Fig. 1: Multi-omics identification and analysis for candidate biomarkers.

A Biomarker screening flowchart. B Volcano plots depicting differentially expressed genes between HCC and ANT tissues in the TCGA-LIHC (left) and ICGC-LIRI-JP (right) datasets. Red dots indicate genes with statistically significant upregulation (FC ≥ 2, FDR < 0.05); gray dots indicate genes without statistically significant differential expression (FDR ≥ 0.05). C Venn diagram showing the intersection of upregulated biomarkers in the three datasets. D Bar charts indicating the diagnostic performance (AUC) of biomarkers (left); Pie charts illustrating the expression status of candidate biomarkers in the datasets (right): red indicates upregulation, blue indicates downregulation, and gray indicates no significant difference. The expression levels of biomarkers in HCC and control tissue were compared using the Wilcoxon rank sum test with Benjamini–Hochberg multiple correction analysis to determine statistical significance. E Expression of candidate biomarkers at the single-cell level. UMAP plot showing cell type identification for 47670 high-quality single cells (upper left); Inset comparing expression levels of candidate biomarkers between ANT and HCC tissues (lower right). F Heatmap of NASH-related HCC progression in a mouse HCC model. ICGC International Cancer Genome Consortium, TCGA The Cancer Genome Atlas, ANT adjacent normal tissues, AUC area under the receiver operating characteristic curve, FC fold change, FDR false discovery rate, FAHNU the First Affiliated Hospital of Nanchang University, HCC hepatocellular carcinoma, NASH non-alcoholic steatohepatitis, UMAP uniform manifold approximation and projection.