Construction method of cancer prognosis prediction model and application thereof

Abstract

The application discloses a construction method of a cancer prognosis prediction model and application thereof. The application provides a construction method of a cancer prognosis prediction model, a device and a system for predicting cancer prognosis. The model, the system and the device provided by the application can effectively predict the prognosis of a gastric cancer patient, and early intervention and early treatment are realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of biological medicine, and particularly relates to a construction method of a cancer prognosis prediction model and application thereof. BACKGROUND

[0002] Gastric cancer usually has no symptoms in the early stage, and the survival rate of some patients is only 24% when they are diagnosed (Tang Y Y, Song J, Miao S Q, Cai J, Song F Z. Identification and prognosis-related key genes in gastric cancer by comprehensive bioinformatics analysis [J]. Journal of the Army Medical University, 2022, 44(06):522-532.). The cornerstone of treatment is surgical resection, and the use of adjuvant chemotherapy or radiotherapy can improve survival rate in appropriate cases, but the treatment of advanced or metastatic gastric cancer has made little progress, and the median overall survival (OS) is still less than 1 year (Carcas L P. Gastric cancer review [J]. Journal of carcinogenesis, 2014, 13.). In recent years, some targeted drugs for advanced gastric cancer have been developed and approved for clinical use, including trastuzumab (first-line for HER2-positive patients), ramucirumab (second-line for anti-angiogenesis), and nivolumab or pembrolizumab (third-line for anti-PD-1). However, the overall prognosis of gastric cancer patients is still poor. Therefore, it is of great clinical significance to develop effective biomarkers for gastric cancer prognosis prediction and as targets for targeted therapy.

[0003] Copper ions are essential metal elements for organisms (bacteria, animals and humans), which can act as cofactors for essential enzymes and play an important role in life activities. Under normal physiological conditions, copper ions maintain a low concentration and dynamic balance in the body, and abnormal accumulation of copper ions can cause copper toxicity and induce cell death. Studies have shown that human genetic mutations can cause copper homeostasis imbalance and induce various diseases, and copper homeostasis imbalance can lead to hereditary copper metabolism disorders, which also play an important role in tumor occurrence and proliferation. In March 2022, Peter Tsvetkov et al. proposed a new type of programmed cell death, copper death. The authors found that TCA-related metabolites in the mitochondrial respiratory process can participate in the regulation of copper death, and through whole genome CRISPR-Cas9 functional deletion screening, verification and cancer dependency map analysis, it was found that copper ions can cause abnormal oligomerization of thioacylated proteins in TCA and reduce the content of Fe-S cluster proteins, and then induce protein toxicity stress response to cause copper death. The concept of copper death deepens people's understanding of the regulation of copper metabolism in tumors, but the role of copper death in gastric cancer is not clear, so exploring the application of copper death core genes in gastric cancer prognosis provides a new means and direction for the treatment and prognosis of gastric cancer. SUMMARY

[0004] In order to make up for the deficiency of the prior art, the application provides a biomarker for predicting the prognosis of gastric cancer by studying the application of copper death related genes in gastric cancer, and a risk model is constructed by the biomarker for predicting the prognosis of gastric cancer.

[0005] In order to achieve the above-mentioned purpose, the application adopts the following technical scheme:

[0006] The first aspect of the application provides a method for constructing a cancer prognosis prediction model, which comprises:

[0007] Obtaining a key gene set regulated by copper death;

[0008] Obtaining a gene expression dataset and a survival label of a cancer patient;

[0009] Based on the gene expression of the key genes regulated by copper death in the gene expression dataset of the cancer patient, performing cluster analysis on the gene expression dataset of the cancer patient to obtain a first copper death regulation mode dataset, a second copper death regulation mode dataset and a third copper death regulation mode dataset;

[0010] Screening significant difference genes of the first copper death regulation mode dataset, the second copper death regulation mode dataset and the third copper death regulation mode dataset to obtain differentially expressed genes related to copper death subtypes;

[0011] Based on the survival label, performing regression analysis on the obtained differentially expressed genes related to copper death subtypes to obtain copper death subtype differentially expressed core genes significantly related to prognosis;

[0012] Based on the obtained copper death subtype differentially expressed core genes significantly related to prognosis, constructing a cancer prognosis prediction model CuPS.

[0013] Further, the cluster analysis is selected from unsupervised cluster analysis.

[0014] Further, the unsupervised cluster analysis uses NMF algorithm.

[0015] Further, the method for obtaining differentially expressed genes related to copper death subtypes further comprises:

[0016] Using the limma package to screen Cu1 dataset, Cu2 dataset and Cu3 dataset of the first copper death regulation mode dataset, the second copper death regulation mode dataset and the third copper death regulation mode dataset, screen significant difference genes of the Cu1 dataset, the Cu2 dataset and the Cu3 dataset, and obtain differentially expressed genes related to copper death subtypes.

[0017] Further, the specific method for obtaining the copper death subtype differential expression core gene significantly related to prognosis comprises:

[0018] Based on the survival label, one-factor Cox regression analysis is performed on the obtained copper death subtype differential expression gene to obtain a copper death subtype differential gene significantly related to prognosis;

[0019] Lasso Cox regression analysis is performed on the copper death subtype differential gene significantly related to prognosis to obtain a copper death subtype differential expression gene significantly related to prognosis;

[0020] One-factor Cox regression analysis is performed on the obtained copper death subtype differential expression gene significantly related to prognosis to obtain a copper death subtype differential expression core gene significantly related to prognosis.

[0021] Further, the method for obtaining the copper death subtype differential expression core gene significantly related to prognosis further comprises: performing further multi-factor Cox analysis on the copper death subtype differential expression gene significantly related to prognosis to obtain the copper death subtype differential expression core gene significantly related to prognosis.

[0022] Further, the calculation formula of the cancer prognosis prediction model CuPS is: CuPS= ,

[0023] Wherein, CuPS is a cancer prognosis risk score, n is the number of genes used for prognosis prediction, 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's prognosis is poor; when the risk score is lower, the patient's prognosis is good.

[0024] Further, the differential expression core gene comprises AHCYL2, ANKRD6 and / or PDGFRB.

[0025] Further, the calculation formula of the cancer prognosis prediction model CuPS is: CuPS=-0.205*exp(AHCYL2)+0.231*exp(ANKRD6)+0.237*exp(PDGFRB), wherein CuPS is a cancer prognosis risk score, and exp is the expression level of the gene; when the risk score is higher, the patient's prognosis is poor; when the risk score is lower, the patient's prognosis is good.

[0026] Further, the cancer comprises gastric cancer, renal cancer, pancreatic cancer, liver cancer, breast cancer, lung cancer.

[0027] Further, the cancer is selected from gastric cancer.

[0028] The second aspect of the present application provides a method for constructing a cancer prognosis prediction model, which comprises:​

[0029] obtaining a key gene set of copper death regulation;

[0030] obtaining a gene expression dataset and a survival label of a cancer patient;

[0031] performing cluster analysis on the gene expression dataset of the cancer patient based on the gene expression of the key gene set of copper death regulation in the gene expression dataset, to obtain a first copper death regulation mode dataset, a second copper death regulation mode dataset, and a third copper death regulation mode dataset;

[0032] screening significant differential genes in the first copper death regulation mode dataset, the second copper death regulation mode dataset, and the third copper death regulation mode dataset to obtain differential expression genes related to copper death subtypes;

[0033] performing single-factor Cox regression analysis on the obtained differential expression genes related to copper death subtypes based on the survival label, to obtain copper death subtype differential genes significantly related to prognosis;

[0034] constructing a cancer prognosis prediction model Cg-score based on the copper death subtype differential genes significantly related to prognosis screened.

[0035] Further, the method for obtaining differential expression genes related to copper death subtypes further comprises:

[0036] screening Cu1 dataset, Cu2 dataset, and Cu3 dataset of copper death subtypes in the first copper death regulation mode dataset, the second copper death regulation mode dataset, and the third copper death regulation mode dataset using the limma package, screening significant differential genes in the Cu1 dataset, the Cu2 dataset, and the Cu3 dataset, and obtaining differential expression genes related to copper death subtypes.

[0037] Further, the method for constructing a cancer prognosis prediction model Cg-score further comprises: obtaining the constructed cancer prognosis prediction model Cg-score through cluster analysis based on the copper death subtype differential genes significantly related to prognosis screened.

[0038] Further, the method for constructing a cancer prognosis prediction model Cg-score further comprises: obtaining a cluster effect scatter plot between copper death subtypes through cluster analysis, and obtaining the constructed cancer prognosis prediction model Cg-score through a PCA algorithm.

[0039] Further, the calculation formula of the constructed cancer prognosis prediction model Cg-score is: Cg-score = ∑ (PC1 i + PC2 i ),

[0040] wherein, Cg-score is a cancer prognosis risk score, i represents a gene; when the risk score is higher, the patient has a poor prognosis; when the risk score is lower, the patient has a good prognosis.

[0041] Further, the cluster analysis is selected from unsupervised cluster analysis.

[0042] Further, the unsupervised cluster analysis uses an NMF algorithm.

[0043] Further, the copper death subtype differential gene includes: AHCYL2, ANKRD6, PDGFRB, ANKRD22, RAB3IL1, C7, PDZRN4, ITGBL1, LRRC32, COL6A3, CDH11, FSTL1, CH25H, TCF4, PRICKLE1, PLOD2, RHOBTB3 and / or PROS1.

[0044] Further, the cancer includes gastric cancer, kidney cancer, pancreatic cancer, liver cancer, breast cancer, lung cancer.

[0045] Further, the cancer is selected from gastric cancer.

[0046] A third aspect of the present application provides a method for predicting the prognosis of cancer, the method comprising:

[0047] Obtaining gene expression data in a sample to be tested;

[0048] Inputting the gene expression data into a cancer prognosis prediction model to obtain a cancer prognosis prediction result of the sample to be tested.

[0049] Further, the cancer prognosis prediction model obtains the cancer prognosis prediction result of the sample to be tested through the gene expression data of AHCYL2, ANKRD6 and / or PDGFRB, and the specific calculation formula is: CuPS=-0.205*exp(AHCYL2)+0.231*exp(ANKRD6)+0.237*exp(PDGFRB), wherein, CuPS is a cancer prognosis risk score, exp is the expression level of a gene, when the risk score is higher, the patient has a poor prognosis; when the risk score is lower, the patient has a good prognosis.

[0050] Further, the cancer prognosis prediction model obtains the cancer prognosis prediction result of the sample to be tested by gene expression data of AHCYL2, ANKRD6, PDGFRB, ANKRD22, RAB3IL1, C7, PDZRN4, ITGBL1, LRRC32, COL6A3, CDH11, FSTL1, CH25H, TCF4, PRICKLE1, PLOD2, RHOBTB3 and / or PROS1, and the specific calculation formula is: Cg-score=∑(PC1 i +PC2 i ), wherein Cg-score is a cancer prognosis risk score, i represents a gene; when the risk score is high, the prognosis of the patient is poor; when the risk score is low, the prognosis of the patient is good.

[0051] The fourth aspect of the present application provides a device for predicting the prognosis of cancer, the device comprising:

[0052] a memory for storing program instructions;

[0053] a processor for calling program instructions, when the program instructions are executed, performing the following method steps for predicting the prognosis of cancer:

[0054] obtaining gene expression data in a sample to be tested;

[0055] inputting the gene expression data into a cancer prognosis prediction model to obtain a cancer prognosis prediction result of the sample to be tested;

[0056] The cancer prognosis prediction model is constructed by the construction method of the cancer prognosis prediction model according to the first aspect and the second aspect of the present application.

[0057] The fifth aspect of the present application provides a system for predicting the prognosis of cancer, the system comprising:

[0058] an acquisition unit for acquiring gene expression data in a sample to be tested;

[0059] a processing unit for inputting the gene expression data into a cancer prognosis prediction model to obtain a cancer prognosis prediction result of the sample to be tested;

[0060] The cancer prognosis prediction model is constructed by the construction method of the cancer prognosis prediction model according to the first aspect and the second aspect of the present application.

[0061] The sixth aspect of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the construction method of the cancer prognosis prediction model according to the first aspect and the second aspect of the present application or the method for predicting the prognosis of cancer according to the third aspect of the present application.

[0062] Advantages and beneficial effects of the present application:

[0063] The present application screens characteristic genes related to the prognosis of gastric cancer through the expression of copper death key genes, and constructs a cancer prognosis prediction model, which can effectively predict the prognosis of gastric cancer patients, and then realize early intervention and early treatment. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 is a construction implementation flowchart of the cancer prognosis prediction model CuPS;

[0065] Figure 2 is a construction implementation flowchart of the cancer prognosis prediction model Cg-score;

[0066] Figure 3 is a copper death subtype differential gene wayne diagram;

[0067] Figure 4 is a copper death subtype gene wayne diagram significantly related to prognosis in three gastric cancer data sets of GEO-meta, GSE84437 and TCGA-STAD;

[0068] Figure 5 is a Lasso Cox regression analysis diagram, wherein 5A is a confidence interval diagram under λ, and 5B is a change trajectory diagram of each independent variable;

[0069] Figure 6 is a single factor Cox diagram of copper death subtype differential expression genes significantly related to prognosis screened by Lasso regression;

[0070] Figure 7 is a gastric cancer patient survival time and survival state distribution diagram with increasing CuPS, wherein 7A is a gastric cancer patient survival time and survival state distribution diagram with increasing CuPS in the GEO-meta training set, 7B is a gastric cancer patient survival time and survival state distribution diagram with increasing CuPS in the GEO-meta test set, 7C is a gastric cancer patient survival time and survival state distribution diagram with increasing CuPS in the GSE84437 independent verification set, and 7D is a gastric cancer patient survival time and survival state distribution diagram with increasing CuPS in the TCGA-STAD independent verification set;

[0071] Figure 8 is a CuPS prediction gastric cancer patient prognosis survival curve diagram, wherein 8A is a CuPS prediction gastric cancer prognosis survival curve diagram in the GEO-meta training set, 8B is a CuPS prediction gastric cancer prognosis survival curve diagram in the GEO-meta test set, 8C is a CuPS prediction gastric cancer prognosis survival curve diagram in the GSE84437 independent verification set, and 8D is a CuPS prediction gastric cancer prognosis survival curve diagram in the TCGA-STAD independent verification set.

[0072] Figure 9 is a Cg subtype PCA clustering effect scatter plot;

[0073] Figure 10 is a Cg-score prognostic risk model to predict the prognosis of gastric cancer patients survival curve chart;

[0074] Figure 11 is a CuPS combined Cg-score prognostic risk model to predict the prognosis of gastric cancer patients survival curve chart. DETAILED DESCRIPTION

[0075] The following provides definitions for some of the terms used in this specification. Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0076] In some of the processes described in the specification and claims of the application and in the above-described drawings, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without these specific details. In other instances, well-known methods have not been described in detail in order to avoid unnecessarily obscuring the present application. Unless otherwise specified, the use of the ordinal adjectives such as "first", "second", "third", etc., to describe a common but distinct attribute, without the initial article, is used only to distinguish between two instances with the same attribute, not to imply that the application has any priority or order between its recited use instances. Reference to prior art documents and maintaining the same reference numbers in the claims is for the purpose of enabling a clear and consistent understanding of the specification and claims, and should not be construed as an admission that the prior art is directly related or prior art base to the claimed application.

[0077] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0078] Figure 1 is a flowchart of the construction of the cancer prognosis prediction model CuPS, specifically, the method comprises the following steps:

[0079] 101: Obtain a set of key genes regulated by copper death;

[0080] 102: Obtain a set of gene expression data and survival labels of cancer patients;

[0081] 103: performing cluster analysis on the gene expression dataset of the cancer patients based on the gene expression of the key genes of copper death regulation in the gene expression dataset of the cancer patients, to obtain a first copper death regulation mode dataset, a second copper death regulation mode dataset, and a third copper death regulation mode dataset;

[0082] 104: screening significant differential genes in the first copper death regulation mode dataset, the second copper death regulation mode dataset, and the third copper death regulation mode dataset, to obtain differential expression genes related to the copper death subtype;

[0083] 105: performing regression analysis on the differential expression genes related to the copper death subtype obtained based on a survival label, to obtain copper death subtype differential expression core genes significantly related to prognosis;

[0084] 106: constructing a cancer prognosis prediction model CuPS based on the copper death subtype differential expression core genes significantly related to prognosis obtained.

[0085] In an embodiment, the set of key genes of copper death regulation can be obtained from literature or databases. Preferably, the set of key genes of copper death regulation is obtained from literature. Specifically, the set of key genes of copper death regulation includes 7 positive regulation genes, namely FDX1, LIPT1, LIAS, DLD, DLAT, PDHA1, and PDHB; and 3 negative regulation genes, namely MTF1, GLS, and CDKN2A.

[0086] In an embodiment, the gene expression dataset and survival label of the cancer patients can be sequencing data from sequencing or sequencing data publicly available in databases. For example, the data can be from TCGA, ICGC, COSMIC, cBioPortal, CGWB, GEO, UALCAN, MethHC, and / or MethyCancer databases. Preferably, the survival and gene expression data of the cancer patients are obtained from TCGA and GEO databases.

[0087] In an embodiment, the cluster analysis on the gene expression dataset of the cancer patients is performed based on the gene expression of the key genes of copper death regulation in the gene expression dataset of the cancer patients, to obtain a first copper death regulation mode dataset, a second copper death regulation mode dataset, and a third copper death regulation mode dataset.

[0088] The term “cluster” refers to classifying data into corresponding categories according to the similarity between the data. The same categories have high similarity, and the difference between different categories is maximized.

[0089] The term "clustering analysis" refers to the process of grouping a collection of transcriptome RNA sequencing data in samples into multiple categories consisting of similar gene expression profiles. Conventional clustering analysis methods such as hierarchical clustering, K-means clustering, second-order clustering, etc. can be used in the present application, and the preferred clustering analysis method in the present application is the NMF unsupervised clustering analysis method. The term "NMF" refers to non-negative matrix factorization.

[0090] In one embodiment, the significant differential genes of the first copper death regulation mode data set, the second copper death regulation mode data set, and the third copper death regulation mode data set are screened to obtain differential expression genes related to the copper death subtype.

[0091] In one embodiment, the copper death subtype Cu1 data set, Cu2 data set, and Cu3 data set of the first copper death regulation mode data set, the second copper death regulation mode data set, and the third copper death regulation mode data set are screened using the limma package to screen the significant differential genes of the Cu1 data set, Cu2 data set, and Cu3 data set, and obtain differential expression genes related to the copper death subtype.

[0092] In one embodiment, the differential expression genes are determined by |logFC| (|log2 (Fold Change)|) value and P (P-value) index, for example, genes with |logFC|>0.1 and P<0.01 are determined as differential expression genes.

[0093] In one embodiment, based on the survival label, regression analysis is performed on the obtained differential expression genes related to the copper death subtype to obtain copper death subtype differential expression core genes significantly related to prognosis.

[0094] The specific method includes: based on the survival label, single-factor Cox regression analysis is performed on the obtained differential expression genes related to the copper death subtype to obtain copper death subtype differential genes significantly related to prognosis; Lasso Cox regression analysis is performed on the copper death subtype differential genes significantly related to prognosis to obtain copper death subtype differential expression genes significantly related to prognosis; single-factor Cox regression analysis is performed on the obtained copper death subtype differential expression genes significantly related to prognosis, and further multi-factor Cox analysis is performed to obtain copper death subtype differential expression core genes significantly related to prognosis.

[0095] In one embodiment, the calculation formula of the cancer prognosis prediction model CuPS is: CuPS = ∑ (Expi / n) * (βi / n) = ∑ (Expi * βi / n2) ,

[0096] wherein CuPS is the cancer prognosis risk score, n is the number of genes used for prognosis prediction, Expi is the expression value of each gene, and βi is the regression coefficient of each gene. Ci is the regression coefficient of each gene; when the risk score is higher, the patient has poor prognosis; when the risk score is lower, the patient has good prognosis.

[0097] In one embodiment, the copper death subtype differential expression core genes significantly associated with prognosis are AHCYL2, ANKRD6 and / or PDGFRB, respectively.

[0098] The calculation formula is: CuPS=-0.205*exp(AHCYL2)+0.231*exp(ANKRD6)+0.237*exp(PDGFRB), wherein CuPS is the cancer prognosis risk score, exp is the expression level of the gene, when the risk score is higher, the patient has poor prognosis; when the risk score is lower, the patient has good prognosis.

[0099] AHCYL2 gene: adenosylhomocysteinase like 2, the typical Homo sapiens mRNA and protein sequence can be found in the NCBI database with gene ID: 23382.

[0100] ANKRD6 gene: ankyrin repeat domain 6, the typical Homo sapiens mRNA and protein sequence can be found in the NCBI database with gene ID: 22881.

[0101] PDGFRB gene: platelet derived growth factor receptor beta, the typical Homo sapiens mRNA and protein sequence can be found in the NCBI database with gene ID: 5159.

[0102] Figure 2 is a schematic diagram of the implementation process of the construction of the cancer prognosis prediction model Cg-score, specifically, the method comprises the following steps:

[0103] 201: Obtain the key gene set regulated by copper death;

[0104] 202: Obtain the gene expression dataset and survival label of the cancer patients;

[0105] 203: Based on the gene expression of the key genes regulated by copper death in the gene expression dataset of the cancer patients, perform cluster analysis on the gene expression dataset of the cancer patients to obtain a first copper death regulation mode dataset, a second copper death regulation mode dataset and a third copper death regulation mode dataset;

[0106] 204: Screen the significant differential genes of the first copper death regulation mode dataset, the second copper death regulation mode dataset, and the third copper death regulation mode dataset to obtain differential expression genes related to the copper death subtype;

[0107] 205: Perform single-factor Cox regression analysis on the obtained differential expression genes related to the copper death subtype based on the survival label to obtain copper death subtype differential genes significantly related to prognosis;

[0108] 206: Based on the copper death subtype differential genes significantly related to prognosis obtained by screening, a cancer prognosis prediction model Cg-score is constructed.

[0109] In one embodiment, based on the copper death subtype differential genes significantly related to prognosis obtained by screening, a cancer prognosis prediction model Cg-score is constructed.

[0110] In one embodiment, the copper death subtype differential genes significantly related to prognosis obtained by screening are subjected to NMF unsupervised clustering analysis to obtain a PCA clustering effect scatter plot of the copper death subtype, and a prognosis risk model Cg-score is calculated through the PCA algorithm.

[0111] The calculation formula of Cg-score is: Cg-score =∑ (PC1 i +PC2 i ),

[0112] Wherein, Cg-score is a cancer prognosis risk score, i represents a gene; when the risk score is high, the prognosis of the patient is poor; when the risk score is low, the prognosis of the patient is good.

[0113] The term "PCA" refers to a mathematical method of transforming many related variables into many uncorrelated (independent) variables called principal components. The first principal component accounts for as much variation in the data as possible, and the subsequent components account for as much of the remaining variation as possible. The "PCA" used in the present application also includes variants of principal component analysis, such as core PCA, etc.

[0114] In one embodiment, the copper death subtype differential genes significantly related to cancer prognosis obtained by screening include: AHCYL2, ANKRD6, PDGFRB, ANKRD22, RAB3IL1, C7, PDZRN4, ITGBL1, LRRC32, COL6A3, CDH11, FSTL1, CH25H, TCF4, PRICKLE1, PLOD2, RHOBTB3, and / or PROS1.

[0115] ANKRD22 gene: ankyrin repeat domain 22, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 118932.

[0116] RAB3IL1 gene: RAB3A interacting protein like 1, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 5866.

[0117] C7 gene: complement C7, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 730.

[0118] PDZRN4 gene: PDZ domain containing ring finger 4, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 29951.

[0119] ITGBL1 gene: integrin subunit beta like 1, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 9358.

[0120] LRRC32 gene: leucine rich repeat containing 32, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 2615.

[0121] COL6A3 gene: collagen type VI alpha 3 chain, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 1293.

[0122] CDH11 gene: cadherin 11, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 1009.

[0123] FSTL1 gene: follistatin like 1, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 11167.

[0124] CH25H gene: cholesterol 25-hydroxylase, typical Homo sapiens mRNA and protein sequences can be found in NCBI database with gene ID: 9023.

[0125] TCF4 gene: transcription factor 4, typical Homo sapiens mRNA and protein sequences can be found in the NCBI database under gene ID: 6925.

[0126] PRICKLE1 gene: prickle planar cell polarity protein 1, typical Homo sapiens mRNA and protein sequences can be found in the NCBI database under gene ID: 144165.

[0127] PLOD2 gene: procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2, typical Homo sapiens mRNA and protein sequences can be found in the NCBI database under gene ID: 5352.

[0128] RHOBTB3 gene: Rho related BTB domain containing 3, typical Homo sapiens mRNA and protein sequences can be found in the NCBI database under gene ID: 22836.

[0129] PROS1 gene: protein S, typical Homo sapiens mRNA and protein sequences can be found in the NCBI database under gene ID: 5627.

[0130] The term "cancer" includes, but is not limited to, biliary tract cancer; bladder cancer; brain cancer, including glioblastoma and medulloblastoma; breast cancer; cervical cancer; choriocarcinoma; colon cancer; endometrial cancer; esophageal cancer; gastric cancer; hematological tumors, including acute lymphocytic and myeloid leukemia; multiple myeloma; AIDS-related leukemias and adult T-cell leukemia lymphoma; intra-epithelial neoplasias, including Bowen's disease and Paget's disease; liver cancer; lung cancer; lymphomas, including Hodgkin's disease and lymphocytic lymphoma; neuroblastoma; oral cancer, including squamous cell carcinoma; ovarian cancer, including ovarian cancers arising from epithelial cell, stromal cell, germ cell, and mesenchymal cell tumors; pancreatic cancer; prostate cancer; rectal cancer; sarcomas, including leiomyosarcoma, rhabdomyosarcoma, liposarcoma, fibrosarcoma, and osteosarcoma; skin cancer, including melanoma, Kaposi's sarcoma, basal cell carcinoma, and squamous cell carcinoma; testicular cancer, including germinal tumors such as seminoma, non-seminoma (teratomas, choriocarcinoma), stromal tumors, and germ cell tumors; thyroid cancer, including thyroid adenocarcinoma and medullary carcinoma; and kidney cancer, including adenocarcinoma and nephroblastoma, breast cancer, prostate cancer, lung cancer, ovarian cancer, colorectal cancer, and brain cancer. A preferred cancer of the present application is gastric cancer.

[0131] The term "prognosis" refers to the likely course or clinical outcome of a disease, and the expressions "predict", "making a prognosis", "determining a prognosis" and the like refer to the prediction of the future progression of gastric cancer.

[0132] As used herein, the terms "good prognosis", "good prognostic" and "positive prognosis" refer to a likely statistically significant prolonged survival, such as prolonged overall survival, prolonged disease-free survival, prolonged relapse-free survival or prolonged progression-free survival, as compared to the median outcome of the disease or to the survival of subjects with a poor prognosis.

[0133] As used herein, the terms "poor prognosis" or "poor prognostic" refer to a likely statistically significant reduced survival, such as reduced overall survival, disease-free survival, relapse-free survival or progression-free survival, as compared to subjects with a good prognosis.

[0134] The present application provides a system for predicting the prognosis of cancer, the system comprising:

[0135] an acquisition unit for acquiring gene expression data in a sample to be tested;

[0136] a processing unit for inputting the expression data into a cancer prognosis prediction model to obtain a cancer prognosis prediction result of the sample to be tested;

[0137] The cancer prognosis prediction model is constructed by the method for constructing a cancer prognosis prediction model.

[0138] In one embodiment, the system includes a processor, which can be a single-core or multi-core processor or more than one processor for parallel processing. The system also includes memory (e.g., random access memory, read only memory, flash memory), electronic storage unit (e.g., hard disk), communication interface (e.g., network adapter) for communicating with one or more other systems, and peripheral devices such as cache, other memory, data storage, and / or electronic display adapter. The memory, electronic storage unit, communication interface, and peripheral devices are in communication with the processor through a communication bus (solid lines), such as a motherboard. The storage unit can be a data storage unit (or data repository) for storing data. The system can be operatively coupled to a computer network by means of the communication interface. The network can be an internet, intranet and / or extranet, or a subset of the internet, intranet, and / or extranet that is in communication with the internet. In some cases, the network is a communication and / or data network. The network can include one or more computer servers that can support distributed computing, such as cloud computing. In some cases, the network can implement a peer-to-peer network by means of the system, which can enable devices coupled to the system to function as a client or a server.

[0139] In one embodiment, the processor can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions can be stored in a memory location, such as the memory. The instructions can be directed to the processor, which can subsequently program or otherwise configure the processor to implement a method of the present application. Examples of activities that the processor can perform include read, decode, execute, and write back.

[0140] In one embodiment, the processor can be part of a circuit, such as an integrated circuit, which can contain one or more other components of the system, in some cases, the circuit is an application specific integrated circuit.

[0141] In one embodiment, the electronic storage unit can store files, such as drivers, libraries and saved programs. The electronic storage unit can store user data, for example, user preferences and user programs. In some cases, the system can include one or more additional data storage units that are external to the computer system, such as located on a remote server that is in communication with the system through an intranet or the internet.

[0142] In one embodiment, the system can communicate with one or more remote computer systems over a network. For example, the system can communicate with a remote computer system of a user (e.g., a physician). Examples of remote computer systems include a personal computer, a slate or tablet PC, a telephone, a smart phone, or a personal digital assistant. The user can access the system via the network.

[0143] In one embodiment, the methods described herein can be implemented by way of machine (e.g., computer processor), readable code. The machine-readable code can be stored in a non-transitory storage medium on a system, such as a memory or electronic storage unit. The machine-readable code or machine-readable instructions can be software. During use, the code can be executed by the processor. In some cases, the code can be retrieved from the storage unit and stored in memory for immediate access by the processor. In some cases, the electronic storage unit can be precluded, and machine-executable instructions be stored in memory.

[0144] The code can be pre-compiled and configured for use with a machine having a processor adapted to execute the code, or can be compiled during runtime. The code can be provided in a programming language that can be selected to enable the code to be executed in a pre-compiled or compiled fashion.

[0145] The application will be further described with reference to the drawings and the following examples. The following examples are intended to illustrate the application and are not intended to limit the scope of the application. Unless otherwise indicated, the experimental procedures in the examples were performed according to conventional conditions or according to the manufacturer's instructions.

[0146] Example 1. Construction of a prognostic risk model, CuPS, based on copper death signature genes and testing of the prognostic effect

[0147] 1. Ten key genes regulated by copper death were obtained from literature. Among them, 7 genes were positively regulated by copper death, namely FDX1, LIPT1, LIAS, DLD, DLAT, PDHA1 and PDHB; 3 genes were negatively regulated by copper death, namely MTF1, GLS and CDKN2A.

[0148] 2、Gastric cancer patient data containing survival and gene expression were obtained through TCGA dataset and GEO dataset. A total of 1401 gastric cancer patients were included in this study, including 350 cases from TCGA-STAD dataset, 433 cases from GSE84437 dataset, 56 cases from GSE34942 dataset, 70 cases from GSE57303 dataset, 192 cases from GSE15459 dataset, and 300 cases from GSE62254 dataset. At the same time, the RNA transcriptome data (FPKM format) of gene expression of gastric cancer were obtained through TCGA dataset, and the RNA transcriptome data was converted to transcriptome format of million / kilobase (TPM). The SVA package was used to remove the batch effect between each dataset.

[0149] The four GEO datasets (GSE34942, GSE57303, GSE15459 and GSE62254) were combined into a GEO-meta dataset, and were divided into training set and test set at a ratio of 1:1, and the GSE84437 dataset and TCGA-STAD dataset were used as independent validation set.

[0150] 3、Based on the expression of the above 10 key genes of copper death regulation, unsupervised clustering analysis was performed using "NMF", and 3 copper death regulation modes were identified.

[0151] 4、Using the "limma" package, 3 copper death subtypes (Cu1, Cu2 and Cu3) under 3 copper death regulation modes were screened, and significant difference genes (|logFC|>0.1, corrected P<0.01) among the three were taken, a total of 470 copper death subtype related differential expression genes (DEGs) were obtained (Table 1). Figure 3 Further single factor Cox analysis was performed in the GEO-meta, GSE84337 and TCGA-STAD three datasets to screen the prognosis related genes (P<0.05), and 18 copper death subtype differential genes significantly related to prognosis were obtained (Table 2). Figure 4

[0152] 5、In the GEO-meta training set, Lasso Cox regression analysis was performed on the 18 copper death subtype differential genes screened for significant correlation with prognosis, and the confidence interval graph and its change trajectory of 18 genes λ were shown in Figure 5 After Lasso Cox regression analysis, 8 copper death subtype differential expression genes significantly related to prognosis were screened, and single factor Cox analysis was performed on the 8 copper death subtype differential expression genes significantly related to prognosis (Table 3). Figure 6 ​The three copper death subtype differential expression core genes significantly related to prognosis are AHCYL2, ANKRD6, and PDGFRB.

[0153] 6. Based on the three copper death subtype differential expression core genes significantly related to prognosis screened above, a cancer prognosis risk model is constructed. The specific calculation formula of the model is CuPS = -0.205*exp(AHCYL2) + 0.231*exp(ANKRD6) + 0.237*exp(PDGFRB), and exp(AHCYL2 / ANKRD6 / PDGFRB) is the expression level of the AHCYL2 / ANKRD6 / PDGFRB gene. Figure 1 ).

[0154] 7. The optimal cut-off value of CuPS is 1.1 by using the "surv_cutpoint" function in the "survival" package. According to the optimal cut-off value, the patients in each data set are divided into a high-risk group and a low-risk group, and the survival time and survival status distribution of gastric cancer patients with increasing CuPS are explored in the training set, test set, and independent validation set. Figure 7 ).

[0155] 8. By Kaplan-Meier survival analysis, the difference in survival time between the two groups is compared, and it is found that the overall survival rate of the high-risk group is significantly lower than that of the low-risk group. The results of the training set, test set, and independent validation set are consistent, and the overall survival rate of the high-risk group is significantly lower than that of the low-risk group. Figure 8 ); that is, when the CuPS risk score is high, the prognosis of the patient is poor; when the CuPS risk score is low, the prognosis of the patient is good.

[0156] In summary, the CuPS model constructed based on the application can predict the survival time / prognosis of gastric cancer patients.

[0157] Example 2: Construction of a prognosis risk model Cg-score based on copper death characteristic genes and testing of the prognosis effect

[0158] 1. The same as the execution steps of 1-4 in Example 1, 18 copper death subtype differential genes significantly related to prognosis are screened. Based on the 18 genes, NMF unsupervised clustering analysis is performed to obtain PCA clustering effect scatter plots of Cg1, Cg2, and Cg3 subtypes Figure 9 ), and the prognosis risk model Cg-score is calculated by the PCA algorithm, and the specific formula is Cg-score = ∑(PC1 i + PC2 i ).Figure 2 ).

[0159] 2. The optimal cutoff value of Cg-score was determined to be 2.00 by the "surv_cutpoint" function in the "survival" package, and the patients in the TCGA and GEO total dataset were divided into a high-risk group and a low-risk group according to the Cg-score.

[0160] 3. The Kaplan-Meier survival analysis was used to compare the differences in survival time between the two groups, and it was found that the overall survival rate of the high-risk group was significantly lower than that of the low-risk group. The results of the training set, test set and independent validation set were consistent, and the overall survival rate of the high-risk group was significantly lower than that of the low-risk group (P<0.05), that is, when the Cg-score risk score is higher, the prognosis of the patient is poor; when the Cg-score risk score is lower, the prognosis of the patient is good. Figure 10

[0161] In summary, the Cg-score model constructed based on the application can predict the survival time / prognosis of gastric cancer patients.

[0162] Example 3: Construction of prognosis risk model CuPS and Cg-score based on copper death characteristic genes, and parallel test of prognosis effect

[0163] 1. The prognosis risk model CuPS and Cg-score based on copper death characteristic genes was constructed in the same way as in Examples 1 and 2.

[0164] 2. According to the optimal cutoff values of CuPS and Cg-score, the gastric cancer patients were divided into H-Cg-score (high Cg-score score), L-Cg-score (low Cg-score score), H-CuPS (high CuPS score) and L-CuPS (low CuPS score) groups, and the Kaplan-Meier survival analysis was performed on the four groups.

[0165] 3. The Kaplan-Meier survival analysis was used to compare the differences in survival time between the two groups, and it was found that the survival rate of the L-Cg-score+L-CuPS group was significantly higher than that of the L-Cg-score+H-CuPS group, H-Cg-score+L-CuPS group and H-Cg-score+H-CuPS group; the survival rate of the H-Cg-score+H-CuPS group was the lowest (P<0.05), that is, when the CuPS and Cg-score risk scores are both high, the prognosis of the patient is poor and the survival time is short; when the CuPS and Cg-score risk scores are both low, the prognosis of the patient is good and the survival time is significantly prolonged. Figure 11

[0166] ​​In summary, the cancer prognosis risk model CuPS and Cg-score combined based on the application can better predict the survival / prognosis of gastric cancer.

[0167] The preferred embodiments of the application are described in detail above with reference to the drawings, but the application is not limited to the specific details in the above-described embodiments. Various simple modifications can be made to the technical solutions of the application within the technical concept of the application, and these simple modifications all belong to the protection scope of the application.

[0168] In addition, it should be noted that each specific technical feature described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, various possible combinations are not described again by the application.

[0169] In addition, various different embodiments of the application can be combined in any manner as long as they do not contradict the idea of the application, and they should also be considered as disclosed by the application.

Claims

1. A method of constructing a cancer prognosis prediction model, characterized by, The method comprises: obtaining a key gene set of copper death regulation; obtaining a gene expression data set and a survival label of a cancer patient; based on the gene expression of the key gene of copper death regulation in the gene expression data set of the cancer patient, performing cluster analysis on the gene expression data set of the cancer patient to obtain a first copper death regulation mode data set, a second copper death regulation mode data set and a third copper death regulation mode data set; screening significant difference genes of the first copper death regulation mode data set, the second copper death regulation mode data set and the third copper death regulation mode data set to obtain differentially expressed genes related to copper death subtypes; based on the survival label, performing regression analysis on the obtained differentially expressed genes related to copper death subtypes to obtain copper death subtype differentially expressed core genes significantly related to prognosis; based on the obtained copper death subtype differentially expressed core genes significantly related to prognosis, constructing a cancer prognosis prediction model CuPS; The cancer is selected from gastric cancer.

2. The method of claim 1, wherein, The cluster analysis is selected from unsupervised cluster analysis.

3. The method of claim 2, wherein, The unsupervised cluster analysis uses NMF algorithm.

4. The method of claim 1, wherein the method is performed on a plurality of samples from a plurality of patients. The method for obtaining differentially expressed genes related to copper death subtypes further comprises: screening Cu1 data set, Cu2 data set and Cu3 data set of copper death subtypes of the first copper death regulation mode data set, the second copper death regulation mode data set and the third copper death regulation mode data set by using limma package, screening significant difference genes of Cu1 data set, Cu2 data set and Cu3 data set to obtain differentially expressed genes related to copper death subtypes.

5. The method of claim 1, wherein, The specific method for obtaining copper death subtype differentially expressed core genes significantly related to prognosis comprises: based on the survival label, performing single factor Cox regression analysis on the obtained differentially expressed genes related to copper death subtypes to obtain copper death subtype differentially expressed genes significantly related to prognosis; performing Lasso Cox regression analysis on the copper death subtype differentially expressed genes significantly related to prognosis to obtain copper death subtype differentially expressed genes significantly related to prognosis; performing single factor Cox regression analysis on the obtained copper death subtype differentially expressed genes significantly related to prognosis to obtain copper death subtype differentially expressed core genes significantly related to prognosis.

6. The method of claim 5, wherein, The method for obtaining copper death subtype differentially expressed core genes significantly related to prognosis further comprises: performing further multi-factor Cox analysis on the copper death subtype differentially expressed genes significantly related to prognosis to obtain copper death subtype differentially expressed core genes significantly related to prognosis.

7. The method of claim 1, wherein the method is performed on a plurality of samples from a plurality of patients. The formula for calculating the cancer prognosis prediction model CuPS is: CuPS = , Wherein, CuPS is the cancer prognosis risk score, 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 poor prognosis; when the risk score is lower, the patient has good prognosis.

8. The method of claim 6, wherein, The differentially expressed core genes comprise AHCYL2, ANKRD6 and / or PDGFRB.

9. The method of claim 8, wherein, The calculation formula of the cancer prognosis prediction model CuPS is: CuPS=-0.205*exp(AHCYL2)+0.231*exp(ANKRD6)+0.237*exp(PDGFRB), wherein CuPS is a cancer prognosis risk score, exp is the expression level of the gene, when the risk score is higher, the prognosis of the patient is poor; when the risk score is lower, the prognosis of the patient is good.

10. A method of constructing a cancer prognosis prediction model, characterized by, The method comprises: obtaining a key gene set of copper death regulation; obtaining a gene expression dataset and a survival label of a cancer patient; performing cluster analysis on the gene expression dataset of the cancer patient based on the gene expression of key genes in copper death regulation in the gene expression dataset of the cancer patient, to obtain a first copper death regulation mode dataset, a second copper death regulation mode dataset, and a third copper death regulation mode dataset; screening significant differential genes in the first copper death regulation mode dataset, the second copper death regulation mode dataset, and the third copper death regulation mode dataset to obtain differential expression genes related to copper death subtypes; performing single factor Cox regression analysis on the obtained differential expression genes related to copper death subtypes based on the survival label to obtain copper death subtype differential genes significantly related to prognosis; constructing a cancer prognosis prediction model Cg-score based on the screened copper death subtype differential genes significantly related to prognosis. The cancer is selected from gastric cancer. 11.The method of claim 10, wherein, The method for obtaining differential expression genes related to copper death subtypes further comprises: screening copper death subtype Cu1 dataset, Cu2 dataset, and Cu3 dataset in the first copper death regulation mode dataset, the second copper death regulation mode dataset, and the third copper death regulation mode dataset using the limma package, screening significant differential genes in the Cu1 dataset, the Cu2 dataset, and the Cu3 dataset, and obtaining differential expression genes related to copper death subtypes.

12. The method of claim 10, wherein the method further comprises: determining the prognosis of the cancer based on the expression level of the at least one gene. The method for constructing a cancer prognosis prediction model Cg-score further comprises: obtaining the constructed cancer prognosis prediction model Cg-score through cluster analysis based on the screened copper death subtype differential genes significantly related to prognosis.

13. The method of claim 10, wherein the method further comprises: determining the prognosis of the cancer based on the expression level of the at least one gene. The method for constructing a cancer prognosis prediction model Cg-score further comprises: obtaining a cluster effect scatter plot between copper death subtypes through cluster analysis, and obtaining the constructed cancer prognosis prediction model Cg-score through a PCA algorithm.

14. The method of claim 10, wherein the method further comprises: determining the prognosis of the cancer based on the expression level of the at least one gene. The calculation formula of the constructed cancer prognosis prediction model Cg-score is: Cg-score =∑(PC1 i +PC2 i ), Wherein, Cg-score is a cancer prognosis risk score, i represents a gene; when the risk score is high, the prognosis of the patient is poor; when the risk score is low, the prognosis of the patient is good.

15. The method of claim 10, wherein the method further comprises: determining the prognosis of the cancer based on the expression level of the at least one gene. The cluster analysis is selected from unsupervised cluster analysis.

16. The method of claim 15, wherein the method further comprises: determining the prognosis of the cancer based on the expression level of the at least one gene. The unsupervised cluster analysis uses an NMF algorithm.

17. The method of claim 10, wherein the method further comprises: determining the prognosis of the cancer based on the constructed model. The copper death subtype differential genes include: AHCYL2, ANKRD6, PDGFRB, ANKRD22, RAB3IL1, C7, PDZRN4, ITGBL1, LRRC32, COL6A3, CDH11, FSTL1, CH25H, TCF4, PRICKLE1, PLOD2, RHOBTB3, and / or PROS1.

18. An apparatus for predicting prognosis of cancer, comprising: The device comprises: a memory for storing program instructions; a processor for calling program instructions, which, when executed, perform the following method steps for predicting cancer prognosis: obtaining gene expression data in a sample to be tested; inputting the gene expression data into a cancer prognosis prediction model to obtain a cancer prognosis prediction result of the sample to be tested; The cancer prognosis prediction model is constructed by the method for constructing a cancer prognosis prediction model according to any one of claims 1-17.

19. A system for predicting the prognosis of cancer, characterized by, The system comprises: An acquisition unit is configured to acquire gene expression data in a sample to be tested. A processing unit is configured to input the gene expression data into a cancer prognosis prediction model to obtain a cancer prognosis prediction result of the sample to be tested. The cancer prognosis prediction model is constructed by the method for constructing a cancer prognosis prediction model according to any one of claims 1-17.

20. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the method for constructing a cancer prognosis prediction model according to any one of claims 1-17.

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