Macrophage-Related Genomic Signatures Predict HCC Prognosis and Therapy Response

Figure captions:

Figure (1A): Distribution of nFeature_RNA before filtering. Figure (1B): Distribution of nFeature_RNA after filtering. Figure (1C): Percent mitochondrial content before filtering. Figure (1D): Percent mitochondrial content after filtering. Figure (1E): Correlation between nCount_RNA and nFeature_RNA. Figure (1F): Total gene count per cell. Figure (1G): Clustering of cells into 7 cell types. Figure (1H): Distribution of cell numbers across clusters. Figure (1I): Top 5 upregulated and downregulated genes for each cell type.

Figure (2A): Hallmark pathway scores across cell types: Visualization of pathway scores highlighting macrophage-related signatures. Figure (2B): top 20 genes highly expressed in macrophages: Macrophage-specific markers and their potential functional roles.

Figure (3A): Univariate Cox regression and correlation analysis of macrophage-related genes. Figure (3B): Kaplan-Meier survival analysis of 12 OS-associated genes. Figure (3C): Unsupervised clustering of HCC patients based on macrophage-related gene expression. Figure (3D): Kaplan-Meier survival curves for the four HCC clusters. Figure (3E-F): Clinical feature distribution and gene expression heatmap among the clusters.

Figure (4A): PCA plot showing distinct separation of the four clusters. Figure (4B): ESTIMATE-based stromal and immune scores among clusters. Figure (4C): ssGSEA confirmation of immune infiltration patterns.

Figure (5A): Selection of 25 prognostic genes using univariate Cox regression from the initial 855 DEGs. Figure (5B): Kaplan-Meier survival curves comparing high vs. low PCA score groups. Figure (5C): Correlation between PCA score and immune cell infiltration levels. Figure (5D): Comparison of PCA scores between alive and deceased patients. Figure (5E): Relationship between PCA score and tumor stage.

Figure (6A): Correlation between PCA score and immune-related gene expression. Figure (6B): GSVA results showing pathway activation (KRAS signaling, coagulation) by PCA score. Figure (6C): PDCD1 (PD-1) and CTLA4 were significantly upregulated in the low PCA score group. Figure (6D): Somatic mutation landscapes in high vs. low PCA score groups (CTNNB1, TP53). Figure (6E): Genes with significant mutation frequency differences between groups. Figure (6F): Chemotherapeutic drug sensitivity predictions for high vs. low PCA score groups.

Sup-Figure (1A): GSVA-based pathway activity comparisons among clusters (Hallmark, KEGG, Reactome). Sup-Figure (1B–C): Detailed GSVA scores for KEGG and Reactome pathways. Sup-Figure(1D): DEGs and their GO/KEGG enrichment.

Sup-Figure (2A–C): Negative correlation of PCA score with cell stemness, TMB, and MSI. Sup-Figure(2D): IPS score differences indicating immunotherapy responsiveness. Sup-Figure(2E): Validation of PCA score as predictor in GSE176307 and Riaz2017 cohorts.

Sup-Figure (3): The curve depicts the relative change in the area under the consensus CDF curve across different k values (ranging from 2 to 9). The sharp decrease in delta area when k increases from 2 to 4, followed by a plateau for k ≥ 4, indicates that k = 4 is the optimal number of clusters (balancing stability and parsimony).