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 t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2018-05-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/4692.pdf |
| _version_ | 1797417734795952128 |
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| author | Jian Chen Xiuwen Wang Bing Hu Yifu He Xiaojun Qian Wei Wang |
| author_facet | Jian Chen Xiuwen Wang Bing Hu Yifu He Xiaojun Qian Wei Wang |
| author_sort | Jian Chen |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T06:23:57Z |
| format | Article |
| id | doaj.art-806373e5dcb94134b178684da0a18aab |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:23:57Z |
| publishDate | 2018-05-01 |
| publisher | PeerJ Inc. |
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| spelling | doaj.art-806373e5dcb94134b178684da0a18aab2023-12-03T11:30:40ZengPeerJ Inc.PeerJ2167-83592018-05-016e469210.7717/peerj.4692Candidate genes in gastric cancer identified by constructing a weighted gene co-expression networkJian Chen0Xiuwen Wang1Bing Hu2Yifu He3Xiaojun Qian4Wei Wang5Department of Chemotherapy, Qilu Hospital, Shandong University, Jinan, Shandong, ChinaDepartment of Chemotherapy, Qilu Hospital, Shandong University, Jinan, Shandong, ChinaDepartment of Chemotherapy, Anhui Provincial Hospital, Hefei, Anhui, ChinaDepartment of Chemotherapy, Anhui Provincial Hospital, Hefei, Anhui, ChinaDepartment of Chemotherapy, Anhui Provincial Hospital, Hefei, Anhui, ChinaDepartment of Chemotherapy, Anhui Provincial Hospital, Hefei, Anhui, ChinaBackground 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.https://peerj.com/articles/4692.pdfGastric cancerWeighted gene co-expression network analysisCandidate geneHistologic gradeOverall survivalPathologic T stage |
| spellingShingle | Jian Chen Xiuwen Wang Bing Hu Yifu He Xiaojun Qian Wei Wang Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network PeerJ Gastric cancer Weighted gene co-expression network analysis Candidate gene Histologic grade Overall survival Pathologic T stage |
| 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 | Gastric cancer Weighted gene co-expression network analysis Candidate gene Histologic grade Overall survival Pathologic T stage |
| url | https://peerj.com/articles/4692.pdf |
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