改写In the present study we obtained the stemness index mRNAsi for HCC patients based on transcriptome data Then based on the mRNAsi scores we created a unique prognostic signature that can divided pati

Based on transcriptome data, we obtained the stemness index (mRNAsi) for HCC patients and developed a unique prognostic signature based on mRNAsi scores that could classify patients into three stemness subtypes. These subtypes had distinct clinical features, functional enrichment, and tumor mutation burdens, and we validated their prognostic prediction capacity using data from the Gene Expression Omnibus (GEO) database. We also conducted integrated bioinformatic studies to evaluate the signature's correlation with clinicopathological characteristics, microenvironment features, genetic patterns, and therapeutic response. Using machine learning algorithms, we developed a stemness subtype classifier based on the expression of nine genes that can be used as a molecular approach to assess the stemness status of patients and predict their response to chemotherapy and immunotherapy.