TRP-Related Gene Signatures Predict Survival and the Immune Microenvironment in Rectal Cancer: A Comprehensive Bioinformatics Study

Xiaojun Wang¹Jie Qiong Peng²Dong Song³Lijun Hou¹Qingshan Wang¹Yan Zhou⁴YANAN MA³Chen Qiu³Qinping Guo^5*Ganggang Wang^3*

• ¹Department of Gastroenterology and Hepatology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
• ²Department of Oncology, Shandong Cancer hospital and institute, Shandong first Medical university and Shandong Academy of Medical Sciences, Jinan, China
• ³Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
• ⁴Department of Clinical Laboratory Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
• ⁵General Surgery Department Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China

The pathogenesis of rectal cancer (RC) involves a variety of biological mechanisms; however, the prognostic significance of temperature-sensitive receptor (TRP) channels in RC patients remains unclear. This study aimed to explore the role of TRP-related genes in RC prognosis and their potential clinical implications. Patients and Methods: RNA-seq data for RC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. TRP scores were calculated for TCGA samples, and modular genes were identified via weighted gene coexpression network analysis (WGCNA). Differentially expressed genes (DEGs) between RC and normal samples were identified via the "limma" software package.TRP-related genes (DETRPs) were identified by intersecting DEGs with modular genes. Biomarkers were identified through univariate and multivariate Cox analyses, as well as least absolute shrinkage and selective operator (LASSO) regression. Prognostic models and nomograms have been developed on the basis of these biomarkers. Additionally, enrichment analysis, immune cell infiltration assessment, and targeted drug prediction were performed. Biomarker expression was further validated experimentally. Results: A total of 246 DETRPs were identified by overlapping 1,989 DEGs and 265 modular genes, which were significantly associated with metabolic pathways. Five biomarkers (BMP5, DHRS11, GLTP, NFE2L3, and TMCC3) were selected to construct a prognostic model and a nomogram based on risk score and age. The risk model demonstrated significant correlations with clinical characteristics. Immune cell infiltration analysis revealed distinct immune cell ratios between high-and low-risk patients, with TMCC3 showing a positive correlation with central memory CD8 T cells and DHRS11 exhibiting a negative correlation with type 2 T helper cells. Furthermore, several targeted drugs, including MK-2206, pazopanib, JNK inhibitor VIII, PLX4720, and NU-7441, were associated with risk scores. Conclusion: This study identified five TRP-related biomarkers associated with RC prognosis, providing novel insights into the role of TRP channels in RC development. These findings may contribute to a deeper understanding of RC pathogenesis and offer potential targets for personalized therapy.