Fig. 1
From: Fibrinogen alpha and beta chains as non-invasive predictors of hepatocellular carcinoma progression
Identification of FGA, FGB, and FGG as potential diagnostic biomarkers for HCC using public-omics data. (A) Analysis of differentially expressed genes (DEGs) for HCC diagnosis using the multistage liver cancer datasets GSE89377 and GSE114564. (B) The expression levels of FGA, FGB, and FGG were analyzed in multiple stages of liver disease including non-cancerous liver (NL; normal liver, CH; chronic hepatitis, LC; liver cirrhosis, DN; dysplastic nodule) and tumor (eHCC; early staged HCC, aHCC; advanced HCC) tissues using the GSE89377 and GSE114564 datasets. (C) Heatmap and hierarchical clustering analysis of FGA, FGB, and FGG according to the stage of liver disease. (D) Investigation of the expression patterns of FGA, FGB, and FGG from normal liver to HCC using the integrated liver expression atlas, GepLiver DB, and bulk RNA-seq data. Left: Heatmap of FGA, FGB, and FGG expression (z-score) in a total of 13 data sets from GepLiver DB. Right: FGA, FGB, and FGG expression across tissues with different phenotypes. (E) Expression of FGA, FGB, and FGG in non-malignant and malignant liver cell analyzed using spatial transcriptomics data. Statistically significant differences were determined using one-way ANOVA with Tukey’s multiple comparisons test; * P < 0.05, ** P < 0.01, *** P < 0.001. Data are shown as mean ± SD.
