如何利用WGCNA发文章？
论文题目：Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network
论文摘要：
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.
具体分析思路：
1、模块检测与及绘制共表达网络
2、模块与临床特性的相关性
3、将与临床特性相关的模块基因进行GO和KEGG分析
4、对筛选出来的Hub基因做生存分析
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