阅读说明：本技术 预测肾癌患者预后的生物标志物 (Biomarkers for predicting prognosis of renal cancer patients ) 是由 杨承刚 王丹 刘乐凯 于 2021-06-30 设计创作，主要内容包括：本发明公开了预测肾癌患者预后的生物标志物,本发明的生物标志物包括免疫相关基因PLAUR、PLTP、RNASE2、RORA,通过检测生物标志物的表达水平,可以实现肾癌患者生存期的预测。(The invention discloses a biomarker for predicting the prognosis of a renal cancer patient, the biomarker comprises immune-related genes PLAUR, PLTP, RNASE2 and RORA, and the prediction of the survival period of the renal cancer patient can be realized by detecting the expression level of the biomarker.)

1. A biomarker panel for the prognosis of kidney cancer, wherein the biomarker panel comprises PLAUR, PLTP, RNASE2 and/or RORA.

2. Use of a reagent for detecting the biomarker profile of claim 1 in a sample for the preparation of a diagnostic product for the prognosis of renal cancer.

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

4. The use according to claim 2, wherein the sample is selected from the group consisting of: peripheral blood samples, serum samples, plasma samples, urine samples, saliva samples, and tissue samples.

5. A diagnostic product for the prognosis of renal cancer, comprising reagents for detecting the biomarker panel of claim 1;

preferably, the diagnostic product comprises a chip, a kit.

6. The diagnostic product of claim 5, wherein the reagents comprise reagents that detect the amount of RNA transcribed from the genes of the biomarker panel;

preferably, the reagent is a reagent that detects the amount of mRNA transcribed from the biomarker population;

preferably, the reagent is a reagent for detecting the amount of cDNA complementary to mRNA transcribed from the gene;

preferably, the reagents comprise probes or primers;

preferably, the diagnostic product further comprises a total RNA extraction reagent, a reverse transcription reagent and/or a secondary sequencing reagent.

7. The diagnostic product of claim 5, wherein the agent is an agent that detects the amount of polypeptides/proteins encoded by the biomarker panel genes;

preferably, the agent is an antibody, an antibody fragment or an affinity protein.

8. A kidney cancer survival prediction device, comprising:

a data acquisition module for acquiring the gene expression profile data of the biomarker group of claim 1 of a cancer patient to be tested;

a prediction module to provide the gene expression profile data as input to a trained prediction model trained to predict the survival of a cancer patient based on gene expression profile data of the cancer patient;

the prediction result acquisition module is used for acquiring the output of the prediction model to obtain the life cycle prediction result of the cancer patient to be detected;

preferably, the prediction model is a Cox regression model;

preferably, the Cox regression model is a LASSOCox regression model;

preferably, the formula of the prediction model isWherein N is the number of genes for predicting prognosis, Expi is the expression level of each gene, and Ci is the regression coefficient of each gene; when the risk score is higher, the patient has a poor prognosis; when the risk score is low, the patient prognosis is good;

preferably, the genes are PLAUR, PLTP, RNASE2 and RORA;

preferably, the C1, C2, C3 and C4 are respectively 0.335, 0.0776, 0.1213 and-0.6463.

9. A computer device comprising a memory and a processor, the memory storing a program, the processor implementing the method when executing the program:

obtaining gene expression profiling data of the biomarker panel of claim 1 for a cancer patient to be tested;

providing the gene expression profile data as input to a trained prediction model;

outputting the survival period prediction result of the cancer patient to be detected;

preferably, the prediction model is a Cox regression model;

preferably, the Cox regression model is a LASSOCox regression model;

preferably, the formula of the prediction model isWherein N is the number of genes used for predicting prognosis, ExpiCi is the regression coefficient for each gene for the expression level of each gene, and the patient has a poor prognosis when the risk score is higher; when the risk score is low, the patient prognosis is good;

preferably, the genes are PLAUR, PLTP, RNASE2 and RORA;

preferably, the C1, C2, C3 and C4 are respectively 0.335, 0.0776, 0.1213 and-0.6463.

10. A computer-readable storage medium on which a program is stored, the program, when executed, implementing a method of:

obtaining gene expression profiling data of the biomarker panel of claim 1 for a cancer patient to be tested;

providing the gene expression profile data as input to a trained prediction model;

outputting the survival period prediction result of the cancer patient to be detected;

preferably, the prediction model is a Cox regression model;

preferably, the Cox regression model is a LASSOCox regression model;

preferably, the formula of the prediction model isWherein N is the number of genes for predicting prognosis, Expi is the expression level of each gene, and Ci is the regression coefficient of each gene; the genes are PLAUR, PLTP, RNASE2 and RORA;

preferably, the C1, C2, C3 and C4 are respectively 0.335, 0.0776, 0.1213 and-0.6463.

Technical Field

The present invention relates to the field of disease diagnosis, more specifically, the present invention relates to biomarkers for predicting the prognosis of renal cancer patients.

Background

Renal cancer (RCC) is one of the most common malignancies of the urinary system and is classified into multiple subtypes according to histological classification, with Clear Cell Carcinoma (CCRCC) being the major subtype of kidney cancer, accounting for about 75-80% of kidney cancer (Pavlovich CP, Schmidt LS, Phillips JL. the genetic basic side of Renal Cell Carcinoma [ J ]. Urol Clin North Am, 2003,30(3):437-54, vii.), other types including papillary Carcinoma, chromophobe Carcinoma, Bellini canal (collecting canal) Carcinoma, etc. Patients with renal clear cell carcinoma had the highest mortality and metastasis rates, with about 400,000 new cases of renal clear cell carcinoma worldwide in 2018, and 175,000 cases of death (Lindenberg L, Mena E, Choyke PL, et al. pet imaging in secondary cancer [ J ]. Curr Opin Oncol,2019,31(3):216 + 221.). The predominant mode of treatment for renal clear cell carcinoma is surgical resection, but patients receiving resection have a postoperative recurrence rate of up to 40% or more (Rossi SH, Klatte T, Usher-Smith J, et al. epidemiology and screening for renal cancer [ J ]. World J Urol, 2018,36(9): 1341-1353.). Renal clear cell carcinoma has no obvious characteristics in the early stage and lacks effective diagnosis methods and early warning signals, about 30 percent of patients have long-term metastasis when first seen, and the 5-year survival rate is reduced from 90 percent to 12 percent (Campbell S, Uzzo RG, Allaf ME, et al. Renal Mass and Localized Renal Cancer: AUA Guideline [ J ] J Urol,2017,198(3): 520-529.).

The tumor immunotherapy is an innovative cancer treatment method after surgical treatment, chemotherapy, radiotherapy and targeted treatment, and has the action mechanism of enhancing the anti-tumor immune response of an organism by activating immune cells in vivo, specifically eliminating tumor tiny residual focuses, inhibiting tumor growth, breaking immune tolerance and achieving the purpose of controlling and killing tumor cells. The traditional cancer treatment method can not only kill tumor cells, but also cause unnecessary damage to normal tissue cells of the body, influence the physical condition of patients and ensure the treatment effect difficultly. With the development of the disciplines of molecular biology, medical immunology and the like, the tumor immunotherapy is more and more mature, and has made a major breakthrough in the treatment of malignant tumors such as melanoma, acute lymphatic leukemia, lung cancer and the like (Cuevas LM, Daud AI. immunotherapy for melanoma [ J ]. Semin Cutan Med Surg,2018,37(2): 127-.

The kidney cancer is a tumor with stronger immunogenicity, and the immunogenicity enables the kidney cancer to have wide immunotherapy prospect. Although immunotherapy has made great progress in the treatment of kidney Cancer, not all patients can benefit from it, there are problems with some patients not responding to immune checkpoint inhibitor treatment, some patients not developing Resistance long after dosing, and immune-related toxicity (Nowick TS, Hu-Lieskovan S, Ribas A. mechanism of Resistance PD-1 and PD-L1 Block [ J ]. Cancer J, 2018,24(1): 47-53.). Therefore, there is a need to develop new effective immunotherapy targets, which can increase the efficacy and reduce the side effects by combination therapy and the like. With the increasing understanding of the immune microenvironment of renal cancer gene combination by next-generation sequencing, treatment selection can be guided by starting from renal cancer genome characteristics, so that the development of precise medicine is advanced, and therefore, better understanding of the molecular mechanism and immune microenvironment of the disease and recognition of better prognostic biomarkers and prediction models are urgently needed.

Disclosure of Invention

The object of the present invention is to provide a biomarker panel, a diagnostic product, a device and an apparatus for the prognosis of kidney cancer.

In order to achieve the above objects, the present invention provides, in a first aspect, a biomarker population for the prognosis of kidney cancer, the biomarker population comprising PLAUR, PLTP, RNASE2 and/or RORA.

In a second aspect, the present invention provides the use of a reagent for detecting a biomarker population according to the first aspect of the present invention in a sample for the preparation of a diagnostic product for the prognosis of renal cancer.

Further, the reagent comprises a reagent for detecting the expression level of the biomarker in the sample by a digital imaging technology, a protein immunization technology, a dye technology, a nucleic acid sequencing technology, a nucleic acid hybridization technology, a chromatographic technology and a mass spectrometry technology.

Further, the sample is selected from the group consisting of: peripheral blood samples, serum samples, plasma samples, urine samples, saliva samples, and tissue samples.

In a third aspect, the present invention provides a diagnostic product for the prognosis of renal cancer, the diagnostic product comprising reagents for detecting the biomarker panel according to the first aspect of the present invention.

Further, the diagnostic product comprises a chip and a kit.

Further, the reagents include reagents that detect the amount of RNA transcribed from the biomarker panel genes.

Further, the reagent is a reagent that detects the amount of mRNA transcribed from the biomarker population.

Further, the reagent is a reagent for detecting the amount of cDNA complementary to mRNA transcribed from the gene.

Further, the reagent includes a probe or a primer.

Furthermore, the diagnostic product also comprises a total RNA extraction reagent, a reverse transcription reagent and/or a second generation sequencing reagent.

Further, the reagent is a reagent for detecting the amount of the polypeptide/protein encoded by the biomarker group gene.

Further, the reagent is an antibody, an antibody fragment, or an affinity protein.

The fourth aspect of the present invention provides a renal cancer survival prediction apparatus, including:

a data acquisition module, configured to acquire gene expression profile data of the biomarker group according to the first aspect of the present invention from a cancer patient to be detected;

a prediction module for providing the gene expression profile data as input to a trained prediction model trained to predict a life span of a cancer patient based on gene expression profile data of the cancer patient;

and the prediction result acquisition module is used for acquiring the output of the prediction model to obtain the life cycle prediction result of the cancer patient to be detected.

Further, the prediction model is a Cox regression model.

Further, the Cox regression model is a LASSO Cox regression model.

Further, the formula of the prediction model isWherein N is the number of genes used for predicting prognosis, Expi is the expression level of each gene, and Ci is the regression coefficient of each gene; when the risk score is higher, the patient has a poor prognosis; when the risk score is low, the patient prognosis is good.

Further, the genes are PLAUR, PLTP, RNASE2 and RORA.

Further, the C1, the C2, the C3 and the C4 are respectively 0.335, 0.0776, 0.1213 and-0.6463.

A fifth aspect of the present invention provides a computer device comprising a memory and a processor, the memory storing a program, the processor implementing the following method when executing the program:

obtaining gene expression profile data of a biomarker panel according to the first aspect of the present invention from a cancer patient to be tested;

providing the gene expression profile data as input to a trained prediction model;

and outputting the survival period prediction result of the cancer patient to be detected.

Further, the prediction model is a Cox regression model.

Further, the Cox regression model is a LASSO Cox regression model.

Further, the formula of the prediction model isWherein N is the number of genes used for predicting prognosis, Expi is the expression level of each gene, Ci is the regression coefficient of each gene, and when the risk score is higher, the prognosis of the patient is poor; when the risk score is low, the patient prognosis is good.

Further, the genes are PLAUR, PLTP, RNASE2 and RORA.

Further, the C1, the C2, the C3 and the C4 are respectively 0.335, 0.0776, 0.1213 and-0.6463.

A sixth aspect of the present invention provides a computer-readable storage medium having stored thereon a program which, when executed, implements a method of:

obtaining gene expression profile data of a biomarker panel according to the first aspect of the present invention from a cancer patient to be tested;

providing the gene expression profile data as input to a trained prediction model;

and outputting the survival period prediction result of the cancer patient to be detected.

Further, the prediction model is a Cox regression model.

Further, the Cox regression model is a LASSO Cox regression model.

Further, the formula of the prediction model isWherein N is the number of genes used for predicting prognosis, Expi is the expression level of each gene, and Ci is the regression coefficient of each gene; the genes are PLAUR, PLTP, RNASE2 and RORA.

Further, the C1, the C2, the C3 and the C4 are respectively 0.335, 0.0776, 0.1213 and-0.6463.

The invention has the advantages and beneficial effects that:

according to the invention, the selected biological markers include PLAUR, PLTP, RNASE2 and/or RORA, so that the prognosis of a renal cancer patient can be effectively predicted, and early intervention and early treatment can be realized.

Drawings

FIG. 1 is a graph of survival curves for the combined prediction of lung adenocarcinoma prognosis in training sets of PLAUR, PLTP, RNASE2 and RORA;

FIG. 2 is a graph of survival after combined prediction of lung adenocarcinoma prognosis for validation sets of PLAUR, PLTP, RNASE2 and RORA;

FIG. 3 is a ROC plot of the combined prediction of lung adenocarcinoma prognosis in training sets of PLAUR, PLTP, RNASE2 and RORA;

FIG. 4 is a ROC plot demonstrating the joint prediction of lung adenocarcinoma prognosis by a validation set of PLAUR, PLTP, RNASE2 and RORA.

Detailed Description

The present invention provides, in part, kits, genetic features, and methods of detecting such genetic features/biomarkers to perform analyses of renal cancer tissue samples, and in one aspect, the present invention provides genetic features related to renal cancer survival that can classify the risk of poor prognosis in an individual, helping guide physicians in selecting treatment strategies.

As used herein, "and/or" should be viewed as specifically disclosing each of the two specified features or components, with or without the other. For example, "a and/or B" will be considered a specific disclosure of each of (i) a, (ii) B, and (iii) a and B, as if each were individually listed herein.

As used herein, the terms "biomarker", "marker" and "genetic characteristic" are interchangeable and refer to a molecule that is differentially present in a sample taken from a subject with a good prognosis of renal cancer as compared to a comparable sample taken from a control subject, e.g., a subject with a poor prognosis of renal cancer. Thus, the biomarkers of the invention provide information about the likely course of kidney cancer and correlate with the prognosis of kidney cancer.

The term "biomarker" refers to any of the individual biomarkers described above, preferably PLAUR, PLTP, RNASE2 or RORA, or any biomarker combination thereof.

In some embodiments, the genetic signature is capable of classifying the prognosis of the individual. As used herein, prognosis refers to prediction of medical outcome and can be used to determine a treatment or diagnostic work schedule based on metrics such as overall survival, renal cancer specific survival, recurrence-free (recurrence) survival, recurrence-free (relapse) survival, and distal recurrence-free survival.

In some embodiments, as understood by those of skill in the art, when the renal cancer prognostic genetic signature consists of the above-described genes, the method for performing the analysis may include measuring the expression of other genes (e.g., for normalization), but classifying the individual using only the genetic signature.

The PLAUR gene: plasmagen activator, urokinase receiver, expressed as gene ID: 5329 typical homo sapiens mRNA and protein sequences can be found in the NCBI database.

PLTP gene: phospholipid transfer protein, in gene ID: 5360 typical homo sapiens mRNA and protein sequences can be found in the NCBI database.

RNASE2 gene: ribonuclear A family member 2, with gene ID: 6036 typical homo sapiens mRNA and protein sequences can be found in NCBI databases.

The RORA gene: RAR related orphan receiver a, with gene ID: 6095 typical homo sapiens mRNA and protein sequences can be found in the NCBI database.

Sample(s)

As used herein, "sample" may refer to a biological sample, typically a clinical sample, and includes, for example, blood and other bodily fluids, including but not limited to peripheral blood, serum, plasma, urine, and saliva; and solid tissue samples, such as biopsy specimens, particularly those containing cancer cells. In certain embodiments, a blood sample, such as a serum or plasma sample, is the most preferred type of sample to be used in the present methods. In general, the sample to be analyzed obtained from the subject is not part of the prognostic methods of the present invention.

The term "sample" also includes samples that have been manipulated or processed in any suitable manner after purchase, including, but not limited to, centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, washing, or enrichment of certain components of the sample, such as a cell population.

Biomarkers

As used herein, "biomarker" refers to a biological molecule present in an individual at different concentrations that can be used to predict the cancer status 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.

Gene expression

Herein, the term "level" is used interchangeably with the terms "amount" and "concentration" when applied to a biomarker, and may refer to either an absolute or relative amount of the biomarker.

The expression level of any of the biomarkers of the invention can be determined by a variety of techniques. In particular, expression at the nucleic acid level may be determined by measuring the amount of RNA, preferably mRNA or any other RNA species, representative of the biomarker in question, using methods well known in the art. Non-limiting examples of suitable methods include digital PCR and real-time (RT) quantitative or semi-quantitative PCR. Primers suitable for these methods can be readily designed by the skilled artisan.

In real-time pcr (qpcr), the reaction is characterized by the point in time during the cycle at which amplification of the target is first detected, rather than the amount of target accumulated over a fixed number of cycles. This point when a signal is first detected is called the threshold period (Ct). In some embodiments, the expression of gene signatures are quantified relative to each other by normalizing the expression of housekeeping genes by subtracting the Ct of the signature gene from the average Ct of the housekeeping genes.

Other suitable techniques for determining the expression level of any one of the biomarkers of the invention at the nucleic acid level include, but are not limited to, Fluorescence Activated Cell Sorting (FACS) and in situ hybridization.

Other non-limiting ways of measuring the amount of RNA, preferably mRNA or any other RNA species, representative of the biomarker in question include transcriptome methods, in particular DNA microarrays. Typically, when the amount of mRNA is to be determined, test and control mRNA samples are reverse transcribed and labeled to generate cDNA probes. The probes are then hybridized to an array of complementary nucleic acids immobilized on a solid support. The array is configured to know the sequence and location of each member of the array. Hybridization of a labeled probe to a particular array member indicates that the sample from which the probe was derived expresses the gene. Non-limiting examples of commercially available microarray systems include Affymetrix GeneChipTM and Illumina BeadChip.

Furthermore, bulk RNA sequencing, single cell RNA sequencing or cDNA sequencing, e.g. by Next Generation Sequencing (NGS) methods, may also be used to determine the expression level of any one of the biomarkers of the invention.

Changes in the modulation of the activity of the gene encoding the biomarker in question can be determined by epigenetic analysis, such as histone modification analysis, for example by chromatin immunoprecipitation followed by sequencing or quantitative PCR, or by quantifying the level of DNA methylation, for example by bisulfite sequencing or capture-based methods, at intergenic regulatory sites or gene regions of the biomarker in question.

It will be apparent to the skilled person that a variety of techniques can be used to determine the expression level of any one of the biomarkers of the invention at the protein level. Non-limiting examples of suitable methods include mass spectrometry-based quantitative proteomics techniques, such as Isobaric Tagging (iTRAQ) and label-free analysis of relative and absolute quantitation reagents, and Selective Reaction Monitoring (SRM) mass spectrometry and any other technique that targets proteomics. Furthermore, the level or amount of a protein marker can be determined, for example, by an immunoassay (e.g., ELISA or) Western blotting, spectrophotometry, enzymatic assay, ultraviolet assay, kinetic assay, electrochemical assay, colorimetric assay, turbidimetric assay, atomic absorption assay, flow cytometry, mass spectrometry flow cytometry or any combination thereof. Other suitable analytical techniques include, but are not limited to, liquid chromatography, such as high performance/High Pressure Liquid Chromatography (HPLC), gas chromatography, nuclear magnetic resonance spectroscopy, related techniques and combinations and mixtures thereof, such as tandem liquid chromatography-mass spectrometry (LC-MS).

The term "primer" as used herein refers to a nucleic acid fragment comprising 5 to 100 nucleotides, preferably 15 to 30 nucleotides, capable of initiating an enzymatic reaction (e.g., an enzymatic amplification reaction).

The term "probe" as used herein refers to a nucleic acid sequence comprising at least 5 nucleotides, e.g., 5 to 100 nucleotides, that hybridizes under specified conditions to an expression product of a target gene or an amplification product of the expression product to form a complex. The hybridization probes may also include labels for detection. Such labels include, but are not limited to, labels for fluorescent quantitative PCR or fluorescent in situ hybridization. In a preferred embodiment, the label may be FAM, HEX, VIC, Cy5, or the like. In another preferred embodiment, the label may be biotin, digoxigenin, or the like.

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 "polypeptide" refers to a compound consisting of amino acids joined together by peptide bonds, including full-length or amino acid fragments of a polypeptide. The amount of polypeptide encoded by the gene can be normalized to the amount of total protein in the sample or the amount of polypeptide encoded by the housekeeping gene.

Prognosis

As used herein, the term "prognosis" refers to the likely course of the disease or clinical outcome, while the expressions "anticipation", "making a prognosis", "determining a prognosis", etc., refer to the prediction of the future progression of the renal cancer.

As used herein, the terms "good prognosis" and "positive prognosis" refer to survival that may be statistically significantly prolonged compared to the median outcome of the disease or to the survival of a subject with a poor prognosis, e.g., prolonged overall survival, prolonged disease-free survival, prolonged relapse-free survival or prolonged progression-free survival.

As used herein, the term "poor prognosis" refers to survival that may be statistically significantly reduced, e.g., reduced overall survival, disease-free survival, relapse-free survival or progression-free survival, as compared to a subject with a good prognosis.

According to the present invention, in a biological sample obtained from a subject who is to be prognosticated for kidney cancer, prognosis is performed based on the detected level of a biomarker associated with the prognosis of kidney cancer. This is also meant to include situations where the prognosis is not finalized but further testing is required. In such embodiments, the method does not itself determine the prognosis of the subject's kidney cancer, but may indicate that further testing is required or would be beneficial. Thus, the present method may be combined with one or more other methods to ultimately determine a prognosis. Such other methods are well known to those skilled in the art and include, but are not limited to, biopsy, molecular characterization of tumors, computed tomography, magnetic resonance imaging, and positron emission tomography, as well as monitoring the level of carcinoembryonic antigen (CEA). Other predictive markers that may be used in combination with the present invention include, but are not limited to, molecular profiling of tumors, examining the chromosomal stability of tumors (microsatellite stability (MSS) and microsatellite instability (MSI)).

In some embodiments, the methods of the invention for prognosing kidney cancer in a subject suffering from kidney cancer may further comprise a therapeutic intervention. Once a subject is identified as having a given likely outcome of a disease, he/she may be subjected to appropriate therapeutic intervention, such as chemotherapy. In such embodiments, the invention can also be configured as a method of treating a renal cancer in a subject in need thereof, wherein the method comprises prognosing the renal cancer as described above, and administering one or more suitable chemotherapeutic agents to the subject.

The invention provides a life cycle prediction device and computer equipment. As described above, it can be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments can be implemented by a computer program, which can be stored in a non-volatile computer readable storage medium, to instruct related hardware, and when executed, the computer program can include the processes of the embodiments of the methods. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The computer according to the present invention is a computing device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof may include at least one memory, at least one processor, and at least one communication bus. Wherein the communication bus is used for realizing connection communication among the elements. The processor may include, but is not limited to, a microprocessor. Computer hardware may also include Application Specific Integrated Circuits (ASICs), Programmable gate arrays (FPGAs), Digital Signal Processors (DSPs), embedded devices, etc. The computer may also include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) -based Cloud consisting of a large number of hosts or network servers, wherein Cloud Computing is one of distributed Computing, a super virtual computer consisting of a collection of loosely coupled computers.

The computing device may be, but is not limited to, any terminal such as a personal computer, a server, etc. capable of human-computer interaction with a user through a keyboard, a touch pad, a voice control device, etc. The computing device herein may also include a mobile terminal, which may be, but is not limited to, any electronic device capable of human-computer interaction with a user through a keyboard, a touch pad, or a voice control device, for example, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a smart wearable device, and other terminals. Networks in which the computing device is located include, but are not limited to, the internet, wide area networks, metropolitan area networks, local area networks, Virtual Private Networks (VPNs), and the like.

The memory is for storing program code. The Memory may be a circuit without a physical form and having a Memory function in an integrated circuit, such as a RAM (Random-Access Memory), a fifo (first InFirst out), and the like. Alternatively, the memory may be a memory in a physical form, such as a memory bank, a TF Card (Trans-flash Card), a smart media Card (smart media Card), a secure digital Card (secure digital Card), a flash memory Card (flash Card), and so on.

The processor may include one or more microprocessors, digital processors. The processor may call program code stored in the memory to perform the associated functions. For example, the respective modules are program codes stored in the memory and executed by the processor to implement the above-described method. The processor is also called a Central Processing Unit (CPU), and may be an ultra-large scale integrated circuit, which is an operation Core (Core) and a Control Core (Control Unit).

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required to implement the invention.

The following detailed description of embodiments of the present application will be made with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present application, are given by way of illustration and explanation only, and are not intended to limit the present application.

Example biomarkers associated with renal cancer prognosis

1. Data download

Public gene expression data and complete clinical annotations of renal clear cell carcinoma were searched in a cancer genomic profile database (TCGA), excluding samples lacking survival or having a survival of 0, with a total number of 531 included samples, randomly divided into 371 as training set and 160 as validation set.

2. Data normalization

RNA-seq data for TCGA were normalized using Voom method, genes were annotated using the tidyverse package in R, de-duplication averages were pooled, and RNA sequencing data for gene expression (FPKM values) were converted to transcripts with million per kilobase (TPM) values.

3. Screening for immune-related genes

Immune-related genes from immport database(s) ((https://www.immport.org/home). Genes co-existing in the immport database with TCGA were screened.

4. One-factor Cox analysis

And (3) carrying out single-factor Cox analysis on the genes in the training set and the verification set by using a survivval package and a survivor package in the R, and screening the genes related to the survival of the kidney cancer patient in the two data sets, wherein the gene with the P <0.001 is considered to have an influence on the survival of the kidney cancer patient.

5. LASSO Cox regression analysis

And performing LASSO Cox regression analysis to construct a LASSO regression model. Using the data of the training set, performing regression analysis modeling on the data of the training set by using a glmnet packet and a survivval packet in the R language, and outputting a correlation coefficient and a risk score (risk score) of a prediction model, wherein seed is set to 2.

And calculating the risk score of each sample by using the same formula when verification is carried out in the verification set, dividing all samples into a high risk group and a low risk group according to the median of the risk score, and further carrying out survival analysis.

6. Survival Curve analysis

The R software 'survivval', 'surviviner' and 'ggplot 2' packages are adopted to carry out survival analysis on the renal cancer patients in the high risk group and the low risk group of the training set and the verification set, and the survival curves are drawn, and the inter-group difference comparison is carried out through log-rank test.

7. ROC curve analysis

In order to evaluate the accuracy of the prognosis model in predicting the kidney cancer prognosis, the R software 'survivval' and 'timeROC' packages are adopted to detect the prognosis efficiencies of the biomarkers for 1 year, 3 years and 5 years by using time-dependent ROC curves, the significance of the difference between various groups of ROC curves is detected by using a self-sampling method, and the P <0.05 is considered to have statistical difference.

8. Results

The results of the gene one-way Cox regression analysis and LASSO coefficients in the risk scores are shown in table 1,

TABLE 1 genes associated with prognosis

Risk score 0.335 × ExpPLAUR +0.0776 × ExpPLTP +0.1213 × expnase 2-0.6463 × ExpRORA.

Renal cancer patients were analyzed in the high risk group (high score) and the low risk group (low score) according to the median of the risk scores, and by KM survival analysis, the difference in survival time of the two groups was compared, and it was found that the cumulative survival rate of patients in the high risk group was significantly lower than that in the low risk group. Consistent with the results of the training set (fig. 1), the cumulative survival of patients in the high risk group was significantly lower than that in the low risk group (fig. 2).

The renal cancer patients in the training set and the verification set are subjected to prognostic ROC curve analysis, and the results show that the risk score prognostic model has better distinguishing performance on the prognosis of the renal cancer patients (FIG. 3 and FIG. 4).

In conclusion, the survival/prognosis of kidney cancer can be predicted based on the gene of the present invention.

The preferred embodiments of the present application have been described in detail with reference to the accompanying drawings, however, the present application is not limited to the details of the above embodiments, and many simple modifications may be made to the technical solution of the present application within the technical idea of the present application, and these simple modifications are within the scope of protection of the present application.

It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described in the present application.

In addition, any combination of the various embodiments of the present application can be made, and the same should be considered as the disclosure of the present application as long as the idea of the present application is not violated.