Purpose: TRP channels have been implicated in cancer progression. Our study seeks to establish a prognostic model for hepatocellular carcinoma (HCC) by utilizing genes related to TRP channels.
Methods: We used the TCGA and ICGC databases as training and validation cohorts, respectively. We calculated the risk scores using Lasso-Cox regression analysis based on the expression levels of prognostic genes and performed survival analysis to compare overall survival between high- and low-risk groups. Then we compared the clinicopathologic characteristics and conducted biological functional analysis. We also explored immune cell infiltration and compared the drug sensitivity.
Results: Using bioinformatics algorithms, we identified 11 TRP-related genes and calculated the risk scores. Patients in the high-risk group demonstrated worse overall survival, as well as more advanced T stage and pathologic stage. The risk score showed a significant association with the cell cycle. The high-risk group had more ICI and RTK targets with elevated expression and showed better therapeutic effect to chemotherapy including 5-fluorouracil, camptothecin, docetaxel, doxorubicin, gemcitabine, and paclitaxel. Overall, an individualized nomogram was constructed by integrating the risk score and requisite clinicopathologic parameters to predict the overall survival of HCC patients.
Conclusions: We successfully established a highly accurate prognostic model for predicting overall survival and therapeutic effects using TRP channel-related genes.
Keywords: Bioinformatic algorithm; Hepatocellular carcinoma; Prognosis prediction; Therapeutic effects; Transient receptor potential.
© 2023. The Author(s).