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Updated: Jan 17, 2026
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
Identification of hub genes related to radiosensitivity and prognosis in rectal cancer
Hui Yang1, Yin Liu2, Mengdi Hao2
1Department of Radio-Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Background:
Radiotherapy is one of the main treatment methods for rectal cancer (RC). However, radioresistance often leads to treatment failure and poor prognosis. This study aimed to explore the core molecules associated with radiosensitivity and prognosis of RC.
Methods:
GSE133057 containing RC radiosensitivity information was downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between the complete response (CR) group and the incomplete response (iCR) group were identified. Immune genes were obtained from the ImmPort database. Radiosensitivity-related immune genes (RRIGs) were obtained by intersecting DEGs and the immune genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses 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 sets at a ratio of 7:3. Prognostic genes were selected by univariate and multivariate Cox analyses, and a risk model and nomogram were built subsequently. External dataset validation was performed. The relationship between the risk 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 cytokine-cytokine receptor interaction, TNF signaling pathway, cAMP signaling pathway, Toll-like receptor (TLR) signaling pathway, and so on. BMP2, COLEC10, MASP2, and GCGR were screened as prognostic genes after Cox regression analysis. The risk score model demonstrated good performance in predicting prognosis as proved by the receiver operating characteristic (ROC) curves. Multivariate 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 high and low risk groups, and these four genes were associated with different immune cell infiltration.
Conclusions:
We identified four key genes: BMP2, COLEC10, MASP2, and GCGR, which play significant roles in the radiosensitivity, immune microenvironment, and prognosis of RC.
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