Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network.
Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network.
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作者：
Chen J;Wang X;Hu B;He Y;Qian X;Wang W
胃癌（GC）是全球最常见的高死亡率癌症之一。然而，GC的分子机制尚不清楚，预后较差。因此，探索潜在的机制和筛选新的预后标志物和治疗靶点是很重要的。采用加权基因共表达网络分析（WGCNA）对癌症基因组图谱（TCGA）中胃癌患者的遗传和临床资料进行分析。区分具有临床意义和保存意义的模块，进行基因本体和通路富集分析。这些模块的枢纽基因在TCGA数据集和另一个独立的基因表达Omnibus （Gene Expression Omnibus， GEO）数据库中通过t检验进行验证。此外，通过生存分析证实了这些基因的意义。我们发现一个由506个基因组成的保存模块与临床特征相关，包括病理T分期和组织学分级。PDGFRB、COL8A1、EFEMP2、FBN1、EMILIN1、FSTL1和KIRREL被鉴定为模块中的候选基因。它们的表达水平与病理T分期和组织学分级相关，也影响GC患者的总生存率。这些候选基因可能参与了胃癌细胞的增殖和分化。它们可以作为新的预后标志物和治疗靶点。其中大部分为GC首次报道，值得进一步研究。
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. 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. 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. 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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WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
4.3
作者：
Langfelder P;Luo R;Oldham MC;Horvath S
通讯作者：
Horvath S
影响因子：
3.8
作者：
Cierna Z;Mego M;Jurisica I;Machalekova K;Chovanec M;Miskovska V;Svetlovska D;Kalavska K;Rejlekova K;Kajo K;Mardiak J;Babal P
通讯作者：
Babal P
影响因子：
64.8
作者：
McKinney EF;Lee JC;Jayne DR;Lyons PA;Smith KG
通讯作者：
Smith KG
影响因子：
3.7
作者：
Allen JD;Xie Y;Chen M;Girard L;Xiao G
通讯作者：
Xiao G