Hepatocellular carcinoma (HCC) is a highly lethal malignancy with high invasiveness and metastasis. Despite progress in its treatment, the high mortality rate persists due to poor prognosis. In this study, we aimed to develop a prognostic model for HCC using platelet-related genes. We downloaded and preprocessed data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Through differential gene expression analysis, univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) machine learning, and multivariate Cox regression, we constructed a prognostic model consisting of six genes (KIF18A, HRG, TUBA4B, MAFF, SELP, and IGF1). The survival analysis and ROC curve evaluation conducted on both the training and validation sets showed excellent predictive performance of the model. In addition, the model was also associated with the malignancy and risk of metastasis of tumors. Additionally, analysis of half maximal inhibitory concentration (IC50) values between high- and low-risk groups for various drugs showed significant differences, suggesting potential differences in predicted drug sensitivity between risk groups. We identified potential stem-like cell subpopulations in HCC cells, which are mostly in the late stage of malignant cell differentiation. In conclusion, we successfully constructed a novel platelet-related prognostic model for HCC.
Pan et al. (Mon,) studied this question.