Dataset Information

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

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.

SUBMITTER: Yin L

PROVIDER: S-EPMC7928364 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Yin Lu L Zhou Liuzhi L Xu Rujun R

Frontiers in molecular biosciences 20210216

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 ...[more]

PMID: 33681288

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Project description:BackgroundHepatocellular carcinoma (HCC) was the sixth common malignancies characteristic with highly aggressive in the world. It was well established that tumor mutation burden (TMB) act as indicator of immunotherapeutic responsiveness in various tumors. However, the role of TMB in tumor immune microenvironment (TIME) is still obscure.MethodThe mutation data was analyzed by employing "maftools" package. Weighted gene co-expression network analysis (WGCNA) was implemented to determine candidate module and significant genes correlated with TMB value. Differential analysis was performed between different level of TMB subgroups employing R package "limma". Gene ontology (GO) enrichment analysis was implemented with "clusterProfiler", "enrichplot" and "ggplot2" packages. Then risk score signature was developed by systematical bioinformatics analyses. K-M survival curves and receiver operating characteristic (ROC) plot were further analyzed for prognostic validity. To depict comprehensive context of TIME, XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS algorithm were employed. Additionally, the potential role of risk score on immune checkpoint blockade (ICB) immunotherapy was further explored. The quantitative real-time polymerase chain reaction was performed to detect expression of HTRA3.ResultsTMB value was positively correlated with older age, male gender and early T status. A total of 75 intersection genes between TMB-related genes and differentially expressed genes (DEGs) were screened and enriched in extracellular matrix-relevant pathways. Risk score based on three hub genes significantly affected overall survival (OS) time, infiltration of immune cells, and ICB-related hub targets. The prognostic performance of risks score was validated in the external testing group. Risk-clinical nomogram was constructed for clinical application. HTRA3 was demonstrated to be a prognostic factor in HCC in further exploration. Finally, mutation of TP53 was correlated with risk score and do not interfere with risk score-based prognostic prediction.ConclusionCollectively, a comprehensive analysis of TMB might provide novel insights into mutation-driven mechanism of tumorigenesis further contribute to tailored immunotherapy and prognosis prediction of HCC.

Project description:BackgroundWhether tumor mutation burden (TMB) correlated with improved survival outcomes or promotion of immunotherapies remained controversy in various malignancies. We aimed to investigate the prognosis of TMB and the potential association with immune infiltrates in clear cell renal cell carcinoma (ccRCC).MethodsWe downloaded the somatic mutation data of 336 ccRCC patients from the Cancer Genome Atlas (TCGA) database, and analyzed the mutation profiles with "maftools" package. TMB was calculated and we classified the samples into high-TMB and low-TMB group. Differential analysis was conducted to compare the expression profiles between two groups using "limma" package, and we identified the 9 hub TMB-related signature from batch survival analysis. Gene ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were performed to screen significantly enriched pathways between two groups. Based on the TIMER database, we further assessed the relationships of the mutants of 9 TMB-related signature with immune infiltration levels in ccRCC. Besides, we utilized the "CIBERSORT" package to estimate the abundance of 22 immune fractions between low- and high-TMB groups, and the significant difference were determined by Wilcoxon rank-sum test. Furthermore, Cox regression model combined with survival analysis were used to evaluate the prognostic value of immune cells. Last, we constructed a Tumor Mutation Burden Prognostic Index (TMBPI) from multivariate Cox results and Receiver Operating Characteristic (ROC) curve was drawn to assess the predictive accuracy.ResultsSingle nucleotide polymorphism (SNP) occurred more frequently than insertion or deletion, and C>T was the most common of SNV in ccRCC. Higher TMB levels conferred poor survival outcomes, associated with higher tumor grades and advanced pathological stages. A total of 1,265 differentially expressed genes were obtained and top 19 immune-related genes were identified in Venn diagram. GSEA revealed that patients in higher TMB groups correlated with MAPK signaling pathway, Wnt signaling pathway and pathway in cancers. Moreover, we identified 9 hub TMB-related immune genes related with survival and mutants of 9 signature were associated with lower immune infiltrates. In addition, infiltration levels of CD8+ T cell, CD4+ memory resting T cell, M1 and M2 macrophages, as well as dendritic resting cells in high-TMB group were lower than that in low-TMB group, especially the level of CD8+ T cell and macrophage correlated negatively with prognosis of ccRCC. Last, the TMBPI was constructed and the AUC of ROC curve was 0.666.ConclusionsHigher TMB correlated with poor survival outcomes and might inhibit the immune infiltrates in ccRCC. The mutants of 9 hub TMB-related immune signature conferred lower immune cells infiltration which deserved further validation.

Project description:BackgroundTumor mutation burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in cancer. Systematic identification of molecular features correlated with TMB is significant, although such investigation remains insufficient.MethodsWe analyzed associations of somatic mutations, pathways, protein expression, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), competing endogenous RNA (ceRNA) antitumor immune signatures, and clinical features with TMB in various cancers using multi-omics datasets from The Cancer Genome Atlas (TCGA) program and datasets for cancer cohorts receiving the immune checkpoint blockade therapy.ResultsAmong the 32 TCGA cancer types, melanoma harbored the highest percentage of high-TMB (≥ 10/Mb) cancers (49.4%), followed by lung adenocarcinoma (36.9%) and lung squamous cell carcinoma (28.1%). Three hundred seventy-six genes had significant correlations of their mutations with increased TMB in various cancers, including 11 genes (ARID1A, ARID1B, BRIP1, NOTCH2, NOTCH4, EPHA5, ROS1, FAT1, SPEN, NSD1,and PTPRT) with the characteristic of their mutations associated with a favorable response to immunotherapy. Based on the mutation profiles in three genes (ROS1, SPEN, and PTPRT), we defined the TMB prognostic score that could predict cancer survival prognosis in the immunotherapy setting but not in the non-immunotherapy setting. It suggests that the TMB prognostic score's ability to predict cancer prognosis is associated with the positive correlation between immunotherapy response and TMB. Nine cancer-associated pathways correlated positively with TMB in various cancers, including nucleotide excision repair, DNA replication, homologous recombination, base excision repair, mismatch repair, cell cycle, spliceosome, proteasome, and RNA degradation. In contrast, seven pathways correlated inversely with TMB in multiple cancers, including Wnt, Hedgehog, PI3K-AKT, MAPK, neurotrophin, axon guidance, and pathways in cancer. High-TMB cancers displayed higher levels of antitumor immune signatures and PD-L1 expression than low-TMB cancers in diverse cancers. The association between TMB and survival prognosis was positive in bladder, gastric, and endometrial cancers and negative in liver and head and neck cancers. TMB also showed significant associations with age, gender, height, weight, smoking, and race in certain cohorts.ConclusionsThe molecular and clinical features significantly associated with TMB could be valuable predictors for TMB and immunotherapy response and therefore have potential clinical values for cancer management.

Project description:Background:Hydroxysteroid 17-Beta Dehydrogenase 6 (HSD17B6), a key protein involved in synthetizing dihydrotestosterone, is abundant in the liver. Previous studies have suggested a role for dihydrotestosterone in modulating progress of various malignancies, and HSD17B6 dysfunction was associated with lung cancer and prostate cancer. However, little is known about the detailed role of HSD17B6 in hepatocellular carcinoma (HCC). Methods:Clinical implication and survival data related to HSD17B6 expression in patients with HCC were obtained through TCGA, ICGC, ONCOMINE, GEO and HPA databases. Survival analysis plots were drawn with Kaplan-Meier Plotter. The ChIP-seq data were obtained from Cistrome DB. Protein-Protein Interaction and gene functional enrichment analyses were performed in STRING database. The correlations between HSD17B6 and tumor immune infiltrates was investigated via TIMER and xCell. The proliferation, migration and invasion of liver cancer cells transfected with HSD17B6 were evaluated by the CCK8 assay, wound healing test and transwell assay respectively. Expression of HSD17B6, TGFB1 and PD-L1 were assessed by quantitative RT-PCR. Results:HSD17B6 expression was lower in HCC compared to normal liver and correlated with tumor stage and grade. Lower expression of HSD17B6 was associated with worse OS, PFS, RFS and DSS in HCC patients. HNF4A bound to enhancer and promoter regions of HSD17B6 gene, activating its transcription, and DNA methylation of HSD17B6 promoter negatively controlled the expression. HSD17B6 and its interaction partners were involved in androgen metabolism and biosynthesis in liver. HSD17B6 inhibited tumor cell proliferation, migration and invasion in liver cancer cells and low expression of HSD17B6 correlated with high immune cells infiltration, relative reduction of immune responses and multiple immune checkpoint genes expression in HCC, probably by regulating the expression of TGFB1. Conclusions:This study indicate that HSD17B6 could be a new biomarker for the prognosis of HCC and an important negative regulator of immune responses in HCC.

Project description:Introduction: Tumor mutation burden (TMB) is an emerging biomarker for immunotherapy of hepatocellular carcinoma (HCC), but its value for clinical application has not been fully revealed.Materials and methods: We used the Wilcox test to identify the differentially expressed immune-related genes (DEIRGs) in groups with high and low TMB as well as screened the immune gene pairs related to prognosis using univariate Cox regression analysis. A LASSO Cox regression prognostic model was developed by combining The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) with the International Cancer Genome Consortium (ICGC) LIRI-JP cohort, and internal (TCGA, ICGC) and external (GSE14520) validation analyses were conducted on the predictive value of the model. We also explored the relationship between the prognostic model and tumor microenvironment via the ESTIMATE algorithm and performed clinical correlation analysis by the chi-square test, revealing its underlying molecular mechanism with the help of Gene Set Enrichment Analysis (GSEA).Results: The prognostic model consisting of 15 immune gene pairs showed high predictive value for short- and long-term survival of HCC in three independent cohorts. Based on univariate multivariate Cox regression analysis, the prognostic model could be used to independently predict the prognosis in each independent cohort. The immune score, stromal score, and estimated score values were lower in the high-risk group than in the low-risk group. As shown by the chi-square test, the prognostic model exhibited an obvious correlation with the tumor stage [American Joint Committee on Cancer tumor-node-metastasis (AJCC-TNM) (p < 0.001), Barcelona Clinic Liver Cancer (BCLC) (p = 0.003)], histopathological grade (p = 0.033), vascular invasion (p = 0.009), maximum tumor diameter (p = 0.013), and background of liver cirrhosis (p < 0.001). GSEA revealed that the high-risk group had an enrichment of many oncology features, including the cell cycle, mismatch repair, DNA replication, RNA degradation, etc.Conclusion: Our research developed and validated a reliable prognostic model associated with TMB for HCC, which may help to further enrich the therapeutic targets of HCC.

Dataset Information

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

Publications

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

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