Additional file 2 of Unraveling the underlying pathogenic factors driving nonalcoholic steatohepatitis and hepatocellular carcinoma: an in-depth analysis of prognostically relevant gene signatures in hepatocellular carcinoma

Additional file 2: Fig. S1. HALMAKER pathway enrichment. HALMAKER pathway enrichment in NASH (up, GSE129516) and HCC (down, GSE142868). Fig. S2. Heatmap showing the contribution of signals. A-B Heatmap of incoming (A) and outgoing (B) signaling pathways in NASH dataset (GSE129516). The upper colored bar graph represents the cumulative signaling intensity of a cell group by totaling all signaling pathways represented in the heatmap. The right-hand grey bar graph indicates the overall signaling strength of a signaling pathway by adding up all cell groups exhibited in the heatmap. C-D Heatmap of incoming (C) and outgoing (D) signal reception pathways in HCC (GSE142868). The upper colored bar graph represents the cumulative signaling intensity of a cell group by totaling all signaling pathways represented in the heatmap. The right-hand grey bar graph indicates the overall signaling strength of a signaling pathway by adding up all cell groups exhibited in the heatmap. Fig. S3. Annotating macrophage subclusters and performing cytoTRANCE analysis. A-B Detecting specific markers for subgroups of macrophages. The dot size indicates the fraction of expressing cells, and the dots are colored based on average expression levels. NASH (GSE129516, A), HCC (GSE142868, B).C-D CytoTRACE predicts the ordering of macrophage subgroups based on their developmental potential, from the lowest differentiation ability to the highest. NASH (GSE129516, C), HCC (GSE142868, D). Fig. S4. Immune cell infiltration and GSEA enrichment analysis combined with bulk RNA-seq dataset. A, C Wilcoxon test of the immune cell infiltration differential analysis based on the ssGSEA algorithm in the NASH dataset (GSE129516, A) and HCC dataset (GSE142868, C). Significance is denoted as follows: ns indicates nonsignificance; * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. B, D Stacking plot depicting the proportion of immune cells based on the CIBERSORTX algorithm in the NASH dataset (GSE129516, B) and HCC dataset (GSE142868, D). E. Volcano map displaying differential gene expression using bulk RNA-seq dataset. F–H Gene set enrichment analysis (GSEA) based on single-cell gene sets and RNA-seq in the NASH dataset (GSE129516, F, G) and HCC dataset (GSE142868, H), NES > 0, activated; NES < 0, inhibited. Fig. S5. Dot plot showing the cluster-specific marker genes. A Cell marker detection of HCC single-cell dataset. The dot size indicates the fraction of expressing cells, and the dots are colored based on average expression levels. B Detection of cell markers for macrophage subsets. The dot size indicates the fraction of expressing cells, and dots are colored based on average expression levels. Fig. S6. Application of CellChat to calculated signal communication in HCC single cell dataset (GSE151530). A Heatmap shows the relative importance of each cell group based on the computed four network centrality measures of the MIF signaling network. B All the significant ligand-receptor pairs that contribute to the signal transmission from hepatocytes to other cell types. Dots represent the contribution of each receptor pair in signals emitted by hepatocytes toward various cells in HCC patients. Dot size indicates significance, while color shade represents the magnitude of contribution. Darker shades, particularly red, indicate a higher contribution, while lighter shades indicate a lower contribution. C-D Heatmap of outgoing (C) and incoming (D) signaling pathways in the HCC (Human) dataset. The upper colored bar graph represents the cumulative signaling intensity of a cell group by totaling all signaling pathways represented in the heatmap. The right-hand grey bar graph indicates the overall signaling strength of a signaling pathway by adding up all cell groups exhibited in the heatmap. Fig. S7. Uivariate and multivariate COX regression analysis, as well as clinical correlation and survival analysis of three independent prognostic genes in HCC patients. A-B Univariate (A) and multivariate (B) COX regression analysis of the signature and different clinical features. C-F Pathological stage (C), Sex (D), pathological grade (E), and Age (F), survival analysis of patients in the relevant high-low risk group G Survival curves related to YBX1, KPNA2, and MED8 expression levels in the high- and low-risk groups.