Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis.
作者： Yexun Song , Wenfang Tang , Hui Li
DOI: 10.1042/BSR20203973
关键词: Real-time polymerase chain reaction 、 Survival analysis 、 Gene 、 CENPF 、 Biomarker (medicine) 、 Computational biology 、 Lung cancer 、 Adenocarcinoma 、 Biology 、 KIF4A
摘要: Background: Lung adenocarcinoma (LUAD) is the most frequent histological type of lung cancer, and its incidence has displayed an upward trend in recent years. Nevertheless, little known regarding effective biomarkers for LUAD. Methods: The robust rank aggregation method was used to mine differentially expressed genes (DEGs) from gene expression omnibus (GEO) datasets. Search Tool Retrieval Interacting Genes (STRING) database extract hub protein–protein interaction (PPI) network. validated using profiles TCGA Oncomine databases verified by real-time quantitative PCR (qRT-PCR). module survival analyses were determined Cytoscape Kaplan–Meier curves. function KIF4A as a investigated LUAD cell lines. Results: PPI analysis identified seven DEGs including BIRC5, DLGAP5, CENPF, KIF4A, TOP2A, AURKA, CCNA2, which significantly upregulated datasets, qRT-PCR our clinical samples. We overall disease-free GEPIA. further found that expressions positively correlated with stage. In lines, proliferation migration inhibited apoptosis promoted knocking down expression. Conclusion: have new functional pathways involved LUAD. gene, progression might represent potential therapeutic target molecular cancer therapy.
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