CTNNB1 mutation frequently occurred in HCC
A flow diagram summarizing the study selection process is shown in Fig. 1. Summarizing the study selection process, including CTNNB1 mutation frequency, differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), drug priority analysis, immune filtrating analysis, weighted gene co-expression network analysis (WGCNA), and integrative analysis.
As was showed in Fig. 2A, 2B, 2C, and 2E, mutations in the CTNNB1 gene are frequent genetic alterations in pan-cancer. Of them, CTNNB1 mutation was detected in 21% of Chinese HCC patients, and D32G/Y/V was a hot spot (Fig. 2C-2D). Findings from TCGA pan-cancer atlas showed that CTNNB1 mutation was screened in 26% of European and American crow, and S45P/F/Y was a hot spot (Fig. 2E-2F). The difference of mutation frequency and mutation site in different populations may provide new insights for patients with CTNNB1 gene mutation. And then, genome integrity (43%) and Wnt signaling pathway (37%) were significantly altered in HCC (Fig. 2A).
CTNNB1 and TP53, AXIN1 mutations are frequently mutually exclusive, while it had the synergistic phenomenon with OBSCN (Supplementary 2A-2B). Compared with the CTNNB1 mutations, patients with CTNNB1 wild-type carried more TP53, WNT pathway (AXIN1), BRCA1 associated protein 1(BAP1), and PI3K-mTOR (TSC2) (Supplementary 2C). Interestingly, there was no significant survival event difference between CTNNB1 mutation and non-mutation in in hepatocellular carcinoma (Supplementary 3).
The dysfunction of cell-cell adhesion and Wnt pathway was enriched in presence of CTNNB1 mutation
Principal component analysis for HCC vs. para-cancer tissues, HCC vs. paired para-cancer tissue, CTNNB1 mutation vs. non-mutation were showed in Fig. 3A-3C. A total of 4507 differentially expressed genes were selected; 3285 were up-regulated and 1222 were down-regulated in HCC relative to para-cancer tissue (Fig. 3D). A total of 4047 differentially expressed genes were selected; 2854 were up-regulated and 1193 were down-regulated in HCC relative to paired para-cancer tissue (Fig. 3E). A total of 2283 differentially expressed genes were selected; 500 were up-regulated and 2854 were down-regulated in CTNNB1 mutation relative to non-mutation (Fig. 3E).
Considering that the number of differential genes was too large, it was difficult to accurately make GO and pathway enrichment analysis directly. We intersect differential expression from three collection above, 181 co-genes were up-regulated and 191 co-genes were down-regulated (Fig. 3G and 3H). Functional enrichment analysis showed the dysfunction of cell-cell adhesion and Wnt pathway was enriched in presence of CTNNB1 mutation (Fig. 3G and 3H).
As was illustrated in Supplementary 4, hallmark_E2F_target, and hallmark_G2M_checkpoint was significantly enriched in HCC vs. para-cancer tissues and HCC vs. paired para-cancer tissue. Hallmark_oxidative_phosphorylation and hallmark_wnt_beta_catenin_signaling was enriched in CTNNB1 mutation vs. non-mutation.
Kinase protein OBSCN was significantly altered in Supplementary 5A, the differential expression of the top 30 kinase protein was showed in Supplementary 5B. 50 cancer hallmark pathway and Wnt pathway related gene set were illustrated in Supplementary 5C-5D.
Association between drug sensitivity and CTNNB1 mutation
15 kinds of small molecules associated with CTNNB1 mutation were found in the DREIMT database, and they mainly related to glucocorticoid receptor agonist, acetylcholine receptor antagonist/agonist, cyclooxygenase inhibitors, tubulin polymerization inhibitors, and protein synthesis inhibitors, which may be involved in the potential mechanism for the treatment of CTNNB1 mutation HCC (Fig. 4A).
Subsequently, RNAactDrug was used to predict anticancer drugs associated with CTNNB1 mutation (Fig. 4B-4D). T-test showed that drug sensitivity of Nutlin-3 and PHA-665752 remained significantly difference between CTNNB1 mutation and non-mutation (Fig. 4B). Pearson and Spearman correlation coefficient from CCLE (Fig. 4B)., celllminer (Fig. 4C), and GDSC (Fig. 4D) drug database showed that CTNNB1 expression was negatively correlated with PLX4720, Trametinib, 6-benzylthioinsone, and PD0325901, while CTNNB1 expression was positively correlated with L-685458, PD-0332991, and sb-682330-a. The findings above suggested that these anti-tumor drugs may become potential therapeutic drugs for CTNNB1 mutation HCC.
High genetic score and TMB in CTNNB1 mutation HCC
As was illustrated in Fig. 5, high tumor mutation burden (TMB), neoantigen loads, intra-tumoral heterogeneity, number of segments, and homologous recombination defects were elevated in the presence of CTNNB1 mutation HCC.
M2 macrophages was significant enriched in CTNNB1 mutation
Findings from Cibersort (Fig. 6A), CTNNB1 mutation had higher levels of M2 macrophages and mast resting cell, while CTNNB1 wildtype HCC tissues had higher levels of CD4 T resting memory cell. Figure 6B showed that macrophage was enriched in CTNNB1 mutation HCC. Furthermore, Fig. 6C suggested that M1 macrophage was enriched in CTNNB1 wildtype HCC. M2 macrophage cell was increased in the presence of CTNNB1 mutation HCC (Fig. 6D)
Collectively, our results suggested M2-type macrophages are significantly enriched in CTNNB1-mutated HCC.
Evaluation of the immunotherapy efficacy and CTNNB1 mutation
The results from IPS methods (Fig. 7A) suggested that decreased effector cells and increased suppressor cells was exhibited in the CTNNB1 mutation HCC, while CTNNB1 mutation HCC remained high immunophenoscore. As was showed in Fig. 7B-7C, CTNNB1 mutation had higher levels of M2 macrophages, while CTNNB1 wildtype HCC tissues had higher levels of TIDE score, CAF, PDCD1, LAG3, HAVCR2 TIGIT and CD8.
Gene module associated with CTNNB1 mutation and M2 macrophage cell
We constructed co-expression network to identify gene module associated with CTNNB1 mutation and M2 macrophage cell, with soft thresholds (power) equal 14 based on the scale-free topology fit indices (Fig. 8A). Then, 11 gene functional modules were identified and exhibited as a hierarchical clustering dendrogram (Fig. 8B). The heatmap of module-trait relationships also suggested that the different functional modules with associated with CTNNB1 mutation and M2 macrophage cell (Fig. 8C). Importantly, we identified two interesting gene modules, including MEmagenta (gene modules positively correlated to CTNNB1 mutation and M2), and MEbrown (gene modules negatively correlated to CTNNB1 mutation and M2).
Enrichment analysis and sub-hub-PPI for interesting gene module from WGCNA
As was showed in Fig. 9A and 9B, genes positively correlated to CTNNB1 mutation and M2 was involved in Wnt signaling pathway and fatty acid derivative biosynthetic process, which consist of Bossard P et al. findings[32], while genes negatively correlated to CTNNB1 mutation and M2 was enriched in leukocyte activation and inflammatory response.
We integrated differentially expressed genes and co-expression gene, Fig. 9C-9F showed that 21 genes and 3 gene was obtained. PPI network constructed by STRING database showed there are close association among RNF43, LGR5, BMP4, AXIN2, ASPSCR1, GLUL, TNFRSF19 (Fig. 9G), CCL2 and FCGR2B remained high connetity (Fig. 9H).
.