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m(6)A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma
BACKGROUND: m(6)A modification plays a key role in the development of hepatocellular carcinoma (HCC). Angiogenesis-related genes (ARGs) are increasingly being used to define signatures predicting patient prognosis. The correlations between m(6)A-related ARGs (mARGs), clinical outcomes, and the immun...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554665/ https://www.ncbi.nlm.nih.gov/pubmed/36246403 http://dx.doi.org/10.1155/2022/8301888 |
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author | Qu, Xiaodong Zhang, Luyao Li, Songbo Li, Tian Zhao, Xingyu Wang, Na Shi, Yongquan |
author_facet | Qu, Xiaodong Zhang, Luyao Li, Songbo Li, Tian Zhao, Xingyu Wang, Na Shi, Yongquan |
author_sort | Qu, Xiaodong |
collection | PubMed |
description | BACKGROUND: m(6)A modification plays a key role in the development of hepatocellular carcinoma (HCC). Angiogenesis-related genes (ARGs) are increasingly being used to define signatures predicting patient prognosis. The correlations between m(6)A-related ARGs (mARGs), clinical outcomes, and the immune and oxidative stress landscape are unclear. METHODS: Univariate Cox regression analysis of 24 mARGs yielded 13 prognostic genes, which were then analyzed for their enriched functions and pathways. After LASSO regression analysis, a prognostic signature was constructed and its reliability validated. Patients were grouped by risk using the signature score, and then the clinical prognosis, the immune landscape, and the oxidative stress landscape between the two groups were analyzed. Drug sensitivity analysis was performed to identify potentially efficient therapeutic agents. RESULTS: Thirteen prognosis-related mARGs consistently clustered patients with HCC into four groups with significantly different prognosis. Four mARGs (EGF, ITGA5, ITGAV, and PLG) were used to construct a prognostic signature and define risk groups. Among them, EGF, ITGA5, and ITGAV, were defined as prognostic risk factors, while PLG was defined as a prognostic protective factor. Compared to low-risk patients, HCC patients in the high-risk group had a poorer prognosis and showed significant differences in clinical characteristics, enriched pathways, tumor stemness, and tumor microenvironment. The drug sensitivity of oxaliplatin and LDK-378 negatively correlated with ITGAV expression. Ten drugs had lower IC50s in the high-risk group, indicating better antitumor efficacy than in the low-risk group, with epothilone B having the lowest IC50 value. CONCLUSIONS: A prognostic model consisting of mARGs can be used to predict the prognosis of HCC patients. The risk grouping of our model can be used to reveal differences in the tumor immune microenvironment of patients with HCC. Further in-depth study may provide new targets for future treatment. |
format | Online Article Text |
id | pubmed-9554665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95546652022-10-13 m(6)A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma Qu, Xiaodong Zhang, Luyao Li, Songbo Li, Tian Zhao, Xingyu Wang, Na Shi, Yongquan Oxid Med Cell Longev Research Article BACKGROUND: m(6)A modification plays a key role in the development of hepatocellular carcinoma (HCC). Angiogenesis-related genes (ARGs) are increasingly being used to define signatures predicting patient prognosis. The correlations between m(6)A-related ARGs (mARGs), clinical outcomes, and the immune and oxidative stress landscape are unclear. METHODS: Univariate Cox regression analysis of 24 mARGs yielded 13 prognostic genes, which were then analyzed for their enriched functions and pathways. After LASSO regression analysis, a prognostic signature was constructed and its reliability validated. Patients were grouped by risk using the signature score, and then the clinical prognosis, the immune landscape, and the oxidative stress landscape between the two groups were analyzed. Drug sensitivity analysis was performed to identify potentially efficient therapeutic agents. RESULTS: Thirteen prognosis-related mARGs consistently clustered patients with HCC into four groups with significantly different prognosis. Four mARGs (EGF, ITGA5, ITGAV, and PLG) were used to construct a prognostic signature and define risk groups. Among them, EGF, ITGA5, and ITGAV, were defined as prognostic risk factors, while PLG was defined as a prognostic protective factor. Compared to low-risk patients, HCC patients in the high-risk group had a poorer prognosis and showed significant differences in clinical characteristics, enriched pathways, tumor stemness, and tumor microenvironment. The drug sensitivity of oxaliplatin and LDK-378 negatively correlated with ITGAV expression. Ten drugs had lower IC50s in the high-risk group, indicating better antitumor efficacy than in the low-risk group, with epothilone B having the lowest IC50 value. CONCLUSIONS: A prognostic model consisting of mARGs can be used to predict the prognosis of HCC patients. The risk grouping of our model can be used to reveal differences in the tumor immune microenvironment of patients with HCC. Further in-depth study may provide new targets for future treatment. Hindawi 2022-09-30 /pmc/articles/PMC9554665/ /pubmed/36246403 http://dx.doi.org/10.1155/2022/8301888 Text en Copyright © 2022 Xiaodong Qu 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 Qu, Xiaodong Zhang, Luyao Li, Songbo Li, Tian Zhao, Xingyu Wang, Na Shi, Yongquan m(6)A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma |
title | m(6)A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma |
title_full | m(6)A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma |
title_fullStr | m(6)A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma |
title_full_unstemmed | m(6)A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma |
title_short | m(6)A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma |
title_sort | m(6)a-related angiogenic genes to construct prognostic signature, reveal immune and oxidative stress landscape, and screen drugs in hepatocellular carcinoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554665/ https://www.ncbi.nlm.nih.gov/pubmed/36246403 http://dx.doi.org/10.1155/2022/8301888 |
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