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Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network

BACKGROUND: Gastric cancer (GC) is one of the most common cancers with high mortality globally. However, the molecular mechanisms of GC are unclear, and the prognosis of GC is poor. Therefore, it is important to explore the underlying mechanisms and screen for novel prognostic markers and treatment...

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Autores principales: Chen, Jian, Wang, Xiuwen, Hu, Bing, He, Yifu, Qian, Xiaojun, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5937478/
https://www.ncbi.nlm.nih.gov/pubmed/29740513
http://dx.doi.org/10.7717/peerj.4692
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author Chen, Jian
Wang, Xiuwen
Hu, Bing
He, Yifu
Qian, Xiaojun
Wang, Wei
author_facet Chen, Jian
Wang, Xiuwen
Hu, Bing
He, Yifu
Qian, Xiaojun
Wang, Wei
author_sort Chen, Jian
collection PubMed
description BACKGROUND: Gastric cancer (GC) is one of the most common cancers with high mortality globally. However, the molecular mechanisms of GC are unclear, and the prognosis of GC is poor. Therefore, it is important to explore the underlying mechanisms and screen for novel prognostic markers and treatment targets. METHODS: The genetic and clinical data of GC patients in The Cancer Genome Atlas (TCGA) was analyzed by weighted gene co-expression network analysis (WGCNA). Modules with clinical significance and preservation were distinguished, and gene ontology and pathway enrichment analysis were performed. Hub genes of these modules were validated in the TCGA dataset and another independent dataset from the Gene Expression Omnibus (GEO) database by t-test. Furthermore, the significance of these genes was confirmed via survival analysis. RESULTS: We found a preserved module consisting of 506 genes was associated with clinical traits including pathologic T stage and histologic grade. PDGFRB, COL8A1, EFEMP2, FBN1, EMILIN1, FSTL1 and KIRREL were identified as candidate genes in the module. Their expression levels were correlated with pathologic T stage and histologic grade, also affected overall survival of GC patients. CONCLUSION: These candidate genes may be involved in proliferation and differentiation of GC cells. They may serve as novel prognostic markers and treatment targets. Moreover, most of them were first reported in GC and deserved further research.
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spelling pubmed-59374782018-05-08 Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network Chen, Jian Wang, Xiuwen Hu, Bing He, Yifu Qian, Xiaojun Wang, Wei PeerJ Bioinformatics BACKGROUND: Gastric cancer (GC) is one of the most common cancers with high mortality globally. However, the molecular mechanisms of GC are unclear, and the prognosis of GC is poor. Therefore, it is important to explore the underlying mechanisms and screen for novel prognostic markers and treatment targets. METHODS: The genetic and clinical data of GC patients in The Cancer Genome Atlas (TCGA) was analyzed by weighted gene co-expression network analysis (WGCNA). Modules with clinical significance and preservation were distinguished, and gene ontology and pathway enrichment analysis were performed. Hub genes of these modules were validated in the TCGA dataset and another independent dataset from the Gene Expression Omnibus (GEO) database by t-test. Furthermore, the significance of these genes was confirmed via survival analysis. RESULTS: We found a preserved module consisting of 506 genes was associated with clinical traits including pathologic T stage and histologic grade. PDGFRB, COL8A1, EFEMP2, FBN1, EMILIN1, FSTL1 and KIRREL were identified as candidate genes in the module. Their expression levels were correlated with pathologic T stage and histologic grade, also affected overall survival of GC patients. CONCLUSION: These candidate genes may be involved in proliferation and differentiation of GC cells. They may serve as novel prognostic markers and treatment targets. Moreover, most of them were first reported in GC and deserved further research. PeerJ Inc. 2018-05-04 /pmc/articles/PMC5937478/ /pubmed/29740513 http://dx.doi.org/10.7717/peerj.4692 Text en ©2018 Chen et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Chen, Jian
Wang, Xiuwen
Hu, Bing
He, Yifu
Qian, Xiaojun
Wang, Wei
Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network
title Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network
title_full Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network
title_fullStr Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network
title_full_unstemmed Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network
title_short Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network
title_sort candidate genes in gastric cancer identified by constructing a weighted gene co-expression network
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5937478/
https://www.ncbi.nlm.nih.gov/pubmed/29740513
http://dx.doi.org/10.7717/peerj.4692
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