CN113450873B - Marker for predicting gastric cancer prognosis and immunotherapy applicability and application thereof - Google Patents
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Abstract
The invention relates to a marker for predicting gastric cancer prognosis and immunotherapy applicability and application thereof. The marker is an mRNA marker which consists of APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX. The invention also discloses a kit for predicting gastric cancer prognosis and immunotherapy applicability and a method for predicting gastric cancer prognosis and immunotherapy applicability. The invention finds markers APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX related to gastric cancer progression through a bioinformatics technology, then constructs a risk model according to the 7 genes, further obtains a risk score, can predict the prognosis of gastric cancer and predict the applicability of immunotherapy through the risk score, and provides a reliable method for analyzing the prognosis and immunotherapy of gastric cancer patients.
Description
Technical Field
The invention relates to a marker for predicting gastric cancer prognosis and immunotherapy applicability and application thereof, belonging to the field of biomedicine.
Background
Gastric cancer is the fifth most common cancer worldwide and is also the cause of death associated with the third most cancers. Many patients are diagnosed at an advanced stage, and an additional 25% to 50% of patients develop metastases during the course of the disease. Despite the continuous improvement of treatment methods, the 5-year survival rate of metastatic gastric cancer is only 5% -20%. The immunotherapy has wide application prospect in the gastric cancer, and the blocking of the immune check point is a currently recognized method for treating the chemotherapy-refractory gastric cancer. Immunotherapy alone or in combination has a positive impact on the treatment of gastric cancer, but the response rate of patients during immunotherapy is not satisfactory due to the high heterogeneity of gastric cancer. Therefore, there is a need to find biomarkers to identify gastric cancer subpopulations that are most likely to respond to specific immunotherapy.
The Tumor Microenvironment (TME) is composed of cellular and acellular components, including peripheral blood vessels, immune cells, fibroblasts, tumor stem cells, and extracellular matrix (ECM). Studies have shown that tumor growth is dependent not only on the accumulation of abnormal genetic material in the original cancer cells, but also on the TME, which provides conditions for the survival, growth and migration of cancer cells. Immune cells in TME, especially Tumor Infiltrating Lymphocytes (TILs), have become prognostic and predictive factors for many solid tumors. Furthermore, immune cells in TME are also important factors affecting the immune therapeutic response. The immune cells in the TME play an important role in the development of tumors, and tumor-associated immune cells can antagonize or promote the progression of tumors, depending on the composition and proportion of the immune cells. Compared to traditional chemotherapy, immunotherapy mainly utilizes immune cells to specifically recognize and attack cancer cells. Therefore, by analyzing the composition and ratio of immune cells in the tissues of gastric cancer patients, it can be evaluated whether the patients can benefit from immunotherapy.
Currently, the American Joint Committee for Cancer (AJCC) staging remains the most basic prognostic tool for gastric cancer, with high staging indicating poor prognosis. However, due to the high heterogeneity of gastric carcinoma, patients in the same Tumor Node Metastasis (TNM) stage may have different prognosis. Similarly, differences in patient response to immunotherapy may also be related to their genetic and molecular backgrounds. Therefore, it is necessary to fully understand the specific characteristics of each patient and combine other important factors to perform individual treatment and prognosis prediction. Through bioinformatics analysis of large-scale genome or transcriptome data, molecular markers related to generation, development and prognosis of gastric cancer can be screened out, reliable treatment targets are provided for precise medicine, and the method has advantages in personalized treatment and prognosis prediction and broad prospects.
Disclosure of Invention
Description of the terms:
expression level: refers to the extent to which a particular mRNA sequence is transcribed from its genomic locus, i.e., the concentration of mRNA in one or more of the sera analyzed.
Aiming at the defects of the prior art, the invention provides a marker for predicting gastric cancer prognosis and immunotherapy applicability and application thereof.
The technical scheme of the invention is as follows:
a marker for predicting gastric cancer prognosis and suitability for immunotherapy, wherein the marker is an mRNA marker consisting of APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX.
The application of the marker for predicting gastric cancer prognosis and immunotherapy applicability adopts the following technical scheme: use of a marker as described above for predicting the prognosis of gastric cancer and for predicting the suitability for immunotherapy; the application includes, but is not limited to, any one or combination of a plurality of evaluation or prediction of prognosis risk, prediction of immunotherapy applicability, prediction of survival rate, treatment/medication scheme establishment, construction of a model for prediction of gastric cancer prognosis risk, construction of a model for immunotherapy applicability, construction of a model for prediction of gastric cancer survival rate, preparation of a detection reagent or device for prediction of gastric cancer prognosis risk, and preparation of a detection reagent or device for prediction of gastric cancer survival rate.
A kit for predicting gastric cancer prognosis and immunotherapy risk is characterized in that the kit comprises the marker and a risk model, wherein the risk model comprises a risk score calculation formula, and the risk score calculation formula is as follows: the risk score is (0.1491 × APOD expression level) + (0.3341 × APOE expression level) + (-0.5133 × CCDC80 expression level) + (0.1787 × CTHRC1 expression level) + (0.4312 × FERMT2 expression level) + (0.1498 × GXYLT2 expression level) + (0.1102 × SMPX expression level).
Preferably, according to the present invention, the method for predicting gastric cancer prognosis using the kit comprises the steps of:
(1) detecting the expression level of APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX in a gastric cancer patient sample;
(2) substituting the expression level obtained in the step (1) into a risk score calculation formula to calculate a risk score; when the risk score is higher than the cut-off value, the gastric cancer patient belongs to a high risk group, which indicates that the gastric cancer patient has poor prognosis and short survival time: when the risk score is lower than the cut-off value, the gastric cancer patient belongs to a low risk group, and the gastric cancer patient is prompted to have good prognosis and long survival period.
Further, the sample includes, but is not limited to, tissue, body fluid. In a particular embodiment of the invention, the sample is a body fluid, in particular blood;
further preferably, the cut-off value is the median risk score.
According to yet another aspect of the invention, a method for predicting the risk of immunotherapy using said kit, said method comprising the steps of:
(1) detecting the expression level of APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX in a gastric cancer patient sample;
(2) substituting the expression level obtained in the step (1) into a risk score calculation formula to calculate a risk score; when the risk score is higher than the cut-off value, the gastric cancer patient belongs to a high risk group, which indicates that the gastric cancer patient is suitable for immunotherapy, and when the risk score is lower than the cut-off value, the gastric cancer patient belongs to a low risk group, which indicates that the gastric cancer patient is not suitable for immunotherapy.
Further, the sample includes, but is not limited to, tissue, body fluid. In a particular embodiment of the invention, the sample is a body fluid, in particular blood;
further preferably, the cut-off value of the sample risk score is the median risk score.
The gastric cancer survival rate prediction model is characterized by being obtained by constructing a nomogram according to the risk score obtained by the risk model and the age and the patient stage.
Further preferably, the prediction model can predict survival rates 1 year, 3 years and 5 years after gastric cancer.
The determination of the expression levels of the above-described 7 genes of the invention follows established standard procedures well known in the art (Sambrook, J.et al (1989) Molecular Cloning: A Laboratory Manual.2nd Ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY; Ausubel, F.M.et al (2001) Current Protocols in Molecular biology.Wiley & Sons, Hoboken, NJ). The assay can be performed at the RNA level, e.g., by Northern blot analysis using mRNA probes, or after reverse transcription of RNA to detect cDNA levels, e.g., by real-time fluorescent quantitative PCR techniques.
APOD, APOE, CC disclosed in the inventionThe sequences of DC80, CTHRC1, FERMT2, GXYLT2 and SMPX all store a comprehensive database of gene expression ((Chttps://www.ncbi.nlm.nih.gov/geo/) In (1).
Compared with the prior art, the invention has the following beneficial effects:
the invention finds markers APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX related to the gastric cancer progression through a bioinformatics technology, then a risk model is constructed according to the 7 genes to further obtain a risk score, and the prognosis of the gastric cancer and the applicability of immunotherapy can be predicted through the risk score; and a gastric cancer survival rate prediction model is further constructed by combining the age and the patient stage, so that the survival rates of the gastric cancer patients for 1 year, 3 years and 5 years can be effectively predicted.
The invention provides a model for predicting the prognosis of a gastric cancer patient, which is composed of 7 genes as biomarkers. The invention provides a reliable method for analyzing the prognosis and immunotherapy of gastric cancer patients.
Description of the drawings:
FIG. 1, a adjacency matrix diagram showing a dendrogram and a clinical profile heatmap.
FIG. 2 is a graph of a scale-free network showing the selection of an optimal soft threshold; a: the relationship between the scale-free fit index (y-axis) and the soft threshold (x-axis) is shown, B: average connectivity (y-axis) and soft threshold (x-axis).
FIG. 3 is a similar gene hierarchy clustering dendrogram based on topological overlap.
FIG. 4 is a heat map of the correlation of modular signature genes with clinical features of gastric cancer.
FIG. 5, protein interaction (PPI) analysis of the midnight blue module gene to obtain two sub-network maps; a is subnetwork 1, B is subnetwork 2 FIG. 6, and a scatter diagram of module identification identity and stage correlation of genes in midnight blue module.
FIG. 7, a gene-intersection plot showing subnetwork 1 and module identity (greater than 0.8) and clinical staging (greater than 0.2).
Fig. 8 shows a consensus cluster map (k 2).
FIG. 9, shows the LASSO regression analysis chart.
FIG. 10 is a 7-gene multifactorial analysis chart showing the results of modeling.
FIG. 11, Kaplan-Meier survival analysis plot showing patients in different risk groups in the training set.
Fig. 12, graph showing the time-dependent ROC analysis of patients with different risk scores in the training set.
FIG. 13, Kaplan-Meier survival analysis plot showing patients in different risk groups in the validation set.
FIG. 14, Kaplan-Meier survival analysis plots showing patients in different risk groups in the GSE84437 dataset.
FIG. 15, Kaplan-Meier survival analysis plot showing patients in different risk groups in the TCGA dataset.
FIG. 16, Kaplan-Meier survival analysis plot showing patients in different risk groups in GSE15459 dataset.
FIG. 17, Kaplan-Meier survival analysis plot showing patients in different risk groups in GSE15460 dataset.
FIG. 18, Kaplan-Meier survival analysis plot showing patients in different risk groups in GSE62254 data set.
Figure 19, single and multi-factor analysis plots showing clinical factors for risk score.
Figure 20, nomogram constructed showing risk score in combination with clinical factors.
Fig. 21 is a calibration chart showing the nomogram predicted 1-year survival rate.
Fig. 22 is a correction graph showing the 3-year survival rate predicted by the nomogram.
Fig. 23 is a correction graph showing the nomogram predicted 5-year survival rate.
Fig. 24, graph showing the dependence of 1-year survival of gastric cancer patients based on age, stage and risk score ROC analysis.
Fig. 25, graph showing the dependency ROC analysis of 3-year survival of gastric cancer patients based on age, stage and risk score.
Fig. 26, graph showing the dependence of ROC analysis on 5-year survival of gastric cancer patients based on age, stage and risk score.
Figure 27, shows high and low risk group patient matrix score, immune score and total score map.
Figure 28, analysis graph showing correlation of risk score with tumor purity.
FIG. 29 shows the results of ssGSEA classification of gastric cancer patients into high and low immune groups.
Figure 30, caldron analysis histogram showing high and low risk groups and high and low immune groups.
Figure 31, graph of the difference between different immunolabels in the high and low risk groups.
Figure 32, graph of immunosuppressive checkpoint expression difference analysis for high and low risk groups.
The specific implementation mode is as follows:
the invention is described in detail below with reference to the drawings and examples, which are only preferred embodiments of the invention, and it should be noted that a person skilled in the art may make several modifications and additions without departing from the method of the invention, and these modifications and additions should also be regarded as the scope of protection of the invention.
The affy software package, input R software package, limma software package, WGCNA R software package, sva R software package, rms R software package, surfcomp software package, and surfval software package referred to in the following examples are all prior art and are derived from https:// cran.r-project.org or https:// www.bioconductor.org/, and are run in R software after being loaded.
Example 1 download of gastric cancer-associated Gene data
From a comprehensive database of gene expression (https://www.ncbi.nlm.nih.gov/geo/) The gastric cancer transcriptome chip raw data and clinical data GSE26901 (n: 109), GSE15460 (n: 248), GSE62254 (n: 300), GSE15459 (n: 192), and GSE84437 (n: 433) are downloaded. Gastric cancer transcriptome FPKM sequencing data and clinical data were downloaded from cancer genome maps (TCGA).
Transcriptome FPKM sequencing data were processed using the affy and input R software packages and differential gene expression analysis was performed using the limma software package. The assessment of the infiltration of stroma and immune cells in the tumor immune microenvironment was performed using the ESTIMATE (using expression data to ESTIMATE stroma and immune cells in malignant tumor tissue) algorithm. The GSE62254 and GSE15460 datasets were combined using the sva R software package to eliminate batch effects, yielding a total of 548 samples. 276 samples were then randomly drawn as a training set and the remaining 272 samples were taken as a validation set. ssGSEA (single sample gene enrichment analysis) analysis is carried out on the training set by adopting a gsva software package based on 29 immune-related gene sets, hierarchical clustering is carried out on the training set by using a Sparcl R software package according to scores, and the training set is divided into a high immune group and a low immune group.
Example 2 gastric cancer-associated Gene data processing
2.1 construction of weighted Gene Co-expression network (WGCNA)
Constructing a data matrix of GSE26901 gene expression by using a WGCNA R software package, and selecting genes with the variance of the first 25 percent as an input data set of a subsequent WGCNA. And then rejecting abnormal samples by using hierarchical clustering, constructing a scale-free network, calculating the dissimilarity of genes, modularizing the genes with similar expression profiles according to the dissimilarity, and calculating the correlation between modules and clinical phenotypes of gastric cancer.
2.2 authentication core Module
The genes in the module are input into a STRING (search tool for retrieving interacting genes/proteins) website to carry out PPI (protein-protein interaction) analysis to obtain PPI scores, then the PPI scores are input into Cytoscape to be analyzed by an MCODE plug-in unit, and finally two sub-networks are obtained.
2.3 construction and validation of Risk models
First, the batch effect was eliminated by combining the GSE62254 and GSE15460 data sets using the sva R software package, and a total of 548 gastric cancer patient samples were obtained. 276 samples of gastric cancer patients are randomly selected as a training set, and the rest 272 samples of gastric cancer patients are selected as a verification set. And (3) carrying out univariate Cox regression analysis on the difference genes of the training set and the verification set by using a survivval software package, carrying out LASSO (least absolute shrinkage and selection operation) regression analysis by using a glmnet software package, and finally establishing a multi-factor Cox risk model and obtaining a risk score.
2.4 construction and evaluation of nomograms
Nomograms and calibration plots were created from risk scores, age and patient stage using the rms R software package.
2.5 statistical analysis
Survival curves were plotted in R software using rms R software package and statistical analysis, ROC curves were plotted using surfcomp software package and surfval software package and AUC values were calculated.
Example 3 construction of a prognostic risk model
3.1WGCNA identified modules associated with tumor progression and further screened for core genes.
To identify genes associated with gastric cancer progression, a co-expression network was constructed in GSE26901 using WGCNA. After removing 5 outlier gastric cancer patient samples, the remaining 104 gastric cancer patient samples were used to construct a adjacency matrix map (fig. 1). Selecting β -9 as soft threshold to construct a scale-free network map (fig. 2), and finally constructing 10 gene co-expression modules (fig. 3). By calculating the correlation of gene co-expression modules with clinical features, the midnight-blue module was found to be most strongly correlated with AJCC-staging (fig. 4). To obtain the core genes, the PPI network in midnight-blue module was continuously analyzed on STRING website, and the results were imported into Cytoscape software and processed with MCODE plug-in to obtain two sub-networks of subnet1 and subnet2 (FIG. 5). Then, 33 genes in total, i.e., subnet1, which is a larger number of genes, were further selected as the study subjects. Considering both the importance of genes in the midnight-blue module and the correlation of clinical stages, the selection module identified 16 genes with identities greater than 0.8 and correlations with AJCC stages greater than 0.2 (fig. 6). When the two genes intersect, 9 core genes are obtained (FIG. 7).
3.2 establishing a prognostic risk model
Gastric cancer patient samples were clustered according to 9 gene expression values using a consensus clustering software package, dividing patients into two categories (fig. 8). The mRNA expression profiles of the two patients were then differentially analyzed to obtain a total of 200 differentially expressed genes (FDR <0.05, | log2FoldChange | >1), 174 of which were up-regulated and 26 were down-regulated. Performing one-factor Cox analysis on 200 differentially expressed genes in a training set to obtain 88 genes (P <0.01) with prognostic value, removing colinearity genes by using LASSO regression analysis (figure 9) to obtain 10 genes, performing multivariate Cox regression optimization to obtain 7 miRNA genes APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX (figure 10), and obtaining a risk scoring formula according to the regression coefficients of Cox and the optimized 7 genes: risk score ═ (0.1491 × APOD) + (0.3341 × APOE) + (-0.5133 × CCDC80) + (0.1787 × CTHRC1) + (0.4312 × FERMT2) + (0.1498 × GXYLT2) + (0.1102 × SMPX), a risk model was constructed. Patients in the training set were divided into high risk groups and low risk groups according to median risk score. Kaplan-Meier survival analysis showed that the prognosis for patients in the high risk group was worse than in the low risk group (FIG. 11). The time-dependent ROC curve shows that the area under the curve (AUC) values for 3 years, 5 years are 0.759 and 0.738, respectively (fig. 12), indicating that the risk score can predict patient survival with higher accuracy. Next, in the validation set, GSE15459, GSE15460, GSE62254, GSE84437 (this data was also downloaded from the comprehensive database of gene expression) and TCGA data set, the Kaplan-Meier Total survival curves showed poor prognosis in the high risk group (FIGS. 13-18).
3.3 clinical value of Risk model
Univariate Cox regression analysis in the training set combined with gender, age, tumor stage, and lorentzian, found that age, patient stage, and risk score had significant prognostic significance (fig. 19). The results of the multivariate Cox regression analysis indicated that risk score could be an independent prognostic factor (fig. 19). An alignment chart was then constructed in the training set, which integrated age, patient stage and risk score (fig. 20). The line segments in all three calibration plots are close to the 45 degree line, indicating that the nomograms show good predictive performance in all of 1 year, 3 years and 5 years (fig. 21-23). The C-index (consistency index) was 0.766 and the 95% Confidence Interval (CI) was 0.730-0.801. ROC analysis was used to assess prediction accuracy of nomograms: the area under the curve (AUC) values of the 1-year, 3-year and 5-year line graphs were 0.846, 0.849 and 0.845, respectively (fig. 24-26). The results all show that the nomogram has good prediction performance.
Example 4 Risk model and immunotherapy suitability relationship
Differences in tumor microenvironment in the high and low risk groups in the GSE62254 dataset were compared. The results show that the high risk group had significantly higher immune, stromal and total scores than the low risk group (fig. 27), while tumor purity was significantly negatively correlated with risk score (fig. 28). The samples were then divided into hyperimmune and hypoimmunized groups based on the 29 immune profiles in GSE62254 using ssGSEA (figure 29), and chi-square analysis test results showed a higher proportion of hyperimmune patients in the hyperimmune group and a higher proportion of hypo-risk patients in the hypo-risk group (figure 30). Further comparing the difference in 29 immune profiles in the higher risk and lower risk groups, most of the profiles, such as immune response-related profile, CD8+ T cells, NK cells, checkpoint, TIL and IFN responses, were found to be expressed at higher levels in the higher risk group (fig. 31). The expression level of immunosuppressive checkpoint molecules continues to be higher in the higher low risk group. The immune checkpoint molecule expression levels were found to be significantly higher in the high risk group than in the low risk group (fig. 32). The above results indicate that patients in the high risk group benefit from immunotherapy more than patients in the low risk group, and that patients in the high risk group benefit from immunotherapy more than patients in the low risk group.
Claims (10)
1. A marker for predicting gastric cancer prognosis and suitability for immunotherapy, wherein the marker is an mRNA marker consisting of APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX; the mRNA marker is used for predicting and predicting gastric cancer prognosis and immunotherapy applicability after calculating a risk score through a risk score calculation formula;
the risk score calculation formula is as follows: risk score = (0.1491 × APOD expression level) + (0.3341 × APOE expression level) + (-0.5133 × CCDC80 expression level) + (0.1787 × CTHRC1 expression level) + (0.4312 × FERMT2 expression level) + (0.1498 × GXYLT2 expression level) + (0.1102 × SMPX expression level).
2. The use of the markers for predicting gastric cancer prognosis and suitability for immunotherapy as claimed in claim 1, wherein the use comprises any one or more of evaluation or prediction of prognosis risk, prediction of suitability for immunotherapy, prediction of survival rate, treatment/medication planning, construction of a model for predicting gastric cancer prognosis risk, construction of a model for suitability for immunotherapy, construction of a model for predicting gastric cancer survival rate, preparation of a detection reagent or device for predicting gastric cancer prognosis risk, and preparation of a detection reagent or device for predicting gastric cancer survival rate.
3. A kit for predicting gastric cancer prognosis and immunotherapy risk, comprising the marker of claim 1 and a risk score calculation formula.
4. The kit of claim 3, wherein the kit is used in a method for predicting gastric cancer prognosis, the method comprising the steps of:
(1) detecting the expression level of APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX in a sample of the gastric cancer patient;
(2) substituting the expression level obtained in the step (1) into a risk score calculation formula of claim 1 to calculate a risk score; when the risk score is higher than the cut-off value, the gastric cancer patient belongs to a high risk group, which indicates that the gastric cancer patient has poor prognosis and short survival time: when the risk score is lower than the cut-off value, the gastric cancer patient belongs to a low risk group, and the gastric cancer patient is prompted to have good prognosis and long survival period.
5. The kit of claim 4, wherein the sample comprises a tissue, a body fluid; the cut-off value of the sample risk score is the median risk score.
6. The kit of claim 3, wherein said kit is used in a method for predicting the risk of immunotherapy comprising the steps of:
(1) Detecting the expression level of APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX in a sample of the gastric cancer patient;
(2) substituting the expression level obtained in the step (1) into a risk score calculation formula in claim 1 to calculate a risk score; when the risk score is higher than the cut-off value, the gastric cancer patient belongs to a high risk group, the gastric cancer patient is indicated to be suitable for the immunotherapy, and when the risk score is lower than the cut-off value, the gastric cancer patient belongs to a low risk group, the gastric cancer patient is indicated to be not suitable for the immunotherapy.
7. The kit of claim 6, wherein the sample comprises a tissue, a body fluid; the cut-off value for the sample risk score is the median risk score.
8. A kit as claimed in claim 5 or claim 7, wherein the bodily fluid is blood.
9. A gastric cancer survival rate prediction model, which is obtained by constructing a nomogram according to the risk score calculated by the risk score calculation formula according to claim 1 in accordance with age and patient stage.
10. The predictive model of claim 9, wherein the predictive model predicts survival rates 1 year, 3 years, 5 years after gastric cancer.
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