Identification of early diagnostic biomarkers via WGCNA in gastric cancer

Background: Gastric cancer (GC) is the world's second-leading cause of cancer-related mortality, continuing to make it a serious healthcare concern. Even though the prevalence of GC reduces, the prognosis for GC patients remains poor in terms of a lack of reliable biomarkers to diagnose early G...

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Autores principales:	Zohreh Rezaei, Javad Ranjbaran, Hossein Safarpour, Samira Nomiri, Fatemeh Salmani, Elham Chamani, Pegah Larki, Oronzo Brunetti, Nicola Silvestris, Tahmine Tavakoli
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Gastric Cancer WGCNA Transcriptome analysis ITGAX CCL14 ADHFE1 Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/e4e096cef9484e87aa50b45edf8d1f26
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spelling	oai:doaj.org-article:e4e096cef9484e87aa50b45edf8d1f262021-12-04T04:33:06ZIdentification of early diagnostic biomarkers via WGCNA in gastric cancer0753-332210.1016/j.biopha.2021.112477https://doaj.org/article/e4e096cef9484e87aa50b45edf8d1f262022-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0753332221012634https://doaj.org/toc/0753-3322Background: Gastric cancer (GC) is the world's second-leading cause of cancer-related mortality, continuing to make it a serious healthcare concern. Even though the prevalence of GC reduces, the prognosis for GC patients remains poor in terms of a lack of reliable biomarkers to diagnose early GC and predict chemosensitivity and recurrence. Methods and material: We integrated the gene expression patterns of gastric cancers from four RNAseq datasets (GSE113255, GSE142000, GSE118897, and GSE130823) from Gene Expression Omnibus (GEO) database to recognize differentially expressed genes (DEGs) between normal and GC samples. A gene co-expression network was built using weighted co-expression network analysis (WGCNA). Furthermore, RT-qPCR was performed to validate the in silico results. Results: The red modules in GSE113255, Turquoise in GSE142000, Brown in GSE118897, and the green-yellow module in GSE130823 datasets were found to be highly correlated with the anatomical site of GC. ITGAX, CCL14, ADHFE1, and HOXB13) as the hub gene are differentially expressed in tumor and non-tumor gastric tissues in this study. RT-qPCR demonstrated a high level of the expression of this gene. Conclusion: The expression levels of ITGAX, CCL14, ADHFE1, and HOXB13 in GC tumor tissues are considerably greater than in adjacent normal tissues. Systems biology approaches identified that these genes could be possible GC marker genes, providing ideas for other experimental studies in the future.Zohreh RezaeiJavad RanjbaranHossein SafarpourSamira NomiriFatemeh SalmaniElham ChamaniPegah LarkiOronzo BrunettiNicola SilvestrisTahmine TavakoliElsevierarticleGastric CancerWGCNATranscriptome analysisITGAXCCL14ADHFE1Therapeutics. PharmacologyRM1-950ENBiomedicine & Pharmacotherapy, Vol 145, Iss , Pp 112477- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Gastric Cancer WGCNA Transcriptome analysis ITGAX CCL14 ADHFE1 Therapeutics. Pharmacology RM1-950
spellingShingle	Gastric Cancer WGCNA Transcriptome analysis ITGAX CCL14 ADHFE1 Therapeutics. Pharmacology RM1-950 Zohreh Rezaei Javad Ranjbaran Hossein Safarpour Samira Nomiri Fatemeh Salmani Elham Chamani Pegah Larki Oronzo Brunetti Nicola Silvestris Tahmine Tavakoli Identification of early diagnostic biomarkers via WGCNA in gastric cancer
description	Background: Gastric cancer (GC) is the world's second-leading cause of cancer-related mortality, continuing to make it a serious healthcare concern. Even though the prevalence of GC reduces, the prognosis for GC patients remains poor in terms of a lack of reliable biomarkers to diagnose early GC and predict chemosensitivity and recurrence. Methods and material: We integrated the gene expression patterns of gastric cancers from four RNAseq datasets (GSE113255, GSE142000, GSE118897, and GSE130823) from Gene Expression Omnibus (GEO) database to recognize differentially expressed genes (DEGs) between normal and GC samples. A gene co-expression network was built using weighted co-expression network analysis (WGCNA). Furthermore, RT-qPCR was performed to validate the in silico results. Results: The red modules in GSE113255, Turquoise in GSE142000, Brown in GSE118897, and the green-yellow module in GSE130823 datasets were found to be highly correlated with the anatomical site of GC. ITGAX, CCL14, ADHFE1, and HOXB13) as the hub gene are differentially expressed in tumor and non-tumor gastric tissues in this study. RT-qPCR demonstrated a high level of the expression of this gene. Conclusion: The expression levels of ITGAX, CCL14, ADHFE1, and HOXB13 in GC tumor tissues are considerably greater than in adjacent normal tissues. Systems biology approaches identified that these genes could be possible GC marker genes, providing ideas for other experimental studies in the future.
format	article
author	Zohreh Rezaei Javad Ranjbaran Hossein Safarpour Samira Nomiri Fatemeh Salmani Elham Chamani Pegah Larki Oronzo Brunetti Nicola Silvestris Tahmine Tavakoli
author_facet	Zohreh Rezaei Javad Ranjbaran Hossein Safarpour Samira Nomiri Fatemeh Salmani Elham Chamani Pegah Larki Oronzo Brunetti Nicola Silvestris Tahmine Tavakoli
author_sort	Zohreh Rezaei
title	Identification of early diagnostic biomarkers via WGCNA in gastric cancer
title_short	Identification of early diagnostic biomarkers via WGCNA in gastric cancer
title_full	Identification of early diagnostic biomarkers via WGCNA in gastric cancer
title_fullStr	Identification of early diagnostic biomarkers via WGCNA in gastric cancer
title_full_unstemmed	Identification of early diagnostic biomarkers via WGCNA in gastric cancer
title_sort	identification of early diagnostic biomarkers via wgcna in gastric cancer
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/e4e096cef9484e87aa50b45edf8d1f26
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Identification of early diagnostic biomarkers via WGCNA in gastric cancer

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