Through Integrated Bioinformatics Analysis to Explore the Prognostic Role of TRP Channel Genes in Cervical Cancer

Background Transient Receptor Potential (TRP) channels are hypothesized to be associated with cancer progression. This study aimed to develop a prognostic model for cervical cancer (CESC) utilizing genes related to TRGs. Methods The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) libraries were employed for determining the training and validation cohorts, respectively. Transcriptome profiles, clinical data, and copy-number variation (CNV) datasets have been obtained from people suffering from cervical squamous cell carcinoma (CESC). Lasso-Cox regression analysis was used to determine the -risk score based on predicting gene expression levels, and survival analysis was used to ascertain the overall difference in survival between the high- and low-risk groups. Single-cell sequencing RNA information from the TISCH database was analyzed using the Seurat software. The software suites GSVA, ClusterProfiler, and IOBR were utilized for functional phenotypic analysis. The patients were split into two groups using consensus clustering. The clinicopathological features were then compared, and an investigation of biological function was carried out. Applying the Kaplan-Meier curve along with the log-rank test, the predictive value of genes was ascertained. The immune situation was the focus of the ensuing inquiry. Additionally, we looked at the connections between the tumor microenvironment adjustment, gene functional enrichment analysis, and TRGs. Results Ten TRP channel genes (TRGs) were included in a predictive risk model. Patients classified into various risk groupings exhibited notable differences in molecular characteristics and clinical symptoms. In particular, the high-risk group had a dire outlook and a higher cancer mutation burden (TMB). Single-cell RNA sequencing (scRNA-seq) analysis results pointed out that high-risk and low-risk cell populations differed significantly in numerous variables. In order to deeper comprehend the molecular regulatory processes underpinning risk subtypes, our study established an aggressive endogenous RNA (ceRNA) protective network. When regarded as a whole, TRG-related gene targeting might serve as a potential therapy approach for cervical cancer (CC). Conclusion We have successfully established a high-precision prognostic model for predicting overall survival and treatment efficacy using TRP channel-related genes.