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Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer

BACKGROUND: Gastric cancer (GC) is the fifth most common cancer and the second leading cause of cancer-related deaths worldwide. Due to the lack of specific markers, the early diagnosis of gastric cancer is very low, and most patients with gastric cancer are diagnosed at advanced stages. The aim of...

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Autores principales: Li, Chao, Yang, Tan, Yuan, Yu, Wen, Rou, Yu, Huan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288199/
https://www.ncbi.nlm.nih.gov/pubmed/37359529
http://dx.doi.org/10.3389/fimmu.2023.1202529
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author Li, Chao
Yang, Tan
Yuan, Yu
Wen, Rou
Yu, Huan
author_facet Li, Chao
Yang, Tan
Yuan, Yu
Wen, Rou
Yu, Huan
author_sort Li, Chao
collection PubMed
description BACKGROUND: Gastric cancer (GC) is the fifth most common cancer and the second leading cause of cancer-related deaths worldwide. Due to the lack of specific markers, the early diagnosis of gastric cancer is very low, and most patients with gastric cancer are diagnosed at advanced stages. The aim of this study was to identify key biomarkers of GC and to elucidate GC-associated immune cell infiltration and related pathways. METHODS: Gene microarray data associated with GC were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were analyzed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia, Gene Set Enrichment Analysis (GSEA) and Protein−Protein Interaction (PPI) networks. Weighted gene coexpression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to identify pivotal genes for GC and to assess the diagnostic accuracy of GC hub markers using the subjects’ working characteristic curves. In addition, the infiltration levels of 28 immune cells in GC and their interrelationship with hub markers were analyzed using ssGSEA. And further validated by RT-qPCR. RESULTS: A total of 133 DEGs were identified. The biological functions and signaling pathways closely associated with GC were inflammatory and immune processes. Nine expression modules were obtained by WGCNA, with the pink module having the highest correlation with GC; 13 crossover genes were obtained by combining DEGs. Subsequently, the LASSO algorithm and validation set verification analysis were used to finally identify three hub genes as potential biomarkers of GC. In the immune cell infiltration analysis, infiltration of activated CD4 T cell, macrophages, regulatory T cells and plasmacytoid dendritic cells was more significant in GC. The validation part demonstrated that three hub genes were expressed at lower levels in the gastric cancer cells. CONCLUSION: The use of WGCNA combined with the LASSO algorithm to identify hub biomarkers closely related to GC can help to elucidate the molecular mechanism of GC development and is important for finding new immunotherapeutic targets and disease prevention.
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spelling pubmed-102881992023-06-24 Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer Li, Chao Yang, Tan Yuan, Yu Wen, Rou Yu, Huan Front Immunol Immunology BACKGROUND: Gastric cancer (GC) is the fifth most common cancer and the second leading cause of cancer-related deaths worldwide. Due to the lack of specific markers, the early diagnosis of gastric cancer is very low, and most patients with gastric cancer are diagnosed at advanced stages. The aim of this study was to identify key biomarkers of GC and to elucidate GC-associated immune cell infiltration and related pathways. METHODS: Gene microarray data associated with GC were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were analyzed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia, Gene Set Enrichment Analysis (GSEA) and Protein−Protein Interaction (PPI) networks. Weighted gene coexpression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to identify pivotal genes for GC and to assess the diagnostic accuracy of GC hub markers using the subjects’ working characteristic curves. In addition, the infiltration levels of 28 immune cells in GC and their interrelationship with hub markers were analyzed using ssGSEA. And further validated by RT-qPCR. RESULTS: A total of 133 DEGs were identified. The biological functions and signaling pathways closely associated with GC were inflammatory and immune processes. Nine expression modules were obtained by WGCNA, with the pink module having the highest correlation with GC; 13 crossover genes were obtained by combining DEGs. Subsequently, the LASSO algorithm and validation set verification analysis were used to finally identify three hub genes as potential biomarkers of GC. In the immune cell infiltration analysis, infiltration of activated CD4 T cell, macrophages, regulatory T cells and plasmacytoid dendritic cells was more significant in GC. The validation part demonstrated that three hub genes were expressed at lower levels in the gastric cancer cells. CONCLUSION: The use of WGCNA combined with the LASSO algorithm to identify hub biomarkers closely related to GC can help to elucidate the molecular mechanism of GC development and is important for finding new immunotherapeutic targets and disease prevention. Frontiers Media S.A. 2023-06-09 /pmc/articles/PMC10288199/ /pubmed/37359529 http://dx.doi.org/10.3389/fimmu.2023.1202529 Text en Copyright © 2023 Li, Yang, Yuan, Wen and Yu 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 Immunology
Li, Chao
Yang, Tan
Yuan, Yu
Wen, Rou
Yu, Huan
Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer
title Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer
title_full Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer
title_fullStr Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer
title_full_unstemmed Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer
title_short Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer
title_sort bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288199/
https://www.ncbi.nlm.nih.gov/pubmed/37359529
http://dx.doi.org/10.3389/fimmu.2023.1202529
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