oligonucleotides may fall within the scope of RNA. Examples of proteins include antibodies, antigens, enzymes, and peptides.
The term "antisense" as used herein refers to an oligomer having a nucleotide base sequence and a subunit-subunit backbone that allows the antisense oligomer to hybridize to a target sequence in an RNA by Watson-Crick base pairing to form an RNA: oligomer heteroduplex nucleic acid molecule in the target sequence.
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 marker 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
Reagent kit
The present disclosure provides a kit for diagnosing ovarian cancer or assessing the risk of prognosis of ovarian cancer, comprising a composition for diagnosing ovarian cancer or assessing the risk of prognosis of ovarian cancer in parkinson's disease. For example, the kit may be an RT-PCR kit, a DNA chip kit, an ELISA kit, a protein chip kit, a rapid kit, or an MRM (multiple reaction monitoring) kit.
For example, the diagnostic kit may further comprise elements necessary for reverse transcription polymerase chain reaction. The RT-PCR kit contains a pair of primers specific for the gene encoding the marker protein. Each primer is a nucleotide having a sequence specific to the nucleic acid sequence of the gene, and may be about 7 to 50bp, more particularly about 10 to 39bp in length. In addition, the kit may further comprise a primer specific for the nucleic acid sequence of the control gene. In addition, the RT-PCR kit may comprise a test tube or suitable vessel, reaction buffers (different pH values and magnesium concentrations), deoxynucleotides (dntps), enzymes (e.g., Taq polymerase and reverse transcriptase), deoxyribonuclease inhibitors, ribonuclease inhibitors, DEPC-water, and sterile water.
In addition, the diagnostic kit of the present disclosure may contain elements necessary for the operation of the DNA chip. The DNA chip kit may comprise a substrate to which a gene or cDNA or an oligonucleotide corresponding to a fragment thereof is bound, and a reagent, a drug and an enzyme for constructing a fluorescently labeled probe. In addition, the substrate may comprise a control gene or cDNA or an oligonucleotide corresponding to a fragment thereof.
In some embodiments, the kits of the present disclosure may comprise the elements necessary for performing an ELISA. The ELISA kit may comprise antibodies specific for the protein. The antibodies have high selectivity and affinity for marker proteins, are non-cross-reactive with other proteins, and may be monoclonal, polyclonal or recombinant. In addition, the ELISA kit may comprise an antibody specific for a control protein. In addition, the ELISA kit may further comprise reagents capable of detecting the bound antibody, e.g., a labeled secondary antibody, a chromophore, an enzyme (e.g., conjugated to an antibody), and substrates thereof or substances capable of binding the antibody
System, computer readable storage medium
It should be understood that "system", "apparatus", "unit" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As will be appreciated by one skilled in the art, the present invention may be embodied as an apparatus, method or computer program product. Accordingly, the present disclosure may be embodied in the form of: may be entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) and in any combination of hardware and software, and may be referred to herein generally as a "unit" or "system". Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media having computer-readable program code embodied in the medium.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
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 construction of ovarian cancer prognostic model
First, experiment method
1. Data set source and preprocessing
Public gene expression data and complete clinical annotations were searched in a gene expression integration database (GEO) and a cancer genomic profile database (TCGA). For the data set in TCGA, RNA sequencing data (FPKM values) and clinical information for gene expression were downloaded from UCSC Xena (https:// gdc. The FPKM values were then converted to million per kilobase (TPM) value transcripts. Gene expression data of GSE17260 and GSE32062 are downloaded from a GEO database (http:// www.ncbi.nlm.nih.gov/GEO /), and are annotated by using an annotation file, and an average value of a plurality of probes corresponding to the same gene is taken as an expression quantity of the gene, so that a gene expression matrix file is obtained. Two gene expression matrix files were combined into one file and the expression data from two different datasets were batch normalized using the "sva" R package. Finally, a normalized gene expression matrix file is obtained. Wherein, the TCGA data set is used as a discovery queue, the GEO data set is used as a verification queue, and the detailed information of the data set is shown in Table 1. The hypoxia-associated gene list was obtained from the marker genomes (hallmark genes sets) of the molecular characterization database, and a total of 191 genes were included in the analysis, all of which were responsive to hypoxic levels.
TABLE 1 basic information of the data sets in this study
Figure BDA0003406171130000101
Figure BDA0003406171130000111
2. Construction and verification of hypoxia gene signature
In the discovery cohort, single-factor Cox regression analysis was used to determine hypoxia genes associated with prognosis (P < 0.01). And (3) constructing a hypoxia gene signature for predicting OV patient prognosis by using a glmnet software package in the R through a Cox regression and Lasso method. In this analysis, lasso penalties are employed, taking into account both shrinkage and variable selection. The optimal value of the lambda penalty parameter is determined by 10 cross-validation. The present invention calculates the risk score for OV patients using the following formula:
Figure BDA0003406171130000112
patients were classified into high-risk and low-risk groups according to median score. In addition, the same formula is used in the validation queue to calculate the risk score.
3. Survival assay
The higher, low hypoxia risk group of OS was analyzed by Kaplan-Meier using the "survivval" and "surviviner" software packages in R. Single-factor Cox analysis was used to determine potential prognostic factors, and multi-factor Cox analysis determined risk scores as independent risk factors for OV patient OS. And generating a ROC curve through a 'timeROC' R software package so as to verify the accuracy of the risk model in predicting the life cycle of the patient.
4. Statistical analysis
R software (version 3.6.3; https:// www.R-project. org) was used for all statistics. The Wilcox test is used to screen for genes that are statistically differentially expressed. Kaplan-Meier curves were plotted and log-rank was used to test the significant difference in OS between groups. Single and multifactor Cox proportional hazards regression analysis was also performed to understand the relationship between the risk score and OS. And evaluating the sensitivity and specificity of the risk score after predicting the prognosis by adopting ROC analysis, wherein the area under the ROC curve (AUC) is an index for judging the accuracy of the prognosis. A p-value of less than 0.05 is statistically significant for all analyses.
Second, experimental results
1. The invention relates to construction of a gene signature prognosis model
A one-way Cox regression analysis was performed on the 191 hypoxic genes, and nine genes that were selected to be significantly associated with OV prognosis are shown in FIG. 1. Afterwards, the regression analysis of LASSO Cox was performed by the nine characteristic genes. An eight-gene risk model consisting of AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI was constructed, as shown in fig. 2. The risk score for the TCGA training cohort was calculated using coefficients obtained from the LASSO algorithm, and the formula is as follows:
risk score ═ 0.045 × AKAP12 expression level) + (0.099 × ALDOC expression level) + (0.109 × ANGPTL4 expression level) + (0.096 × CITED2 expression level) - (0.306 × ISG20 expression level) + (0.046 × PPP1R15A expression level) - (0.169 × PRDX5 expression level) + (0.045 × TGFBI expression level)
2. Prognostic value of Risk signals
The heat map results indicate that six of the eight hypoxia genes are highly expressed in the high risk score group, which means that high risk patients tend to develop hypoxic microenvironments. The data of the present invention also show that the mortality rate was significantly higher in the high risk group than in the low risk group, as shown in FIGS. 3 and 4. In addition, Kaplan-Meier survival analysis was performed to assess the prognostic value of OV hypoxia signals, and as a result the survival time of patients in the high risk score group was significantly shorter than in the low risk score group, as shown in figure 5. The high hypoxia risk score is related to the finding of OS differences in the cohort, which has been further validated by the validation cohort.
3. Hypoxia signature shows a strong prognostic evaluation ability
To evaluate the predictive efficiency of low oxygen signature in 1-year, 3-year, and 5-year survival, the present invention implements a Received Operating Characteristic (ROC) curve using data from the discovery queue and validation queue data sets. The area under the ROC curve (AUC) was 0.67 at 1 year, 0.64 at 3 years, and 0.71 at 5 years, respectively, indicating a higher predictive value. The GEO dataset verifies this further as shown in fig. 6. Single and multifactorial Cox are then analyzed for hypoxia risk signatures to assess the independent prognostic value of OV patients OS. Single factor analysis indicates that a high risk score is associated with poor OS. Multifactorial analysis has shown that risk scores are significantly less correlated with OS in OV patients and can be an independent prognostic factor for OV. These have been validated by the GEO database as shown in fig. 7, 8.
Example 2 Real Time PCR detection of changes in expression levels of genes of interest in ovarian cancer tissue samples
First, experiment purpose
And detecting the change of the mRNA transcription level of the target gene in the ovarian cancer tissue sample by using a SYBR Green I chimeric fluorescence method.
Second, Experimental materials
1. Sample List
The laboratory provided 10 ovarian tissue samples, of which 5 ovarian cancer tissue samples, 5 paracancer control tissues:
table 2 sample list
Sample number Sample name Sample number Sample name
1 C1 6 E1
2 C2 7 E2
3 C3 8 E3
4 C4 9 E4
5 C5 10 E5
2. Experiment main reagent
TABLE 3 list of reagents used
Figure BDA0003406171130000131
3. Experiment main instrument
TABLE 4 List of instruments used
Name of instrument Instrument type Manufacturer of the product
Electric tissue homogenizer DY89--11 NINGBO SCIENTZ BIOTECHNOLOGY Co.,Ltd.
Centrifugal machine Centrifuge 5424R Eppendorf
NanoVue Plus 28956057 BIOCHROM LTD
Fluorescent quantitative PCR instrument ABI7300 Applied Biosystems
Third, Experimental methods
1. Primer design
1.1 Real Time PCR detection of the target gene primer. The following primers were synthesized by Bomaide.
TABLE 5 primer sequences
Figure BDA0003406171130000141
Figure BDA0003406171130000151
2. Procedure of experiment
2.1 extraction of Total RNA from samples
Adding 1mL of TRIzol into a glass homogenate bottle in an ultraclean bench (the homogenate bottle is dried for 4 hours by using an oven at 180 ℃ in advance), pressing the homogenate bottle onto the instrument, weighing 50-100 mg of tissues and putting the tissues into the glass homogenate bottle, adjusting the rotation speed to about 1500 revolutions, starting homogenizing in an ice-water bath, stopping 30 seconds every 30 seconds of grinding, and repeating for 3-4 times. Sample volume should not exceed TRIzol volume 10%.
② placing the sample added with TRIzol at room temperature (15-30 ℃) for 10min to completely separate the nucleic acid protein complex.
③ 1mL of TRIzol is added with 200 μ L of chloroform, shaken vigorously for 2min, shaken again every 1 min for 5-6 times, and then kept still for 7 min.
Fourthly, centrifuging for 15min at 4 ℃ and 12000 rpm. The sample was divided into three layers: the bottom layer is a yellow organic phase, and the upper layer is a colorless aqueous phase and an intermediate layer. RNA is predominantly in the aqueous phase, with a volume of approximately 60% of the TRIzol used.
Fifthly, transferring the upper aqueous phase to a new EP tube (about 400uL, and not sucking the middle layer as much as possible to avoid pollution). Add 500. mu.L of isopropanol and let stand at room temperature for 10 min.
Sixthly, preparing 75 percent ethanol and placing the ethanol in an ice box for precooling.
Seventhly, centrifuging for 15min at 12000rpm at 4 ℃, and generating white precipitates at the bottom of the tube after centrifugation. The supernatant was carefully removed with a pipette.
And adding 1mL of 75% cold ethanol, and shaking, washing and precipitating. Centrifuge at 7500rpm for 5min at 4 ℃ and carefully discard the supernatant.
Ninthly, reversely buckling the EP pipe on the filter paper to absorb redundant water, carefully absorbing liquid in the pipe by using a 10-microliter gun head (the gun head does not contact RNA), placing the EP pipe for 5min at room temperature (the time is too long, the RNA activity is reduced due to over-drying), and enabling the RNA to become transparent;
adding 40 μ L RNase-free water (DEPC water) into the red, detecting OD value and concentration with nanodrop, and marking on the tube;
Figure BDA0003406171130000161
storing in a refrigerator at-80 deg.C.
2.2 Synthesis of mRNAcDNA by reverse transcription
mRNA reverse transcription was performed using FastKing cDNA first strand synthesis kit (cat # KR116), genomic DNA reaction was first removed, 5 Xg DNA Buffer 2.0ul, TotalRNA 1ug, and RNase Free ddH were added to the tube2O to make the total volume 10uL, heating in a water bath at 42 ℃ for 3min, and mixing 10 XKing RT Buffer 2.0uL and FastKing RT Enzyme Mix1.0uL，FQ-RT Primer Mix 2.0uL，RNase Free ddH2O5.0 uL, mixing, adding into the test tube, mixing to give 20uL, heating in water bath at 42 deg.C for 15min and 95 deg.C for 3min, and storing at-20 deg.C or lower when the synthesized cDNA is required to be stored for a long time.
2.3 RealTimePCR
2.3.1 fluorescent quantitation of mRNA
2.3.1.1 Instrument and analysis method
Using ABI 7300 type fluorescence quantitative PCR instrument and adopting 2-△△CTThe method performs a relatively quantitative analysis of the data.
2.3.1.2 the operation is as follows:
(one) reaction system: amplification was carried out using SuperReal PreMix Plus (SYBR Green) (cat # FP205) and the experimental procedures were performed according to the product instructions. The RealTime reaction system is:
TABLE 6 RealTime reaction System
Figure BDA0003406171130000162
Figure BDA0003406171130000171
(II) the amplification procedure is as follows: 95 degrees 15min, (95 degrees 10sec, 55 degrees 30sec, 72 degrees 32sec) × 40 cycles, 95 degrees 15sec, 60 degrees 60sec, 95 degrees 15 sec).
(III) primer screening
Mixing cDNA of each sample, diluting the cDNA by 10 times by taking the cDNA as a template, taking 2 mu l of each diluted sample as the template, respectively amplifying by using a target gene primer and an internal reference gene primer, simultaneously carrying out dissolution curve analysis at 60-95 ℃, and carrying out primer screening according to the principle of high amplification efficiency and single peak of the dissolution curve.
(IV) sample RealTimePCR detection
After 10-20-fold dilution of each sample cDNA, 2. mu.l of each sample cDNA was used as a template, and amplified with the target gene primer and the reference gene primer, respectively (see Table 7). At the same time, the dissolution curve analysis is carried out at 60-95 ℃.
TABLE 7 sample RealTimePCR detection design
Form panel Sample cDNA Sample cDNA
Repeatedly detecting the number of channels 3 3
Primer and method for producing the same Target gene primer Internal reference gene primer
Fourth, experimental results
RNA concentration detection results and 1.5% agarose RNA electrophoresis detection results
TABLE 8 RNA concentration and purity results
Figure BDA0003406171130000172
Figure BDA0003406171130000181
Note: the RNA dissolved in water results in a low A260/280 ratio
Note: a, the concentration does not reach the standard; b, unqualified A260/A280; c, unqualified electrophoretogram; h, qualified
Sample evaluation standard:
(1) the concentration is more than 30ng/ul
(2)1.8<A260/A280<2.0
(3) The electrophoretic pattern shows three distinct bands (the third band may not be visible)
(4) A clearer strip can be seen in the electrophoretogram of the product after RT-PCR
TABLE 9 electrophoretic Loading
Figure BDA0003406171130000182
Figure BDA0003406171130000191
The electrophoretogram of the PCR product is shown in FIG. 9, in which the sequence of the electrophoretograms of the PCR product is: marker, C1, E1, C2, E2, C3, E3, C4, E4, C5 and E5. C1, E1, C2, E2, C3, E3, C4, E4, C5 and E510 samples are finally selected according to the running glue fruit to carry out real-time quantitative experiments. Mark: DNA Marker: DM2000, 100,250,500,750,1000 and 2000bp from bottom to top in sequence, wherein 750bp is a bright band.
2. Results and analysis of RealTimePCR detection for each sample
2.1 real-time amplification profiles for each sample and dissolution profiles for sample amplification products
The real-time amplification curve and the product dissolution curve of the internal reference GAPDH gene are shown in FIG. 10; the real-time amplification curve and the product dissolution curve of the internal reference ACTB gene are shown in FIG. 11; the real-time amplification curve and the product dissolution curve of the AKAP12 gene are shown in FIG. 12; the ALDOC gene real-time amplification graph and the product dissolution graph are shown in FIG. 13; the graph of the real-time amplification of the ANGPTL4 gene and the graph of the product dissolution are shown in FIG. 14; the real-time amplification curve and the product dissolution curve of the CITED2 gene are shown in FIG. 15; the real-time amplification curve and the product dissolution curve of the PPP1R15A gene are shown in FIG. 16; the real-time amplification graph and the product dissolution graph of the PRDX5 gene are shown in figure 17.
2.2 relative quantitative analysis results of each sample
According to the original detection result of RealTimePCR
Figure BDA0003406171130000192
Relative quantitative calculation formula, i.e.
Figure BDA0003406171130000201
The relative quantification of the gene of interest in each sample, i.e., the difference in the mRNA transcription level of the gene of interest in each of the other samples relative to the control sample, is calculated.
As shown in fig. 18, ALDOC, CITED2, and PRDX5 were significantly up-regulated in ovarian cancer tissue, and AKAP12, ANGPTL4, and PPP1R15A were significantly down-regulated in ovarian cancer tissue, compared to the control group.
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. Marker for diagnosing ovarian cancer or assessing the risk of prognosis of ovarian cancer, wherein the marker comprises AKAP12, ALDOC, ANGPTL4, CITED2, PPP1R15A and/or PRDX5, preferably the marker further comprises ISG20 and/or TGFBI, preferably the marker is a combination of AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5 and TGFBI.
2. An agent which detects the level of expression of the marker of claim 1 in a sample.
3. The reagent according to claim 2, wherein the reagent comprises a reagent capable of detecting the expression level of mRNA of the marker,
preferably, the detecting of the expression level of the marker mRNA is performed using any one of methods selected from the group consisting of polymerase chain reaction, real-time fluorescence quantitative reverse transcription polymerase chain reaction, competitive polymerase chain reaction, nuclease protection assay, in situ hybridization, nucleic acid microarray, northern blot, and DNA chip.
4. The reagent according to claim 2, wherein the reagent comprises a reagent capable of detecting the expression level of a protein encoded by the marker,
preferably, the expression level of the protein encoded by the detection marker is performed using any one selected from the group consisting of multiplex proximity extension assay, enzyme-linked immunosorbent, radioimmunoassay, sandwich assay, western blot, immunoprecipitation, immunohistochemical staining, fluoroimmunoassay, enzyme substrate color development, antigen-antibody aggregation, fluorescence activated cell sorting, mass spectrometry, MRM assay, assay using a panel of multiplex amine-specific stable isotope reagents, or protein chip assay.
5. The reagent of claim 2, wherein the reagent comprises:
a primer or probe that specifically binds to the marker gene;
an antibody, peptide, aptamer, or compound that specifically binds to the marker protein.
6. An ovarian cancer prognostic risk assessment model using the marker expression level of claim 1 as an input variable, preferably the model calculates a risk score using the following equation:
risk score ═ 0.045 × AKAP12 expression level) + (0.099 × ALDOC expression level) + (0.109 × ANGPTL4 expression level) + (0.096 × CITED2 expression level) - (0.306 × ISG20 expression level) + (0.046 × PPP1R15A expression level) - (0.169 × PRDX5 expression level) + (0.045 × TGFBI expression level)
7. An ovarian cancer prognosis risk assessment system comprising:
1) a detection unit: a detection module comprising the marker of claim 1;
2) an analysis unit: inputting the expression level of the marker detected by the detection unit as an input variable into the model of claim 6 for analysis;
3) an evaluation unit: judging whether the sample corresponds to the risk of ovarian cancer prognosis of the subject.
8. Use according to any one of the following:
(1) use of an agent according to any one of claims 2 to 5 in the manufacture of a product for the diagnosis of ovarian cancer;
(2) use of an agent according to any one of claims 2 to 5 in the manufacture of a product for use in assessing the prognostic risk of ovarian cancer.
(3) Use of the marker of claim 1 in the construction of the model of claim 6;
(4) use of the marker of claim 1 in the construction of the system of claim 7.
9. The use of claim 8, wherein the product comprises a kit or chip.
10. A computer-readable storage medium comprising a stored computer program, wherein the computer program when executed controls an apparatus in which the computer-readable storage medium resides to execute the risk assessment model of claim 6.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170153240A1 (en) * 2015-11-30 2017-06-01 Sungkwang Medical Foundation Composition, kit, and method for diagnosing and treating ovarian cancer
CN112680523A (en) * 2021-01-25 2021-04-20 复旦大学附属中山医院 Molecular model for judging prognosis of ovarian cancer patient and application
CN113096739A (en) * 2021-04-09 2021-07-09 东南大学 Analysis method of immune prognosis diagnosis marker combination of ovarian cancer

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170153240A1 (en) * 2015-11-30 2017-06-01 Sungkwang Medical Foundation Composition, kit, and method for diagnosing and treating ovarian cancer
CN112680523A (en) * 2021-01-25 2021-04-20 复旦大学附属中山医院 Molecular model for judging prognosis of ovarian cancer patient and application
CN113096739A (en) * 2021-04-09 2021-07-09 东南大学 Analysis method of immune prognosis diagnosis marker combination of ovarian cancer

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