论文题目：
Integrated bioinformatics analysis reveals key candidate
genes and pathways in breast cancer
论文摘要：
Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non‑tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t‑tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein‑protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in ʻcell divisionʼ, the ʻproteinaceous extracellular matrix (ECM)ʼ, ʻECM structural constituentsʼ and ʻECM‑receptor interactionʼ, whereas downregulated genes were mainly enriched in ʻresponse to drugsʼ, ʻextracellular spaceʼ, ʻtranscriptional activator activityʼ and the ʻperoxisome proliferator‑activated receptor signaling pathwayʼ. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2‑a, baculoviral inhibitor of apoptosis repeat‑containing protein 5, cyclin‑dependent kinase 1, G2/mitotic‑specific cyclin‑B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in ʻmitotic nuclear divisionʼ, ʻmid‑bodyʼ, ʻprotein bindingʼ and ʻcell cycleʼ. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC.
论文主要分析内容：
1、差异表达分析
2、GO富集分析
3、Pathway富集分析
4、蛋白互作网络分析
大家可以发现，GEO数据挖掘就是这个套路，下数据，差异表达分析，GO分析，Pathway分析，PPI分析，然后就说得到了重要的关键基因和信号通路，完事。其实，我们还有很多套路组合的，例如做一下WGCNA，或者做一下生存分析。或者联合Oncomine数据挖掘等等，这样的组合又产生很多的套路，这样大家就会觉得套路单一。
希望本文对大家有所帮助。
联系客服
