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Fig. 5 | Journal of Big Data

Fig. 5

From: Integration of transcriptomic analysis and multiple machine learning approaches identifies NAFLD progression-specific hub genes to reveal distinct genomic patterns and actionable targets

Fig. 5

CTNNB1/COL1A2 axis correlates with fibrosis severity during NAFLD-HCC progression. (a) Five mutational signatures were identified using TCGA-HCC WES data and NMF algorithm. The three major mutational signatures were annotated with “Defective DNA mismatch repair”, “Aflatoxin exposure”, and “Aristolochic acid exposure”. (b) The abundance of each mutational signature in TCGA-HCC was shown in a pie chart. (c) 184 HCC samples with history of alcohol consumption, hepatitis or NAFLD were extracted, and the distribution of each mutational signature was depicted in a stacked barplot. (d) NAFLD-HCC is characterized with high COL1A2 expression. (e) Oncoplot demonstrated that CTNNB1 acts as the most frequently mutated gene in the COL1A2-low cohort, with the mutation frequency up to 48% (left panel). In contrast, CTNNB1 is observed rarely mutated in the COL1A2-high samples (right panel). (f) In the integrated analyses of TCGA-HCC, MSK-HCC and INSERM-HCC cohorts, the gene-pair of TP53 and CTNNB1 is shown significantly mutually exclusive. (g) COL1A2 mRNA expression is significantly elevated in CTNNB1-wild type HCC samples compared to CTNNB1-mutated ones. (h) Among nine representative oncogenic pathways, the WNT signaling pathway is the most frequently affected one in the COL1A2-low cohort. (i) A functional network showed the top five important pathways in CTNNB1-WT/COL1A2-high HCC samples were termed “vasculature development”, “chemotaxis”, “ECM organization”, “positive regulation of locomotion”, and “positive regulation of cell adhesion”

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