Correlations between sternness indices for hepatocellular carcinoma, clinical characteristics, and prognosis

Recent studies have shown that cancer stem cells (CSCs) are involved in the occurrence and development of hepatocellular carcinoma (HCC). However, potential mechanisms for this have not yet been elucidated. We constructed a model based on the Progenitor Cell Biology Consortium database to generate stemness indices. We then utilized RNA-seq data and clinical information from the Cancer Genome Atlas (CGA) and International Cancer Genome Consortium (ICGC) for model predictions and verification. An mRNA gene expression-based stemness index (mRNAsi) and a DNA methylation-based stemness index (mDNAsi) were both calculated through one-class logistic regression. By applying univariate Cox regression analysis, we found that the mRNAsi and the mDNAsi correlated significantly with overall survival. Functional prediction analyses were used to characterize implicated genes and their degree of involvement as network hubs through protein-protein interaction analysis, and Spearman's rank correlation coefficient test was used to assess the relationship between hub genes and indices for stemness. The mRNAsi values for CGA and ICGC carcinoma samples correlated significantly with negative clinical characteristics and overall survival, whereas gene and protein-protein interaction analyses revealed that SNAP25, KPT19, GABBR1, and EPCAM were negatively associated with clinical mDNAsi scores. Collectively, the data suggest that our new stemness model based on related genes may predict patient prognoses.