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. 2021 Feb 16:7:599142.
doi: 10.3389/fmolb.2020.599142. eCollection 2020.

Identification of Tumor Mutation Burden and Immune Infiltrates in Hepatocellular Carcinoma Based on Multi-Omics Analysis

Lu Yin  1 , Liuzhi Zhou  2 , Rujun Xu  1
Affiliations

Identification of Tumor Mutation Burden and Immune Infiltrates in Hepatocellular Carcinoma Based on Multi-Omics Analysis

Lu Yin et al. Front Mol Biosci. .

Abstract

We aimed to explore the tumor mutational burden (TMB) and immune infiltration in HCC and investigate new biomarkers for immunotherapy. Transcriptome and gene mutation data were downloaded from the GDC portal, including 374 HCC samples and 50 matched normal samples. Furthermore, we divided the samples into high and low TMB groups, and analyzed the differential genes between them with GO, KEGG, and GSEA. Cibersort was used to assess the immune cell infiltration in the samples. Finally, univariate and multivariate Cox regression analyses were performed to identify differential genes related to TMB and immune infiltration, and a risk prediction model was constructed. We found 10 frequently mutated genes, including TP53, TTN, CTNNB1, MUC16, ALB, PCLO, MUC, APOB, RYR2, and ABCA. Pathway analysis indicated that these TMB-related differential genes were mainly enriched in PI3K-AKT. Cibersort analysis showed that memory B cells (p = 0.02), CD8+ T cells (p = 0.09), CD4+ memory activated T cells (p = 0.07), and neutrophils (p = 0.06) demonstrated a difference in immune infiltration between high and low TMB groups. On multivariate analysis, GABRA3 (p = 0.05), CECR7 (p < 0.001), TRIM16 (p = 0.003), and IL7R (p = 0.04) were associated with TMB and immune infiltration. The risk prediction model had an area under the curve (AUC) of 0.69, suggesting that patients with low risk had better survival outcomes. Our study demonstrated for the first time that CECR7, GABRA3, IL7R, and TRIM16L were associated with TMB and promoted antitumor immunity in HCC.

Keywords: biomarkers; hepatocellular carcinoma; immune infiltration; prognosis; tumor mutation burden.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Landscape of frequently gene mutation in HCC. (A–C) Statistical calculations of mutation types based on different categories, where missense mutation, SNP, C > T mutation were the most (D,E) Display of TMB in each HCC sample (F) The top 10 mutant genes in HCC, including TP53, TTN, CTNNB1, MUC16, ALB, PCLO, MUC, APOB, RYR2, ABCA. (G) Landscape of mutation information of each HCC sample in waterfall plot. Each column represents a sample (H) Co-expression of mutant genes in HCC. HCC, hepatocellular carcinoma; SNP, single nucleotide polymorphism. *p < 0.001, ˙p < 0.05.
FIGURE 2
FIGURE 2
Gene ontology and KEGG analysis of the gene expression of the two TMB groups. (A,B) GO enriched analysis of differential genes in BP, CC, MP. (C,D) KEGG analysis with these differential genes were enriched in PI3K-AKt, cytokine-cytokine receptor interaction and focal adhesion axis. TMB: tumor mutation burden; BP: biological process; CC, cellular component; MP, molecular function; KEGG, kyoto encyclopedia of genes and genomes.
FIGURE 3
FIGURE 3
Gene set enrichment analysis GSEA with high and low TMB groups. (A–C) The top3 pathway axis enriched in high TMB groups included proteasome, drug metabolism other enzymes, porphyrin and chlorophyll metabolism. (D–F) The top3 pathway axis enriched in low TMB groups were ECM receptor interaction, vascular smooth muscle contraction, and ether lipid metabolism.
FIGURE 4
FIGURE 4
Tumor-infiltrating immune cells in hepatocellular carcinoma. (A) The stacked bar graph showed the infiltration of 22 immune cells in each sample. Each color represented a type of immune cell. (B) The Wilcoxon rank-sum test displayed that memory B cells (p = 0.02), CD8+ T cells (p = 0.09), CD4+ memory activated T cells (p = 0.07) and neutrophils (p = 0.06) had a difference in high and low TMB groups.
FIGURE 5
FIGURE 5
Identification of significant immune genes for HCC prognostication. (A) The Venn diagram showed that a total of 51 differential immune genes were associated with tumor mutation burden and immune infiltration. Kaplan–Meier analysis revealed that down-expression of GABRA3, CECR7, TRIM16 and up-expression of IL7R were associated with better survival outcomes and low recurrence. (B) GABRA3 (p = 0.0017) (C) CECR7 (p = 0.022) (D) TRIM16L (p = 0.011) (E) IL7R (p = 0.003)
FIGURE 6
FIGURE 6
Analysis and evaluation of the risk prediction (diagnosis) models in HCC. (A) Kaplan-Meier analysis demonstrated that patients with higher risk showed worse survival rate (p = 0.002). (B) The AUC of ROC curve (AUC = 0.69) showed the predictive accuracy of TMB risk scores. AUC, area under curve; HCC, hepatocellular carcinoma; ROC, receiver operating characteristic.
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