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Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer

BACKGROUND: Gastric cancer is one of the common malignant tumors of the digestive system worldwide, posing a serious threat to human health. A growing number of studies have demonstrated the important role that lipid droplets play in promoting cancer progression. However, few studies have systematic...

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Autores principales: Liu, Mengxiao, Fang, Xidong, Wang, Haoying, Ji, Rui, Guo, Qinghong, Chen, Zhaofeng, Ren, Qian, Wang, Yuping, Zhou, Yongning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875057/
https://www.ncbi.nlm.nih.gov/pubmed/36713557
http://dx.doi.org/10.3389/fonc.2022.1038932
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author Liu, Mengxiao
Fang, Xidong
Wang, Haoying
Ji, Rui
Guo, Qinghong
Chen, Zhaofeng
Ren, Qian
Wang, Yuping
Zhou, Yongning
author_facet Liu, Mengxiao
Fang, Xidong
Wang, Haoying
Ji, Rui
Guo, Qinghong
Chen, Zhaofeng
Ren, Qian
Wang, Yuping
Zhou, Yongning
author_sort Liu, Mengxiao
collection PubMed
description BACKGROUND: Gastric cancer is one of the common malignant tumors of the digestive system worldwide, posing a serious threat to human health. A growing number of studies have demonstrated the important role that lipid droplets play in promoting cancer progression. However, few studies have systematically evaluated the role of lipid droplet metabolism-related genes (LDMRGs) in patients with gastric cancer. METHODS: We identified two distinct molecular subtypes in the TCGA-STAD cohort based on LDMRGs expression. We then constructed risk prediction scoring models in the TCGA-STAD cohort by lasso regression analysis and validated the model with the GSE15459 and GSE66229 cohorts. Moreover, we constructed a nomogram prediction model by cox regression analysis and evaluated the predictive efficacy of the model by various methods in STAD. Finally, we identified the key gene in LDMRGs, ABCA1, and performed a systematic multi-omics analysis in gastric cancer. RESULTS: Two molecular subtypes were identified based on LDMRGs expression with different survival prognosis and immune infiltration levels. lasso regression models were effective in predicting overall survival (OS) of gastric cancer patients at 1, 3 and 5 years and were validated in the GEO database with consistent results. The nomogram prediction model incorporated additional clinical factors and prognostic molecules to improve the prognostic predictive value of the current TNM staging system. ABCA1 was identified as a key gene in LDMRGs and multi-omics analysis showed a strong correlation between ABCA1 and the prognosis and immune status of patients with gastric cancer. CONCLUSION: This study reveals the characteristics and possible underlying mechanisms of LDMRGs in gastric cancer, contributing to the identification of new prognostic biomarkers and providing a basis for future research.
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spelling pubmed-98750572023-01-26 Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer Liu, Mengxiao Fang, Xidong Wang, Haoying Ji, Rui Guo, Qinghong Chen, Zhaofeng Ren, Qian Wang, Yuping Zhou, Yongning Front Oncol Oncology BACKGROUND: Gastric cancer is one of the common malignant tumors of the digestive system worldwide, posing a serious threat to human health. A growing number of studies have demonstrated the important role that lipid droplets play in promoting cancer progression. However, few studies have systematically evaluated the role of lipid droplet metabolism-related genes (LDMRGs) in patients with gastric cancer. METHODS: We identified two distinct molecular subtypes in the TCGA-STAD cohort based on LDMRGs expression. We then constructed risk prediction scoring models in the TCGA-STAD cohort by lasso regression analysis and validated the model with the GSE15459 and GSE66229 cohorts. Moreover, we constructed a nomogram prediction model by cox regression analysis and evaluated the predictive efficacy of the model by various methods in STAD. Finally, we identified the key gene in LDMRGs, ABCA1, and performed a systematic multi-omics analysis in gastric cancer. RESULTS: Two molecular subtypes were identified based on LDMRGs expression with different survival prognosis and immune infiltration levels. lasso regression models were effective in predicting overall survival (OS) of gastric cancer patients at 1, 3 and 5 years and were validated in the GEO database with consistent results. The nomogram prediction model incorporated additional clinical factors and prognostic molecules to improve the prognostic predictive value of the current TNM staging system. ABCA1 was identified as a key gene in LDMRGs and multi-omics analysis showed a strong correlation between ABCA1 and the prognosis and immune status of patients with gastric cancer. CONCLUSION: This study reveals the characteristics and possible underlying mechanisms of LDMRGs in gastric cancer, contributing to the identification of new prognostic biomarkers and providing a basis for future research. Frontiers Media S.A. 2023-01-11 /pmc/articles/PMC9875057/ /pubmed/36713557 http://dx.doi.org/10.3389/fonc.2022.1038932 Text en Copyright © 2023 Liu, Fang, Wang, Ji, Guo, Chen, Ren, Wang and Zhou https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Liu, Mengxiao
Fang, Xidong
Wang, Haoying
Ji, Rui
Guo, Qinghong
Chen, Zhaofeng
Ren, Qian
Wang, Yuping
Zhou, Yongning
Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer
title Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer
title_full Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer
title_fullStr Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer
title_full_unstemmed Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer
title_short Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer
title_sort characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875057/
https://www.ncbi.nlm.nih.gov/pubmed/36713557
http://dx.doi.org/10.3389/fonc.2022.1038932
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