Multiomics Identification of Radioresistance-Associated Biomarkers and Prognostic Model Construction in Rectal Cancer

Radiotherapy remains a cornerstone in the local management of rectal cancer (RC); however, resistance to radiation significantly compromises therapeutic efficacy and adversely affects patient prognosis. Identification of biomarkers associated with radioresistance and the development of a prognostic model based on radiotherapy-related genes in RC remain critical for enhancing treatment outcomes.

Data related to RC were obtained from public repositories, including bulk RNA-seq data from 993 patients across four Gene Expression Omnibus (GEO) and one The Cancer Genome Atlas (TCGA) cohort, as well as single-cell RNA-seq data from six samples. A prognostic model was developed using differential expression analysis, functional enrichment analysis, and least absolute shrinkage and selection operator regression analysis. Associations between risk score and prognosis were assessed through gene set variation analysis, gene set enrichment analysis, and construction of a nomogram to identify potential therapeutic targets for RC.

Prognosis-related genes were determined through analysis of clinical data from patients with RC in the GEO and TCGA datasets, leading to the development of a risk score model. The risk score demonstrated significant associations with immune cell infiltration, chemotherapy drug sensitivity, and multiple signaling pathways. Protein expression levels of the key genes in patients with RC were verified using the Human Protein Atlas database. Furthermore, immunohistochemical evaluation in animal models provided additional validation.

Molecular characteristics and mechanisms underlying radiotherapy response in RC were clarified through multiomics analysis. Five key genes were identified as potentially related to radiotherapy sensitivity in RC. These prognostic genes may serve as novel biomarkers and potential targets for diagnosis, prognostic evaluation, and clinical management of RC.