Literature DB >> 27107678

Integrative functional genomic analysis unveils the differing dysregulated metabolic processes across hepatocellular carcinoma stages.

Vignesh Ramesh1, Kumaresan Ganesan2.   

Abstract

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease and the development of targeted therapeutics is still at an early stage. The 'omics' based genome-wide profiling comprising the transcriptome, miRNome and proteome are highly useful in identifying the deregulated molecular processes involved in hepatocarcinogenesis. One of the end products and processes of the central dogma being the metabolites and metabolic processes mediate the cellular functions. In recent years, metabolomics based investigations have revealed the major deregulated metabolic processes involved in carcinogenesis. However, the integrative analysis of the holistic metabolic processes with genomics is at an early stage. Since the gene-sets are highly useful in assessing the biological processes and pathways, we made an attempt to infer the deregulated cellular metabolic processes involved in HCC by employing metabolism associated gene-set enrichment analysis. Further, the metabolic process enrichment scores were integrated with the transcriptome profiles of HCC. Integrative analysis shows three distinct metabolic deregulations: i) hepatocyte function related molecular processes involving lipid/fatty acid/bile acid synthesis, ii) inflammatory processes with cytokine, sphingolipid & chondriotin sulphate metabolism and iii) enriched nucleotide metabolic process involving purine/pyrimidine & glucose mediated catabolic process, in hepatocarcinogenesis. The three distinct metabolic processes were found to occur both in tumor and liver cancer cell line profiles. Unsupervised hierarchical clustering of the metabolic processes along with clinical sample information has identified two major clusters based on AFP (alpha-fetoprotein) and metastasis. The study reveals the three major regulatory processes involved in HCC stages.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Hepatocellular carcinoma; Inflammation; Integrative genomics; Lipid metabolism; Metabolic process; Transcriptome

Mesh:

Year:  2016        PMID: 27107678     DOI: 10.1016/j.gene.2016.04.039

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  4 in total

1.  Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma.

Authors:  Dokyoon Kim; Ruowang Li; Anastasia Lucas; Shefali S Verma; Scott M Dudek; Marylyn D Ritchie
Journal:  J Am Med Inform Assoc       Date:  2017-05-01       Impact factor: 4.497

Review 2.  Emerging roles of lipid metabolism in cancer metastasis.

Authors:  Xiangjian Luo; Can Cheng; Zheqiong Tan; Namei Li; Min Tang; Lifang Yang; Ya Cao
Journal:  Mol Cancer       Date:  2017-04-11       Impact factor: 27.401

3.  Bioinformatics analysis and experimental verification of five metabolism-related lncRNAs as prognostic models for hepatocellular carcinoma.

Authors:  Wei Wang; Zhenfeng Deng; Zongrui Jin; Guolin Wu; Jilong Wang; Hai Zhu; Banghao Xu; Zhang Wen; Ya Guo
Journal:  Medicine (Baltimore)       Date:  2022-01-28       Impact factor: 1.889

4.  CpG Site-Specific Methylation-Modulated Divergent Expression of PRSS3 Transcript Variants Facilitates Nongenetic Intratumor Heterogeneity in Human Hepatocellular Carcinoma.

Authors:  Shuye Lin; Hanli Xu; Mengdi Pang; Xiaomeng Zhou; Yuanming Pan; Lishu Zhang; Xin Guan; Xiaoyue Wang; Bonan Lin; Rongmeng Tian; Keqiang Chen; Xiaochen Zhang; Zijiang Yang; Fengmin Ji; Yingying Huang; Wu Wei; Wanghua Gong; Jianke Ren; Ji Ming Wang; Mingzhou Guo; Jiaqiang Huang
Journal:  Front Oncol       Date:  2022-04-11       Impact factor: 5.738

  4 in total

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