Dataset Information

Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data.

ABSTRACT: In order to explore the prognosis of tumor mutation burden (TMB) and the relationship with tumor infiltrating immune cells in hepatocellular carcinoma (HCC), we downloaded somatic mutation data and transcriptome profiles of 376 HCC patients from The Cancer Genome Atlas (TCGA) cohort. We divided the samples into high-TMB and low-TMB groups. A higher TMB level indicated improved overall survival (OS) and was associated with early pathological stages. One hundred and nine differentially expressed genes (DEGs) were identified in HCC. Moreover, based on four hub TMB-related signatures, we constructed a TMB Prognostic model (TMBPM) that possessed good predictive value with area under curve (AUC) of 0.701. HCC patients with higher TMBPM scores showed worse OS outcomes (p < 0.0001). Moreover, DCs subsets not only revealed higher infiltrating abundance in the high-TMB group, but also correlated with worse OS and hazard risk for high-TMB patients in HCC. Meanwhile, CD8+ T cells and B cells were associated with improved survival outcomes. In sum, high TMB indicates good prognosis for HCC and promotes HCC immune infiltration. Hence, DCs and the four hub TMB-related signatures can be used for predicting the prognosis in HCC as supplements to TMB.

SUBMITTER: Liu S

PROVIDER: S-EPMC8109113 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data.

Liu Shaoqing S Tang Qianjie Q Huang Jianwen J Zhan Meixiao M Zhao Wei W Yang Xiangyu X Li Yong Y Qiu Lige L Zhang Fujun F Lu Ligong L He Xu X

Aging 20210404 8

In order to explore the prognosis of tumor mutation burden (TMB) and the relationship with tumor infiltrating immune cells in hepatocellular carcinoma (HCC), we downloaded somatic mutation data and transcriptome profiles of 376 HCC patients from The Cancer Genome Atlas (TCGA) cohort. We divided the samples into high-TMB and low-TMB groups. A higher TMB level indicated improved overall survival (OS) and was associated with early pathological stages. One hundred and nine differentially expressed g ...[more]

PMID: 33820866

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Project description:IntroductionTumor 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 methodsWe 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).ResultsThe 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.ConclusionOur research developed and validated a reliable prognostic model associated with TMB for HCC, which may help to further enrich the therapeutic targets of HCC.

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:IntroductionFemale breast cancer is the most common malignancy worldwide, with a high disease burden. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity. Dysregulation of the degradome may disrupt cellular homeostasis and trigger carcinogenesis. Thus we attempted to understand the prognostic role of degradome in breast cancer by means of establishing a prognostic signature based on degradome-related genes (DRGs) and assessed its clinical utility in multiple dimensions.MethodsA total of 625 DRGs were obtained for analysis. Transcriptome data and clinical information of patients with breast cancer from TCGA-BRCA, METABRIC and GSE96058 were collected. NetworkAnalyst and cBioPortal were also utilized for analysis. LASSO regression analysis was employed to construct the degradome signature. Investigations of the degradome signature concerning clinical association, functional characterization, mutation landscape, immune infiltration, immune checkpoint expression and drug priority were orchestrated. Cell phenotype assays including colony formation, CCK8, transwell and wound healing were conducted in MCF-7 and MDA-MB-435S breast cancer cell lines, respectively.ResultsA 10-gene signature was developed and verified as an independent prognostic predictor combined with other clinicopathological parameters in breast cancer. The prognostic nomogram based on risk score (calculated based on the degradome signature) showed favourable capability in survival prediction and advantage in clinical benefit. High risk scores were associated with a higher degree of clinicopathological events (T4 stage and HER2-positive) and mutation frequency. Regulation of toll-like receptors and several cell cycle promoting activities were upregulated in the high-risk group. PIK3CA and TP53 mutations were dominant in the low- and high-risk groups, respectively. A significantly positive correlation was observed between the risk score and tumor mutation burden. The infiltration levels of immune cells and the expressions of immune checkpoints were significantly influenced by the risk score. Additionally, the degradome signature adequately predicted the survival of patients undergoing endocrinotherapy or radiotherapy. Patients in the low-risk group may achieve complete response after the first round of chemotherapy with cyclophosphamide and docetaxel, whereas patients in the high-risk group may benefit from 5-flfluorouracil. Several regulators of the PI3K/AKT/mTOR signaling pathway and the CDK family/PARP family were identified as potential molecular targets in the low- and high-risk groups, respectively. In vitro experiments further revealed that the knockdown of ABHD12 and USP41 significantly inhibit the proliferation, invasion and migration of breast cancer cells.ConclusionMultidimensional evaluation verified the clinical utility of the degradome signature in predicting prognosis, risk stratification and guiding treatment for patients with breast cancer.

Dataset Information

Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data.

Publications

Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data.

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