Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC)
Xin Yang,1 Qiong Liu,1 Juan Zou,2 Yu-kun Li,2 Xia Xie1 1Department of Infectious Diseases, The First Affiliated Hospital of University of South China, Heng Yang, Hunan, 421000, People’s Republic of China; 2Key Laboratory of Tumor Cellular and Molecular Pathology, College of Hunan Province, Cancer Re...
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doaj-2541caa7421b4c33b16b1e0bbf1a47db2021-07-14T19:53:23ZengDove Medical PressCancer Management and Research1179-13222021-07-01Volume 135683569866993Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC)Yang XLiu QZou JLi YKXie XXin Yang,1 Qiong Liu,1 Juan Zou,2 Yu-kun Li,2 Xia Xie1 1Department of Infectious Diseases, The First Affiliated Hospital of University of South China, Heng Yang, Hunan, 421000, People’s Republic of China; 2Key Laboratory of Tumor Cellular and Molecular Pathology, College of Hunan Province, Cancer Research Institute, University of South China, Hengyang, Hunan, 421001, People’s Republic of ChinaCorrespondence: Xia XieDepartment of Infectious Diseases, The First Affiliated Hospital of University of South China, Heng Yang, Hunan, 421000, People’s Republic of ChinaEmail xiaixe123123@163.comBackground: Metabolic disorders have attracted increasing attention from scientists who conduct research on various tumours, especially hepatocellular carcinoma (HCC). The purpose of this study was to assess the prognostic significance of metabolism in HCC.Methods: The expression profiles of metabolism-related genes (MRGs) of 349 surviving HCC patients were extracted from The Cancer Genome Atlas (TCGA) database. Subsequently, a series of biomedical computational algorithms were used to identify a seven-MRG signature as a prognostic model. GSEA indicated the function and pathway enrichment of these MRGs. Then, drug sensitivity analysis was used to identify the hub gene, which was tested using IHC staining.Results: A total of 420 differential MRGs and 116 differentially expressed transcription factors (TFs) were identified in HCC patients based on data from the TCGA database. The GO and KEGG enrichment analyses indicated that metabolic disturbance might be involved in the development of HCC. LASSO regression analysis was used to construct a seven-MRG signature (DHDH, ENO1, G6PD, LPCAT1, PDE6D, PIGU and PPAT) that could predict the prognosis of HCC patients. GSEA revealed the functional and pathway enrichment of these seven MRGs. Then, drug sensitivity analysis indicated that G6PD might play a key role in the prognosis of HCC by promoting chemoresistance. Finally, we used IHC staining to demonstrate the relationship between G6PD expression levels and clinical parameters in HCC patients.Conclusion: The results of this study provide a potential method for predicting the prognosis of HCC patients and avenues for further studies of HCC metabolism. Moreover, the function of G6PD may play a key role in the development and progression of HCC.Keywords: bioinformatical analysis, hepatocellular carcinoma, metabolic-genomic landscape, The Cancer Genome Atlas, prognostic indexhttps://www.dovepress.com/identification-of-a-prognostic-index-based-on-a-metabolic-genomic-land-peer-reviewed-fulltext-article-CMARbioinformatical analysishepatocellular carcinomametabolic-genomic landscapethe cancer genome atlasprognostic index |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yang X Liu Q Zou J Li YK Xie X |
spellingShingle |
Yang X Liu Q Zou J Li YK Xie X Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC) Cancer Management and Research bioinformatical analysis hepatocellular carcinoma metabolic-genomic landscape the cancer genome atlas prognostic index |
author_facet |
Yang X Liu Q Zou J Li YK Xie X |
author_sort |
Yang X |
title |
Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC) |
title_short |
Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC) |
title_full |
Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC) |
title_fullStr |
Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC) |
title_full_unstemmed |
Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC) |
title_sort |
identification of a prognostic index based on a metabolic-genomic landscape analysis of hepatocellular carcinoma (hcc) |
publisher |
Dove Medical Press |
series |
Cancer Management and Research |
issn |
1179-1322 |
publishDate |
2021-07-01 |
description |
Xin Yang,1 Qiong Liu,1 Juan Zou,2 Yu-kun Li,2 Xia Xie1 1Department of Infectious Diseases, The First Affiliated Hospital of University of South China, Heng Yang, Hunan, 421000, People’s Republic of China; 2Key Laboratory of Tumor Cellular and Molecular Pathology, College of Hunan Province, Cancer Research Institute, University of South China, Hengyang, Hunan, 421001, People’s Republic of ChinaCorrespondence: Xia XieDepartment of Infectious Diseases, The First Affiliated Hospital of University of South China, Heng Yang, Hunan, 421000, People’s Republic of ChinaEmail xiaixe123123@163.comBackground: Metabolic disorders have attracted increasing attention from scientists who conduct research on various tumours, especially hepatocellular carcinoma (HCC). The purpose of this study was to assess the prognostic significance of metabolism in HCC.Methods: The expression profiles of metabolism-related genes (MRGs) of 349 surviving HCC patients were extracted from The Cancer Genome Atlas (TCGA) database. Subsequently, a series of biomedical computational algorithms were used to identify a seven-MRG signature as a prognostic model. GSEA indicated the function and pathway enrichment of these MRGs. Then, drug sensitivity analysis was used to identify the hub gene, which was tested using IHC staining.Results: A total of 420 differential MRGs and 116 differentially expressed transcription factors (TFs) were identified in HCC patients based on data from the TCGA database. The GO and KEGG enrichment analyses indicated that metabolic disturbance might be involved in the development of HCC. LASSO regression analysis was used to construct a seven-MRG signature (DHDH, ENO1, G6PD, LPCAT1, PDE6D, PIGU and PPAT) that could predict the prognosis of HCC patients. GSEA revealed the functional and pathway enrichment of these seven MRGs. Then, drug sensitivity analysis indicated that G6PD might play a key role in the prognosis of HCC by promoting chemoresistance. Finally, we used IHC staining to demonstrate the relationship between G6PD expression levels and clinical parameters in HCC patients.Conclusion: The results of this study provide a potential method for predicting the prognosis of HCC patients and avenues for further studies of HCC metabolism. Moreover, the function of G6PD may play a key role in the development and progression of HCC.Keywords: bioinformatical analysis, hepatocellular carcinoma, metabolic-genomic landscape, The Cancer Genome Atlas, prognostic index |
topic |
bioinformatical analysis hepatocellular carcinoma metabolic-genomic landscape the cancer genome atlas prognostic index |
url |
https://www.dovepress.com/identification-of-a-prognostic-index-based-on-a-metabolic-genomic-land-peer-reviewed-fulltext-article-CMAR |
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