CN114334147A - Application of combined STAT signal pathway related genes in colorectal cancer prognosis model - Google Patents
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Abstract
The invention provides application of combined STAT signal pathway related genes in a colorectal cancer prognosis model, wherein the combined STAT signal pathway related genes are CAV1, EPO, IL13, LEP and NEUROD 1; the method for establishing the colorectal cancer prognosis model comprises the steps of 1) data collection and arrangement, 2) screening of STAT signal pathway related genes with differential expression, 3) construction of the prognosis model of the STAT signal pathway related genes, and 4) construction of a nomogram. The prognosis model has the advantage of high accuracy, can provide a new method for disease diagnosis and prognosis for colorectal cancer patients clinically, simultaneously discloses the prediction effect of the prognosis model on tumor microenvironment, and provides important information for the development of targeted therapy.
Description
Technical Field
The invention belongs to the technical field of tumor molecular biology, and particularly relates to application of a combined STAT signal pathway related gene in a colorectal cancer prognosis model.
Background
In 2020, colorectal cancer (CRC) is listed as the third most common malignant tumor worldwide, with more than 180 million new cases and 90 million death cases each year, and seriously threatens human health. Over the past decade, rapid development of therapeutic technologies has provided promise for the treatment of CRC. With the aid of genomics, transcriptomics, proteomics and epigenomic data, targeted therapy is becoming a new choice but still in the phase of initiative compared to traditional therapeutic approaches. Therefore, the inventors are in need of new markers and predictive models to assess the prognosis of patients.
In tumor cells and microenvironments, Signal Transduction and Activator of Transcription (STAT) signals are essential conditions for tumorigenesis to develop. STAT protein is a transcription factor that is activated primarily by direct stimulation of tyrosine phosphorylation and serine residues, and mediates activation of a variety of downstream signaling pathways. The role of STAT in most cancer types has been studied to date, but the role of STAT signaling pathways, including its regulators and effectors, in colorectal cancer remains largely unknown.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the application of the combined STAT signal pathway related gene in a colorectal cancer prognosis model, has the advantage of high accuracy, and can provide a new method for disease diagnosis and prognosis for colorectal cancer patients clinically.
In order to achieve the purpose, the specific technical scheme of the invention is as follows:
use of a combined STAT signaling pathway related gene in a colorectal cancer prognosis model, wherein the combined STAT signaling pathway related gene is CAV1, EPO, IL13, LEP, and NEUROD 1.
As another embodiment of the present invention, the method for establishing a colorectal cancer prognosis model comprises the following steps:
step 1) data collection and arrangement
Obtaining clinical data and gene expression data of colorectal cancer from a cancer genomic map (TCGA) database and a gene expression integration (GEO) database;
step 2) screening STAT signal pathway related genes differentially expressed
Performing differential expression analysis on a plurality of known STAT signal pathway related genes by using a Limma package, demonstrating a gene interaction network by using an 'igraph' package, and screening a differentially expressed gene combination compared with a normal sample;
step 3) construction of prognosis model of STAT signal pathway related gene
Adopting single-factor COX regression analysis to evaluate each differential expression gene related to the prognosis of the colorectal cancer patient screened in the step 2), further screening to obtain a differential gene combination related to an STAT signal channel according to a P value and LASSO Cox regression analysis, wherein the calculation method of the evaluation model comprises the following steps:
a risk index score ═ i;
wherein Coefi represents the coefficient and Xi represents the normalized gene expression level;
step 4) constructing a nomogram
Independent predictors were integrated using rms, foreign and survival packages based on genetic and clinical characteristics, nomograms were constructed by correction and elimination trend correspondence analysis (DCA), patient survival was assessed and model efficacy was assessed.
As another embodiment of the present invention, the differentially expressed genome combinations selected in step 2) compared to the normal sample are IFNL3, IFNE, CSF2, IFNL2, IL23A, AGT, IL20, OSM, LIF, IL13, PRL, EPO, HAMP, CENPJ, MGAT5, PIGU, OCS2, TSLP, IL6ST, PTPRC, PTK2B, HCLS1, NEUROD1, CCL5, IL10, CSF1R, IL10RA, PTPRT, KIT, LEP, ERBB4, IGF1, IL2, CAV1 and GHR.
As another embodiment of the present invention, the set of differences related to STAT signal pathway screened in step 3) is CAV1, EPO, IL13, LEP and NEUROD 1.
As another embodiment of the present invention, the risk score in step 3) is calculated as:
risk index score ═ (0.009 × CAV1mRNA expression level) + (2.236 × EPO mRNA expression level) + (((-0.600) × IL13mRNA expression level) + (0.116 × lepmra expression level) + (0.059 × neuurod 1mRNA expression level).
As another embodiment of the present invention, in step 3), the patients are divided into a high risk group and a low risk group according to a median risk score, the overall survival rate between the two groups is compared using Kaplan-Meier analysis, and the test is performed using log-rank.
As another embodiment of the invention, the separability of the high-risk group and the low-risk group is evaluated by Principal Component Analysis (PCA), the survival, risk regression, timeROC and surfminer packages are used to construct a receiver operating characteristic curve (ROC), and the accuracy of the gene markers is evaluated by the area under the curve (AUC).
The invention has the following beneficial effects:
the invention utilizes the TCGA database and the GEO database to analyze the whole genome of the colorectal cancer, establishes a prognosis model related to the colorectal cancer prognosis based on the five STAT signal path related genes, has the advantage of high accuracy, and can provide a new method for disease diagnosis and prognosis for colorectal cancer patients clinically; meanwhile, the prediction effect of the compound on the tumor microenvironment is disclosed, and important information is provided for the development of targeted therapy.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a heatmap of 35 distinct genes in colorectal cancer tissue and normal tissue;
FIG. 2 is a correlation network of 35 differentially expressed STAT signal-related genes;
FIG. 3 is a forest plot of univariate Cox regression analysis showing risk ratios and 95% confidence intervals;
FIG. 4 is a heat map of clinical pathology and risk score of the incorporated colorectal cancer patients;
FIG. 5 is a schematic of the distribution of risk scores, time to live and status of survival;
FIG. 6 is a Kaplan-Meier plot of Overall Survival (OS) between low-risk and high-risk colorectal cancer patients;
FIG. 7 is a graph of Receiver Operating Characteristics (ROC) for a 3-year OS prognosis model;
FIG. 8 is a graph of Receiver Operating Characteristics (ROC) for a 5-year OS prognosis model;
FIG. 9 is a univariate analysis evaluation predictor;
FIG. 10 is a multivariate analysis evaluating predictor factors;
FIG. 11 is a nomogram predictive of survival curves for patients with colorectal cancer;
FIG. 12 is a calibration graph of a prediction curve and an observation curve;
FIG. 13 is a Decision Curve Analysis (DCA) curve of the predictive model;
FIG. 14 is a chart of Receiver Operating Characteristics (ROC) curves assessing the predictive value of nomograms for 3-year survival in colorectal cancer patients;
FIG. 15 is a Receiver Operating Characteristic (ROC) curve evaluating the predictive value of a nomogram for 5-year survival in colorectal cancer patients;
FIG. 16 is a graph of survival status, survival time and risk score distribution;
FIG. 17 is a Kaplan-Meier curve of Overall Survival (OS) between low-risk and high-risk colorectal cancer patients;
FIG. 18 is a univariate analysis evaluation predictor;
FIG. 19 is a multivariate analytical evaluation predictor factor;
FIG. 20 is a calibration graph of predicted and observed curves for a nomogram;
FIG. 21 is a Decision Curve Analysis (DCA) curve of a nomogram;
figure 22 is a predictive value of Receiver Operating Characteristic (ROC) curve evaluation nomograms for 3-year survival of colorectal cancer patients in a GSE14333 cohort;
figure 23 is a predictive value of Receiver Operating Characteristic (ROC) curve evaluation nomograms for 5-year survival of colorectal cancer patients in a GSE14333 cohort;
FIG. 24 is a GO pathway enrichment analysis of Differentially Expressed Genes (DEG);
FIG. 25 is KEGG pathway enrichment analysis of Differentially Expressed Genes (DEG)
FIG. 26 is the expression of CAV1 in colon and rectal cancers and the relationship to immune cell infiltration;
FIG. 27 shows scores for low risk immune-related cells (left) and function (right) in TCGA cohort;
figure 28 shows scores for immune-related cells (left) and function (right) of high risk groups in TCGA cohort;
FIG. 29 is a summary of immune cell composition;
FIG. 30 is a graph showing the differences in immune cell composition by risk group I;
FIG. 31 is a second graphical representation of the differences in immune cell composition by risk group;
figure 32 shows the correlation of immune cell infiltration.
In the figure, T represents tumor, N represents normal tissue, HR represents hazard ratio, LL represents lower limit, UL represents upper limit, AUC represents area under the curve, and TME represents tumor microenvironment.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Method
1) Data collection
Clinical data and gene expression data for colorectal cancer are obtained from a cancer genomic map (TCGA) database and a gene expression integration (GEO) database.
2) Identification of Differentially Expressed Genes (DEGs) in STAT signaling pathway
The STAT signal pathway related gene is obtained from a gene attribute classification (GO) class name "STAT receptor signal pathway" (STAT); a total of 175 STAT signal pathway-associated genes were identified and further analyzed by "limma" package selection for DEGs (P <0.001, | log2FC | >1) between CRC and normal samples, and the "igraph" software package was used to demonstrate the gene interaction network.
3) Construction of prognostic Gene signatures
And evaluating the predicted value of the DEGs by adopting a one-factor Cox regression model. Using LASSO Cox regression analysis, the inventors calculated the risk score using the formula:
where Coefi represents the coefficient and Xi represents the normalized gene expression level.
Patients were divided into two groups of low and high risk, and their separability was evaluated by Principal Component Analysis (PCA), the difference in Overall Survival (OS) was evaluated by Kaplan-Meier analysis, and log-rank test was used. The accuracy of gene markers was assessed by Receiver Operating Characteristic (ROC) curves and area under the curve (AUC) using the "survival", "risk regression", "timeROC" and "surfminer" software packages.
Single and multifactor Cox regression analyses were further performed in combination with clinical data. The independent prediction factors were integrated by three R-packages, "rms", "foreign" and "survival", and nomograms were constructed by corrected and eliminated trend correspondence analysis (DCA). For the GSE14333 dataset, a risk score was obtained by the method described above.
4) Functional enrichment analysis of features
In order to research potential pathways and functions related to features, the inventor uses a 'clusterirprofiler' package to perform GO and Kyoto gene and genome encyclopedia (KEGG) pathway enrichment analysis, and the screening thresholds are respectively | log2FC | ≧ 0.5 and P < 0.05.
5) Immune cell infiltration and component analysis
The relationship between prognostic genes and immune cell infiltration was analyzed using a tumor immune assessment resource (TIMER) database. In addition, infiltration scores of 16 immune cells and 13 immune-related pathways were assessed using the "gsva" package, single sample gene set enrichment (ssGSEA) analysis. The inventors further evaluated the differences in the composition of 22 immune cell types using the CIBERSORT algorithm.
6) Statistical analysis
The inventors performed statistical analysis using R software (version 4.1.0) and SPSS software (version 25.0). Comparing the two groups of averages by using a student t test; the classification variable difference is evaluated by adopting Pearson's Chi-Square test; comparing survival difference by using Log-rank test to generate a Kaplan-Meier curve for visualization; evaluating prognostic factors by adopting single-factor and multi-factor Cox regression analysis; differences in immune cell infiltration and composition were assessed by the Wilcoxon test.
Results
1) Differential genes associated with STAT signaling pathway in colorectal cancer
Analysis of colorectal cancer patient transcriptome data in the TCGA database screened 35 differentially expressed genes associated with STAT signal pathway (genes that met P <0.001 and | log2 (fold difference [ FC ]) | >1 as differential genes).
Compared to normal tissue, as shown in fig. 1, the expression levels of IFNL3, IFNE, CSF2, IFNL2, IL23A, AGT, IL20, OSM, LIF, IL13, PRL, EPO, HAMP, CENPJ, MGAT5, PIGU are significantly up-regulated in colorectal cancer tissue, while SOCS2, TSLP, IL6ST, PTPRC, PTK2B, HCLS1, NEUROD1, CCL5, IL10, CSF1R, IL10RA, PTPRT, KIT, LEP, ERBB4, IGF1, IL2, CAV1, GHR are significantly down-regulated in colorectal cancer, wherein 35 differentially expressed gene interactions are shown in fig. 2 and detailed information of 35 differentially expressed genes is shown in fig. 3.
2) Construction of prognosis model of STAT signal pathway-related gene
Single-factor COX regression analysis Each differentially expressed gene associated with the prognosis of colorectal cancer patients was evaluated and 5 differentially expressed genes associated with STAT signals (CAV1, EPO, IL13, LEP, and NEUROD1) were further screened based on P-values and LASSO Cox regression analysis.
The prognostic characteristic model is constructed into a risk index scoring formula as follows:
risk index score ═ (0.009 × CAV1mRNA expression level) + (2.236 × EPO mRNA expression level) + (((-0.600) × IL13mRNA expression level) + (0.116 × lepmra expression level) + (0.059 × neuurod 1mRNA expression level).
Colorectal cancer patients were divided into low risk groups and high risk groups according to median risk score.
The clinical pathology of colorectal cancer patients is shown in fig. 4, and the distribution of risk scores and survival time is shown in fig. 5. The Kaplan-Meier survival curves show: patients with higher risk scores had poorer survival (P < 0.001; as shown in figure 6) compared to patients with lower risk scores. The area under the curve for 3-year and 5-year survival rates were 0.644 and 0.668, respectively (as shown in fig. 7 and 8).
Univariate Cox regression analysis showed that the risk score was significantly correlated with OS (as shown in fig. 9), and multivariate Cox regression analysis showed that the risk score independently predicted prognosis for colorectal cancer patients (as shown in fig. 10).
3) Nomogram construction based on risk scoring
For quantitative assessment and prediction of survival of CRC patients, a nomogram based on risk score was established as shown in fig. 11, with predicted values of nomogram verified by a calibration curve as shown in fig. 12.
The nomogram provides a better prognostic value (as shown in fig. 13) compared to treatment of all patients or no treatment regimen, with AUC values of the ROC curve of 0.781 and 0.812 for prediction of 3-and 5-year survival (as shown in fig. 14, fig. 15, respectively).
4) Verification of STAT signal-related gene features by external data set
To validate the STAT signal-related gene signature, a GSE14333 dataset, which contained the raw transcriptome sequencing data of 226 colorectal cancer patients and clinical information of the patients, was used for validation.
The distribution of risk scores and survival times is shown in figure 16.
Patient survival was significantly reduced for patients with higher risk scores (as shown in FIG. 17; P <0.0001) compared to patients in the low risk group, the risk score was significantly correlated with patient survival (as shown in FIG. 18) and was an independent predictor of multivariate Cox regression analysis (as shown in FIG. 19), and the predictive value of the nomogram was verified by the calibration curve as shown in FIG. 20; nomograms provided better prognostic value (as shown in fig. 21), with AUC values of 0.686 and 0.750 for nomogram ROC curves used to predict 3-year and 5-year survival (as shown in fig. 22, 23, respectively).
5) Functional analysis of risk groups
To explore biological functions and pathways associated with risk scoring, the inventors performed GO and KEGG pathway enrichment analysis to derive Differentially Expressed Genes (DEG).
As shown in fig. 24, analysis of the GO pathway showed that Differentially Expressed Genes (DEG) were significantly associated with immune-related pathways, including modulation of immune effector processes, lymphocyte-mediated immunity, various immune responses, etc., whereas analysis of the KEGG pathway showed significant dysregulation of cytokine-cytokine receptor interactions and chemokine signaling pathways (as shown in fig. 25).
6) Immunogenomics analysis of colorectal cancer patient characteristics
The association of each gene in the STAT signal pathway-based features with immune infiltration was evaluated by the TIMER database.
Of these 5 genes, CAV1 was significantly associated with B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, and dendritic cell infiltration in colorectal cancer (as shown in fig. 26), while the association between other genes and immune cell infiltration was not significant.
To comprehensively assess the immunological profile of colorectal cancer, a single sample of 29 immune gene sets was subjected to ssGSEA scoring, and patients with higher risk scores had significantly reduced immune cell infiltration (as shown in figure 27) and down-regulation of immune-related pathways (as shown in figure 28) in the TCGA cohort compared to patients with lower risk scores.
Furthermore, the composition of infiltrating immune cells was assessed by CIBERSORT algorithm, a summary of tumor microenvironment cellular composition is shown in figure 29, and studies showed that the level of infiltration of activated CD4+ memory T cells, eosinophils, neutrophils, follicular helper T cells and M0 macrophages was significantly different in the low risk and high risk groups (shown in figure 30, figure 31, respectively).
The correlation of 22 immune cells indicates that M0 macrophages are negatively associated with resting dendritic cells (r ═ 0.41), resting mast cells (r ═ 0.41) and CD8+ T cells (r ═ 0.39), and furthermore, resting mast cells are negatively associated with activated mast cells (r ═ 0.42) (as shown in fig. 32).
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the scope of the invention. It will be appreciated by those skilled in the art that changes may be made without departing from the scope of the invention, and it is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.
Claims (7)
1. Use of a combined STAT signaling pathway related gene in a colorectal cancer prognosis model, wherein the combined STAT signaling pathway related gene is CAV1, EPO, IL13, LEP, and NEUROD 1.
2. Use of the combined STAT signaling pathway-related gene as claimed in claim 1 in a colorectal cancer prognosis model, wherein the colorectal cancer prognosis model is established by a method comprising the steps of:
step 1) data collection and arrangement
Obtaining clinical data and gene expression data of colorectal cancer from a cancer genomic map (TCGA) database and a gene expression integration (GEO) database;
step 2) screening STAT signal pathway related genes differentially expressed
Performing differential expression analysis on a plurality of known STAT signal path related genes by using a Limma package, and screening a differentially expressed gene combination compared with a normal sample by using an igraph package search gene interaction network;
step 3) construction of prognosis model of STAT signal pathway related gene
Adopting single-factor COX regression analysis to evaluate each differential expression gene related to the prognosis of the colorectal cancer patient screened in the step 2), further screening to obtain a differential gene combination related to an STAT signal channel according to a P value and LASSOCox regression analysis, wherein the calculation method of the evaluation model comprises the following steps:
wherein Coefi represents the coefficient and Xi represents the normalized gene expression level;
step 4) constructing a nomogram
Independent predictors were integrated using rms, foreign and survival packages based on genetic and clinical characteristics, nomograms were constructed by correction and elimination trend correspondence analysis (DCA), patient survival was assessed and model efficacy was assessed.
3. Use of the combined STAT signaling pathway-associated genes of claim 2 in a colorectal cancer prognosis model, wherein the differentially expressed genome selected in step 2) compared to the normal sample is selected from the group consisting of IFNL3, IFNE, CSF2, IFNL2, IL23A, AGT, IL20, OSM, LIF, IL13, PRL, EPO, HAMP, CENPJ, MGAT5, PIGU, OCS2, TSLP, IL6ST, PTPRC, PTK2B, HCLS1, NEUROD1, CCL5, IL10, CSF1R, IL10RA, PTPRT, KIT, LEP, ERBB4, IGF1, IL2, CAV1 and GHR.
4. Use of the combined STAT signal pathway-associated genes of claim 3 in a colorectal cancer prognosis model, wherein the panel of differences associated with the STAT signal pathway screened in step 3) is CAV1, EPO, IL13, LEP and NEUROD 1.
5. Use of the combined STAT signaling pathway-associated gene as claimed in claim 4 in a colorectal cancer prognosis model, wherein the risk score in step 3) is calculated as:
risk index score ═ (0.009 × CAV1mRNA expression level) + (2.236 × EPOmRNA expression level) + (((-0.600) × IL13mRNA expression level) + (0.116 × lepmra expression level) + (0.059 × neuurod 1mRNA expression level).
6. Use of combined STAT signaling pathway related genes in a colorectal cancer prognosis model according to claim 4 wherein in step 3) the patients are divided into high risk and low risk groups based on median risk score, the overall survival between the two groups is compared using Kaplan-Meier analysis and tested using log-rank.
7. Use of the combined STAT signaling pathway-associated genes in a colorectal cancer prognosis model according to claim 6, wherein the separability is evaluated by Principal Component Analysis (PCA) on the high risk group and the low risk group, respectively, and the accuracy of the gene markers is evaluated by area under the curve (AUC) by constructing a receiver operating characteristic curve (ROC) using the "survival", "risk regression", "timeROC" and "surfminer" packages.
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