1. Mining TCGA Database for Tumor Microenvironment-Related Genes of Prognostic Value in Hepatocellular Carcinoma
- Author
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Ya Guo, Zhenfeng Deng, Jingjing Zeng, Minhao Peng, Jilong Wang, Banghao Xu, Zongrui Jin, Guolin Wu, and Zhang Wen
- Subjects
Male ,0301 basic medicine ,Carcinoma, Hepatocellular ,Stromal cell ,Article Subject ,lcsh:Medicine ,Biology ,computer.software_genre ,General Biochemistry, Genetics and Molecular Biology ,Cohort Studies ,03 medical and health sciences ,0302 clinical medicine ,Immune system ,Text mining ,Databases, Genetic ,Biomarkers, Tumor ,Tumor Microenvironment ,Carcinoma ,medicine ,Humans ,Protein Interaction Maps ,Gene ,Regulation of gene expression ,Tumor microenvironment ,General Immunology and Microbiology ,Database ,business.industry ,Gene Expression Profiling ,Liver Neoplasms ,lcsh:R ,Computational Biology ,General Medicine ,Middle Aged ,Prognosis ,medicine.disease ,digestive system diseases ,Gene Expression Regulation, Neoplastic ,030104 developmental biology ,030220 oncology & carcinogenesis ,Hepatocellular carcinoma ,Female ,business ,computer ,Algorithms ,Genes, Neoplasm ,Research Article - Abstract
Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies. Recent studies reveal that tumor microenvironment (TME) components significantly affect HCC growth and progression, particularly the infiltrating stromal and immune cells. Thus, mining of TME-related biomarkers is crucial to improve the survival of patients with HCC. Public access of The Cancer Genome Atlas (TCGA) database allows convenient performance of gene expression-based analysis of big data, which contributes to the exploration of potential association between genes and prognosis of a variety of malignancies, including HCC. The “Estimation of STromal and Immune cells in MAlignant Tumors using Expression data” algorithm renders the quantification of the stromal and immune components in TME possible by calculating the stromal and immune scores. Differentially expressed genes (DEGs) were screened by dividing the HCC cohort of TCGA database into high- and low-score groups according to stromal and immune scores. Further analyses of functional enrichment and protein-protein interaction networks show that the DEGs are mainly involved in immune response, cell adhesion, and extracellular matrix. Finally, seven DEGs have significant association with HCC poor outcomes. These genes contain FABP3, GALNT5, GPR84, ITGB6, MYEOV, PLEKHS1, and STRA6 and may be candidate biomarkers for HCC prognosis.
- Published
- 2019