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Development and validation of a CTNNB1-associated metabolic prognostic model for hepatocellular carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is a heterogeneous malignancy closely related to metabolic reprogramming. We investigated how CTNNB1 mutation regulates the HCC metabolic phenotype and thus affects the prognosis of HCC. We obtained the mRNA expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA), the International Cancer Genomics Consortium (ICGC) and the Gene Expression Omnibus database (GSE14520 and GSE116174). We conducted gene set enrichment analysis on HCC patients with and without mutant CTNNB1 through TCGA dataset. The Kaplan-Meier analysis and univariate Cox regression analysis assisted in screening metabolic genes related to prognosis, and the prognosis model was constructed using the Lasso and multivariate Cox regression analysis. The prognostic model showed good prediction performance in both the training cohort (TCGA) and the validation cohorts (ICGC, GSE14520, GSE116174), and the high-risk group presented obviously poorer overall survival compared with low-risk group. Cox regression analysis indicated that the risk score can be used as an independent predictor for the overall survival of HCC. The immune infiltration in different risk groups was also evaluated in this study to explore underlying mechanisms. This study is also the first to describe an metabolic prognostic model associated with CTNNB1 mutations and could be implemented for determining the prognoses of individual patients in clinical practice.

SUBMITTER: Huo J

PROVIDER: S-EPMC7812275 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Development and validation of a CTNNB1-associated metabolic prognostic model for hepatocellular carcinoma.

Huo Junyu J Wu Liqun L Zang Yunjin Y

Journal of cellular and molecular medicine 20201209 2

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy closely related to metabolic reprogramming. We investigated how CTNNB1 mutation regulates the HCC metabolic phenotype and thus affects the prognosis of HCC. We obtained the mRNA expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA), the International Cancer Genomics Consortium (ICGC) and the Gene Expression Omnibus database (GSE14520 and GSE116174). We conducted gene set enrichment analysis on HCC patient ...[more]

PMID: 33300278

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Project description:BACKGROUND:CpG island methylator phenotype (CIMP), a common biological phenomenon characterized by a subset of concurrently methylated genes, can have an influence on the progression of multiple cancers. However, the potential mechanism of CIMP in hepatocarcinogenesis and its clinical relevance remains only partially understood. METHODS:We used a methylation array from the cancer genome atlas (TCGA) to stratify HCC patients into different CIMP subtypes, and evaluated their correlation with clinical characteristics. In addition, mutation, CNV, and transcriptome profiles were also utilized to evaluate the distinctive genomic patterns correlated with CIMP. Finally, a CIMP-associated prognostic model (CPM) was trained and validated using four independent datasets. FINDINGS:A subgroup of patients was identified as having CIMP-H, which was associated with worse OS and DFS. Gene enrichment analysis indicated that the terms "liver cancer with EPCAM up", "tumor invasiveness up", "methyltransferase complex", and "translational initiation" were enriched in CIMP-H subgroup. Notably, somatic mutation analysis indicated that CIMP-H patients presented with a higher mutation burden of BRD4, DDIAS and NOX1. Moreover, four CPM associated genes could significantly categorize patients into low- and high-risk groups in the training dataset and another 3 independent validation datasets. Finally, a nomogram incorporating a classifier based on four mRNAs, pathological M stage and CIMP status was established, which showed a favorable discriminating ability and might contribute to clinical decision-making for HCC. INTERPRETATION:Our work highlights the potential clinical application value of CPM in predicting the overall survival of HCC patients and the mechanisms underlying the role of CIMP in hepatocarcinogenesis. FUND: This work was supported by the State Key Project on Infectious Diseases of China (2018ZX10723204-003), the National Nature Science Foundation of China (Nos. 81874065, 81500565, 81874149, 81572427, and 81401997), the Hepato-Biliary-Pancreatic Malignant Tumor Investigation Fund of Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH11800001-2018356).

Project description:BackgroundTP53 mutation is the most common mutation in hepatocellular carcinoma (HCC), and it affects the progression and prognosis of HCC. We investigated how TP53 mutation regulates the HCC immunophenotype and thus affects the prognosis of HCC.MethodsWe investigated TP53 mutation status and RNA expression in different populations and platforms and developed an immune prognostic model (IPM) based on immune-related genes that were differentially expressed between TP53WT and TP53MUT HCC samples. Then, the influence of the IPM on the immune microenvironment in HCC was comprehensively analysed.FindingsTP53 mutation resulted in the downregulation of the immune response in HCC. Thirty-seven of the 312 immune response-related genes were differentially expressed based on TP53 mutation status. An IPM was established and validated based on 865 patients with HCC to differentiate patients with a low or high risk of poor survival. A nomogram was also established for clinical application. Functional enrichment analysis showed that the humoral immune response and immune system diseases pathway represented the major function and pathway, respectively, related to the IPM genes. Moreover, we found that the patients in the high-risk group had higher fractions of T cells follicular helper, T cells regulatory (Tregs) and macrophages M0 and presented higher expression of CTLA-4, PD-1 and TIM-3 than the low-risk group.InterpretationTP53 mutation is strongly related to the immune microenvironment in HCC. Our IPM, which is sensitive to TP53 mutation status, may have important implications for identifying subgroups of HCC patients with low or high risk of unfavourable survival. FUND: This work was supported by the International Science and Technology Cooperation Projects (2016YFE0107100), the Capital Special Research Project for Health Development (2014-2-4012), the Beijing Natural Science Foundation (L172055 and 7192158), the National Ten Thousand Talent Program, the Fundamental Research Funds for the Central Universities (3332018032), and the CAMS Innovation Fund for Medical Science (CIFMS) (2017-I2M-4-003 and 2018-I2M-3-001).

Project description:BackgroundThe tumor immune microenvironment is pivotal in predicting clinical outcomes and therapeutic efficacy in cancer patients. This study aims to develop an immune prediction model (IPM) to effectively predict prognosis and immunotherapeutic response in patients with hepatocellular carcinoma (HCC).MethodsAn IPM was constructed and validated based on immune-related genes. The influence of IPM on the HCC immune microenvironment, as well as the possible mechanism, was comprehensively analyzed. The value of the model in predicting the response of HCC patients to immunotherapy was also evaluated.ResultsA novel IPM based on eight genes was developed and validated to predict the prognosis of HCC patients. These genes are matrix metalloproteinase 12 (MMP12), heme oxygenase 1 (HMOX1), C-X-C motif chemokine receptor 6 (CXCR6), hepatoma-derived growth factor (HDGF), placental growth factor (PGF), tyrosine kinase 2 (TYK2), retinoid X receptor beta (RXRB), and cyclin-dependent kinase 4 (CDK4). High-risk patients showed significantly poorer survival than low-risk patients. A nomogram was also established based on the IPM and tumor, node, metastasis (TNM) classification, which showed some net clinical benefit. Gene set enrichment analysis (GSEA) revealed several significantly enriched oncological signatures and immunologic signatures. Furthermore, high-risk patients were characterized by severe clinicopathological characteristics and immune cell infiltration. Finally, we found the that the IPM showed a significant positive correlation with programmed cell death 1 (PDCD1), cluster of differentiation 274 (CD274), and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) expression, suggesting a potentially enhanced effects of immunotherapy antibodies in HCC patients with a high risk score.ConclusionsA novel IPM that could predict clinical prognosis and immunotherapeutic response in HCC patients was developed. Our findings not only provide new insights into the identification of HCC patients with poor survival, but also deepen our understanding of the immune microenvironment, as well as the mechanism of immunotherapy, in HCC.

Dataset Information

Development and validation of a CTNNB1-associated metabolic prognostic model for hepatocellular carcinoma.

Publications

Development and validation of a CTNNB1-associated metabolic prognostic model for hepatocellular carcinoma.

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