基于微阵列数据的肺癌相关核心基因的挖掘和鉴定分析

Mining and Identification of Lung Cancer Related Core Genes Based on Microarray Data

背景:肺癌是源于支气管黏膜上皮的恶性肿瘤,发病率和死亡率均居恶性肿瘤首位,由于发病隐匿,早期症状不明显,就诊时大多属晚期,5年总生存率(overall survival, OS)仅为19%,当出现远处转移时其生存率更低。为了筛选肺癌发生发展的潜在基因,本研究从GEO数据库中获得GSE136043和GSE146460进行生物信息学分析。方法:首先,利用GEO2R识别差异表达基因(DEGs),通过GO (Gene Ontology)和KEGG (Kyoto Encyclopedia of Genes and Genomes)分析对差异表达基因(DEGs)进行功能注释。利用STRING工具构建蛋白–蛋白相互作用(PPI)网络,挖掘出最重要的模块和核心基因。结果:共鉴定出73个差异表达基因。差异表达基因的功能变化主要集中在ATP合成偶联的过程,细胞识别,细胞膜、骨架等结构形成和多种酶或转运体如碳水化合物结合和无机阳离子跨膜转运体活性等方面。LYN、TNC、TAGLN、IGFBP1、ANPEP、SERPINE1、IGFBP4、TGFB2基因被鉴定为核心基因。结论:综上所述,本研究中发现的差异表达基因和核心基因有可能成为潜在的诊断和治疗靶点。

Background: Lung cancer is a malignant tumor that originates from bronchial epithelial epithelium. Incidence rate and mortality rate are the highest in malignant tumors. Because of the early onset of symptoms, the majority of patients are late. The overall survival (OS) in five years is only 19%. In order to screen the potential genes for the development of lung cancer, we obtained GSE136043 and GSE146460 from GEO Database for bioinformatics analysis. Methods: Firstly, differential expression genes (DEGs) were identified by geo2r, and their functions were annotated by GO and KEGG analysis. Using STRING tools to construct protein-protein interaction (PPI) network, the most important modules and hub genes were mined. Results: A total of 73 differentially expressed genes were identified. The functional changes of differentially expressed genes mainly focus on the process of ATP synthesis coupling, cell recognition, structure formation of cell membrane and cytoskeleton, and activities of various enzymes or transporters such as carbohydrate binding and inorganic cation transmembrane transporters. LYN, TNC, TAGLN, IGFBP1, ANPEP, SERPINE1, IGFBP4, TGFB2 were identified as hub genes. Conclusion: In conclusion, the differentially expressed genes and core genes found in this study may become potential diagnostic and therapeutic targets.