T cell exhaustion-related exosome genes for predicting survival and immunotherapy efficacy in colorectal cancer

Background Treatment options for colorectal cancer are limited. T cell exhaustion is one of the barriers to tumor immunotherapy. No comprehensive analysis of T cell exhaustion-related exosome prognostic models for colorectal cancer (CRC) has been conducted. Method Samples were collected from the Cancer Genome Atlas (TCGA) database, exoRBase database and Gene Expression Omnibus (GEO) database. The single sample gene set enrichment analysis (ssGSEA) algorithm screened out T cell exhaustion-related exosome differential expression genes, signature genes were screened by univariate Cox regression and Lasso regression, and risk score models were constructed and validated. A nomogram containing risk scores and clinical parameters was established and evaluated. In addition, single cell analysis and tumor immune microenvironment assessment were also performed. Results Sixteen signature genes were identified, based on which the risk score model was constructed and validated. This model can predict the overall survival (OS) of TCGA and GEO queues well. Scores were identified as independent risk factors for OS and correlated with certain clinicopathological features. A nomogram was developed that integrated clinical parameters and risk scores and showed higher predictive accuracy. Finally, significant differences in immune microenvironment were found between the high- and low-risk groups. Thus, scores can also be used to predict the response to immunotherapy. Conclusions In general, we screened out T cell exhaustion-related exosome genes of CRC, constructed a risk score model which could predict survival and immunotherapy efficacy, and found correlations between risk scores and clinicopathologic features and immune microenvironment.