改写In order to investigate the underlying mechanism of these two mRNAsi subgroups and elaborate the function of mRNAsi phenotype model we development a novel stemness-based classification of HCC patien

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To investigate the mechanism underlying the two mRNAsi subgroups and elaborate on the function of the mRNAsi phenotype model, we developed a novel stemness-based classification of HCC patients using multiple bioinformatic analyses. Firstly, we conducted differential expression analysis to identify differentially expressed genes (DEGs) between tumor and adjacent tissue in the TCGA-LIHC and GSE14520 cohorts (with ∣Fold Change∣> 1.2 and P < 0.05). We then performed correlation analysis between all DEGs and mRNAsi score (with person correlation coefficient, ∣R∣> 0.3 and P < 0.05). This resulted in the identification of 19 mRNAsi phenotype-related DEGs, including 13 genes that were increased (Risk, HR > 1) and six genes that were decreased (Protect, HR < 1) in the mRNAsi-high group (Figure 2A).

To account for the heterogeneity of stemness characteristics, we used unsupervised consensus clustering to construct a specific stemness-based classification of HCC patients based on the expression patterns of the 19 mRNAsi phenotype-based DEGs. This resulted in the categorization of patients into three subgroups: the stemness subtype C1 (109 cases, 29.9%), the stemness subtype C2 (138 cases, 37.8%), and the stemness subtype C3 (118 cases, 32.3%). As shown in Figure 2B, 2C, and 2D, patients in the three subgroups exhibited distinct expression patterns of the 19 mRNAsi phenotype-related DEGs.

Furthermore, Kaplan-Meier curve analysis showed that patients in stemness subtype C3 had a significantly worse prognosis compared to those in stemness subtypes C1 and C2 (Figure 2E). These findings suggest that the novel stemness-based classification of HCC patients is a valuable tool for investigating the mechanism underlying mRNAsi subgroups and for elaborating on the function of the mRNAsi phenotype model.