基于蛋白互作网络识别非小细胞肺癌相关基因功能模块

Identification of Non-small Cell Lung Cancer-related Gene Functional Module Based on Protein Interaction Network

本研究对非小细胞肺癌(non-small cell lung carcinoma,NSCLC)基因表达数据进行差异表达分析,并与蛋白质相互作用网络(PPIN)数据进行整合,进一步利用Heinz搜索算法识别NSCLC相关的基因功能模块,并对模块中的基因进行功能(GO term)和通路(KEGG)富集分析,旨在探究肺癌发病分子机制。蛋白互作网络分析得到一个包含96个基因和117个相互作用的功能模块,以及8个对NSCLC的发生和发展起到关键作用候选基因标志物。富集分析结果表明,这些基因主要富集于基因转录催化及染色质调控等生物学过程,并在基础转录因子、黏着连接、细胞周期、Wnt信号通路及HTLV-Ⅰ感染等生物学通路中发挥重要作用。本研究对非小细胞肺癌相关的基因和生物学通路进行预测,可用于肺癌的早期诊断和早期治疗,以降低肺癌死亡率。

In order to explore the molecular mechanism of lung cancer,differential expression analysis of non-small cell lung carcinoma(NSCLC)gene expression data was performed and then integrated with protein interaction network(PPIN)data to further identify NSCLC-related gene function module using Heinz search algorithm.Finally,functional(GO term)and pathway(KEGG)enrichment analysis of the genes in the module were performed.From the results of PPIN analysis,a functional module containing 96 genes and 117 interactions was obtained,and 8 candidate gene markers that play key roles in the development and progression of NSCLC were identified.Enrichment analysis results showed that these genes are mainly enriched in biological processes such as gene transcriptional catalysis and chromatin regulation.And it plays an important role in biological pathways such as basal transcription factors,adhesion junctions,cell cycle,Wnt signaling pathway and HTLV-I infection.This study predicts the genes and biological pathways associated with non-small cell lung cancer,which can be used for early diagnosis and early treatment of lung cancer to reduce lung cancer mortality.