Construction and validation of prognostic risk model based on radiosensitivity-related immune genes in rectal cancer

Abstract

Abstract Background Radiotherapy is closely related to the tumor immune microenvironment, but the role of immune genes in radiosensitivity and prognosis of rectal cancer (RC) is still unclear. This study aims to construct a prognostic risk model based on radiosensitivity-related immune genes (RRIGs), which can be used for predicting prognosis of RC. Methods GSE133057 dataset of RC was downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between different radiosensitivity groups. RRIGs were obtained by intersecting DEGs and immune genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were performed to study the biological functions of RRIGs. Transcriptomic and clinical data of RC were downloaded from The Cancer Genome Atlas (TCGA) database, and the entire cohort was randomly divided into training and testing set at a ratio of 7:3. Prognostic genes were selected by Cox analysis, and a risk model and nomogram were subsequently built. The relationship between the model and immune cell infiltration was analyzed by single-sample gene set enrichment analysis (ssGSEA). Results A total of 76 RRIGs were identified, and they were mainly involved in immune-related biological processes and pathways. BMP2, COLEC10, MASP2, and GCGR were screened as prognostic genes after Cox regression analysis. Subsequently, these prognostic genes were used to construct a risk score model, which demonstrated good performance in predicting prognosis, as proven by the receiver operating characteristic (ROC) curves. Cox regression analysis showed that the risk score was an independent prognostic factor for RC. Moreover, we found that the immune microenvironment was different between the low- and high-risk groups. Conclusions We developed and validated a prognostic risk model based on RRIGs, which could serve as a tool for predicting prognosis of RC. These findings enhanced the understanding of the relationship among radiosensitivity, immune genes and prognosis in RC.