A novel prognostic signature of immune-related genes for patients with colorectal cancer

Abstract

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with an estimated 1.8 million new cases worldwide and associated with high mortality rates of 881 000 CRC-related deaths in 2018. Screening programs and new therapies have only marginally improved the survival of CRC patients. Immune-related genes (IRGs) have attracted attention in recent years as therapeutic targets. The aim of this study was to identify an immune-related prognostic signature for CRC. To this end, we combined gene expression and clinical data from the CRC data sets of The Cancer Genome Atlas (TCGA) into an integrated immune landscape profile. We identified a total of 476 IRGs that were differentially expressed in CRC vs normal tissues, of which 18 were survival related according to univariate Cox analysis. Stepwise multivariate Cox proportional hazards analysis established an immune-related prognostic signature consisting of SLC10A2, FGF2, CCL28, NDRG1, ESM1, UCN, UTS2 and TRDC. The predictive ability of this signature for 3- and 5-year overall survival was determined using receiver operating characteristics (ROC), and the respective areas under the curve (AUC) were 79.2% and 76.6%. The signature showed moderate predictive accuracy in the validation and GSE38832 data sets as well. Furthermore, the 8-IRG signature correlated significantly with tumour stage, invasion, lymph node metastasis and distant metastasis by univariate Cox analysis, and was established an independent prognostic factor by multivariate Cox regression analysis for CRC. Gene set enrichment analysis (GSEA) revealed a relationship between the IRG prognostic signature and various biological pathways. Focal adhesions and ECM-receptor interactions were positively correlated with the risk scores, while cytosolic DNA sensing and metabolism-related pathways were negatively correlated. Finally, the bioinformatics results were validated by real-time RT-qPCR. In conclusion, we identified and validated a novel, immune-related prognostic signature for patients with CRC, and this signature reflects the dysregulated tumour immune microenvironment and has a potential for better CRC patient management.

Keywords: TCGA; bioinformatics; colorectal cancer; immunogenomic landscape; prognostic signature.

FIGURE 1

Flow chart for the development and verification of an immune‐related prognostic signature for CRC

FIGURE 2

Screening of differentially expressed immune‐related genes (IRGs) in colorectal cancer (CRC). A, Volcano plot showing the differentially expressed IRGs in tumours vs normal tissue samples. Blue dots represent down‐regulated IRGs, and red dots represent up‐regulated IRGs. B, Gene expression heat map of differentially expressed IRGs in CRC. C, Results of the gene ontology (GO) term enrichment study. D, Results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment study

FIGURE 3

Construction of the immune‐related prognostic signature in CRC. A, Forest plot of immune‐related prognostic genes based on univariate Cox regression analysis. B, Kaplan‐Meier plots of the immune‐related signature showing worse survival in the high‐risk group compared to the low‐risk group (log‐rank P‐value < .0001). C, Time‐dependent (ROC) curve of the immune‐related signature for 1‐, 3‐ and 5‐year overall survival. D, Distribution of the survival status, risk score and gene expression data of CRC patients in the training group

FIGURE 4

Verification of the immune‐related signature in three independent cohorts. A, Kaplan‐Meier plots of the immune‐related signature in the validation group. B, Time‐dependent receiver operating characteristics (ROC) curve of the immune‐related signature in the validation group. C, Kaplan‐Meier plots of the immune‐related signature in The Cancer Genome Atlas (TCGA) data set. D, Time‐dependent ROC curve of the immune‐related signature in TCGA data set. E, Kaplan‐Meier plots of the immune‐related signature in the GSE38832 database. F, Risk score distribution in low‐ and high‐risk groups

FIGURE 5

Association of the immune‐related signature with clinicopathological characteristics. A, Forest plot of risk scores and other clinical factors based on a univariable Cox regression analysis. B, Forest plot of risk scores and other clinical factors based on a multivariate Cox regression analysis. C, Expression profiles of the eight immune‐related genes. D, Box plots showing risk score distribution of different clinical factors

FIGURE 6

Expression of immune‐related genes (IRGs) associated with clinicopathological features, and construction of a nomogram for survival assessment. A, Associations between the different IRGs and clinicopathological features. B, Nomogram integrating the immune‐related signature to clinicopathological characteristics. C, Plots displaying the calibration of each model comparing predicted and actual 3‐ and 5‐year overall survival. The graph relative to the 45° line showing the model relative to perfect prediction

FIGURE 7

Correlation of immune‐related genes with biomarkers and construction of a competing endogenous RNA (ceRNA) network for colorectal cancer. A, BRAF gene expression in risk groups. B, NRAS gene expression in risk groups. C, PIK3CA gene expression in risk groups. D, The regulatory network of ceRNAs based on immune‐related genes