Research Paper Volume 13, Issue 9 pp 12865—12895

Integrative analysis identifies key mRNA biomarkers for diagnosis, prognosis, and therapeutic targets of HCV-associated hepatocellular carcinoma

Building a WGCNA network to identify the most significant module correlated with survival status. (A) Sample clustering tree with clinical traits. (B) Heatmap showing the eigengene networks according to the topological overlap matrix (TOM) based dissimilarity. (C) Gene clustering dendrogram, with each color corresponding to an individual gene module. (D) Pearson correlation analysis between module eigengenes and clinical traits. (E) scatter plot showing the gene significance (GS) vs module membership (MM) for the turquoise module. WGCNA, Weight Gene Co-expression Network Analysis.

Figure 3. Building a WGCNA network to identify the most significant module correlated with survival status. (A) Sample clustering tree with clinical traits. (B) Heatmap showing the eigengene networks according to the topological overlap matrix (TOM) based dissimilarity. (C) Gene clustering dendrogram, with each color corresponding to an individual gene module. (D) Pearson correlation analysis between module eigengenes and clinical traits. (E) scatter plot showing the gene significance (GS) vs module membership (MM) for the turquoise module. WGCNA, Weight Gene Co-expression Network Analysis.