Construction of an immune gene expression meta signature to assess the prognostic risk of colorectal cancer patients.

Abstract

Despite recent advancements in colorectal cancer (CRC) treatment, particularly with the introduction of immunotherapy and checkpoint inhibitors, the efficacy of these therapies remains limited to a subset of patients. To address this challenge, our study aimed to develop a prognostic biomarker based on immune-related genes to predict better outcomes in CRC patients and aid in treatment decision-making. We comprehensively analysed immune gene expression signatures associated with CRC prognosis to construct an immune meta-signature with prognostic potential. Utilising data from The Cancer Genome Atlas (TCGA), we employed Cox regression to identify immune-related genes with prognostic significance from multiple studies. Subsequently, we compared the expression levels of immune genes, levels of immune cell infiltration, and various immune-related molecules between high-risk and low-risk patient groups. Functional analysis using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses provided insights into the biological pathways associated with the identified prognostic genes. Finally, we validated our findings using a separate CRC cohort from the Gene Expression Omnibus (GEO). Integration of the prognostic genes revealed significant disparities in survival outcomes. Differential expression analysis identified a set of immune-associated genes, which were further refined using LASSO penalisation and Cox regression. Univariate Cox regression analyses confirmed the autonomy of the gene signature as a prognostic indicator for CRC patient survival. Our risk prediction model effectively stratified CRC patients based on their prognosis, with the high-risk group showing enrichment in pro-oncogenic terms and pathways. Immune infiltration analysis revealed an augmented presence of certain immunosuppressive subsets in the high-risk group. Finally, we validated the performance of our prognostic model by applying the risk score equation to a different CRC patient dataset, confirming its prognostic potential in this new cohort. Overall, our study presents a novel immune-related gene signature with promising implications for predicting cancer progression and prognosis, thereby enabling more personalised management strategies for CRC patients.