赣南医学院学报2024，Vol.44Issue(9)：873-879,7.DOI:10.3969/j.issn.1001-5779.2024.09.001
基于网络拓扑分析识别非小细胞肺癌的关键基因
Identification of essential genes of non-small cell lung cancer based on topology network analysis
摘要
Abstract
Objective:To explore potential biomarkers for non-small cell lung cancer(NSCLC)by analyzing the differential genes protein-protein interaction network and weighted gene co-expression network of NSCLC.Methods:Three sets of NSCLC expression profiles were collected from public databases as the research subjects.Firstly,the protein-protein interaction network was constructed using genes that had exhibited differential expression in NSCLC,and essential genes were identified based on the topological structures of the network.Subsequently,the essential genes were further validated using the NSCLC weighted gene co-expression network analysis.Finally,Logistic regression,functional enrichment,survival analysis and other methods were employed to evaluate the potential of essential genes as NSCLC biomarkers.Results:In three independent NSCLC datasets,a screening using differential genes protein-protein interaction networks and weighted gene co-expression networks identified six essential genes associated with NSCLC:CDK1,CCNA2,CDC20,TOP2A,KIF11,and BUB1B.The Logistic regression analysis indicated that these essential genes had significant predictive potential for NSCLC,with an average AUC of 0.945(range:0.895-1)across the three datasets.By performing analysis using the KM-Plotter online survival analysis website,it was found that the expression levels of these six essential genes were all significantly associated with the prognosis of NSCLC patients.Functional enrichment analysis results showed that these genes mainly enriched in cancer-related biological pathways in cell cycle,cellular senescence,etc.Conclusion:Genes CDK1,CCNA2,CDC20,TOP2A,KIF11,and BUB1B are closely associated with the occurrence and development of NSCLC and may serve as potential biomarkers for NSCLC.关键词
癌,非小细胞肺/蛋白质互作网络/加权共表达网络/功能富集/生存分析Key words
Cancer,Non-small cell lung/Protein-protein interaction network/Weighted gene co-expression network analysis(WGCNA)/Functional enrichment/Survival analysis分类
医药卫生引用本文复制引用
葛泳,吴泽童,程桉桉,杨文武,张洁,肖剑虹,陈丹丹,李红东..基于网络拓扑分析识别非小细胞肺癌的关键基因[J].赣南医学院学报,2024,44(9):873-879,7.基金项目
赣南医学院校级课题(ZR2213) （ZR2213）
