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Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis

BACKGROUND: The molecular classification of HCC premised on metabolic genes might give assistance for diagnosis, therapy, prognosis prediction, immune infiltration, and oxidative stress in addition to supplementing the limitations of the clinical staging system. This would help to better represent t...

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Autores principales: Cui, Shimeng, Zhang, Minghao, Bai, Shanshan, Bi, Yanfang, Cong, Shan, Jin, Shi, Li, Shuang, He, Hui, Zhang, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969974/
https://www.ncbi.nlm.nih.gov/pubmed/36860731
http://dx.doi.org/10.1155/2023/1847700
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author Cui, Shimeng
Zhang, Minghao
Bai, Shanshan
Bi, Yanfang
Cong, Shan
Jin, Shi
Li, Shuang
He, Hui
Zhang, Jian
author_facet Cui, Shimeng
Zhang, Minghao
Bai, Shanshan
Bi, Yanfang
Cong, Shan
Jin, Shi
Li, Shuang
He, Hui
Zhang, Jian
author_sort Cui, Shimeng
collection PubMed
description BACKGROUND: The molecular classification of HCC premised on metabolic genes might give assistance for diagnosis, therapy, prognosis prediction, immune infiltration, and oxidative stress in addition to supplementing the limitations of the clinical staging system. This would help to better represent the deeper features of HCC. METHODS: TCGA datasets combined with GSE14520 and HCCDB18 datasets were used to determine the metabolic subtype (MC) using ConsensusClusterPlus. ssGSEA method was used to calculate the IFNγ score, the oxidative stress pathway scores, and the score distribution of 22 distinct immune cells, and their differential expressions were assessed with the use of CIBERSORT. To generate a subtype classification feature index, LDA was utilized. Screening of the metabolic gene coexpression modules was done with the help of WGCNA. RESULTS: Three MCs (MC1, MC2, and MC3) were identified and showed different prognoses (MC2-poor and MC1-better). Although MC2 had a high immune microenvironment infiltration, T cell exhaustion markers were expressed at a high level in MC2 in contrast with MC1. Most oxidative stress-related pathways are inhibited in the MC2 subtype and activated in the MC1 subtype. The immunophenotyping of pan-cancer showed that the C1 and C2 subtypes with poor prognosis accounted for significantly higher proportions of MC2 and MC3 subtypes than MC1, while the better prognostic C3 subtype accounted for significantly lower proportions of MC2 than MC1. As per the findings of the TIDE analysis, MC1 had a greater likelihood of benefiting from immunotherapeutic regimens. MC2 was found to have a greater sensitivity to traditional chemotherapy drugs. Finally, 7 potential gene markers indicate HCC prognosis. CONCLUSION: The difference (variation) in tumor microenvironment and oxidative stress among metabolic subtypes of HCC was compared from multiple angles and levels. A complete and thorough clarification of the molecular pathological properties of HCC, the exploration of reliable markers for diagnosis, the improvement of the cancer staging system, and the guiding of individualized treatment of HCC all gain benefit greatly from molecular classification associated with metabolism.
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spelling pubmed-99699742023-02-28 Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis Cui, Shimeng Zhang, Minghao Bai, Shanshan Bi, Yanfang Cong, Shan Jin, Shi Li, Shuang He, Hui Zhang, Jian Oxid Med Cell Longev Research Article BACKGROUND: The molecular classification of HCC premised on metabolic genes might give assistance for diagnosis, therapy, prognosis prediction, immune infiltration, and oxidative stress in addition to supplementing the limitations of the clinical staging system. This would help to better represent the deeper features of HCC. METHODS: TCGA datasets combined with GSE14520 and HCCDB18 datasets were used to determine the metabolic subtype (MC) using ConsensusClusterPlus. ssGSEA method was used to calculate the IFNγ score, the oxidative stress pathway scores, and the score distribution of 22 distinct immune cells, and their differential expressions were assessed with the use of CIBERSORT. To generate a subtype classification feature index, LDA was utilized. Screening of the metabolic gene coexpression modules was done with the help of WGCNA. RESULTS: Three MCs (MC1, MC2, and MC3) were identified and showed different prognoses (MC2-poor and MC1-better). Although MC2 had a high immune microenvironment infiltration, T cell exhaustion markers were expressed at a high level in MC2 in contrast with MC1. Most oxidative stress-related pathways are inhibited in the MC2 subtype and activated in the MC1 subtype. The immunophenotyping of pan-cancer showed that the C1 and C2 subtypes with poor prognosis accounted for significantly higher proportions of MC2 and MC3 subtypes than MC1, while the better prognostic C3 subtype accounted for significantly lower proportions of MC2 than MC1. As per the findings of the TIDE analysis, MC1 had a greater likelihood of benefiting from immunotherapeutic regimens. MC2 was found to have a greater sensitivity to traditional chemotherapy drugs. Finally, 7 potential gene markers indicate HCC prognosis. CONCLUSION: The difference (variation) in tumor microenvironment and oxidative stress among metabolic subtypes of HCC was compared from multiple angles and levels. A complete and thorough clarification of the molecular pathological properties of HCC, the exploration of reliable markers for diagnosis, the improvement of the cancer staging system, and the guiding of individualized treatment of HCC all gain benefit greatly from molecular classification associated with metabolism. Hindawi 2023-02-18 /pmc/articles/PMC9969974/ /pubmed/36860731 http://dx.doi.org/10.1155/2023/1847700 Text en Copyright © 2023 Shimeng Cui et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cui, Shimeng
Zhang, Minghao
Bai, Shanshan
Bi, Yanfang
Cong, Shan
Jin, Shi
Li, Shuang
He, Hui
Zhang, Jian
Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis
title Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis
title_full Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis
title_fullStr Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis
title_full_unstemmed Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis
title_short Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis
title_sort development of prognostic features of hepatocellular carcinoma based on metabolic gene classification and immune and oxidative stress characteristic analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969974/
https://www.ncbi.nlm.nih.gov/pubmed/36860731
http://dx.doi.org/10.1155/2023/1847700
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