Islam Rakibul, Ahmed Liton, Paul Bikash Kumar, Ahmed Kawsar, Bhuiyan Touhid, Moni Mohammad Ali
Department of Software Engineering, Daffodil International University (DIU), Ashulia, Savar, Dhaka, 1342, Bangladesh.
Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh.
J Genet Eng Biotechnol. 2021 Mar 19;19(1):43. doi: 10.1186/s43141-021-00134-1.
Worldwide, more than 80% of identified lung cancer cases are associated to the non-small cell lung cancer (NSCLC). We used microarray gene expression dataset GSE10245 to identify key biomarkers and associated pathways in NSCLC.
To collect Differentially Expressed Genes (DEGs) from the dataset GSE10245, we applied the R statistical language. Functional analysis was completed using the Database for Annotation Visualization and Integrated Discovery (DAVID) online repository. The DifferentialNet database was used to construct Protein-protein interaction (PPI) network and visualized it with the Cytoscape software. Using the Molecular Complex Detection (MCODE) method, we identify clusters from the constructed PPI network. Finally, survival analysis was performed to acquire the overall survival (OS) values of the key genes. One thousand eighty two DEGs were unveiled after applying statistical criterion. Functional analysis showed that overexpressed DEGs were greatly involved with epidermis development and keratinocyte differentiation; the under-expressed DEGs were principally associated with the positive regulation of nitric oxide biosynthetic process and signal transduction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway investigation explored that the overexpressed DEGs were highly involved with the cell cycle; the under-expressed DEGs were involved with cell adhesion molecules. The PPI network was constructed with 474 nodes and 2233 connections.
Using the connectivity method, 12 genes were considered as hub genes. Survival analysis showed worse OS value for SFN, DSP, and PHGDH. Outcomes indicate that Stratifin may play a crucial role in the development of NSCLC.
在全球范围内，超过80%已确诊的肺癌病例与非小细胞肺癌（NSCLC）相关。我们使用基因芯片基因表达数据集GSE10245来识别NSCLC中的关键生物标志物和相关通路。
为了从数据集GSE10245中收集差异表达基因（DEG），我们应用了R统计语言。使用注释可视化与整合发现数据库（DAVID）在线资源库完成功能分析。使用DifferentialNet数据库构建蛋白质-蛋白质相互作用（PPI）网络，并使用Cytoscape软件进行可视化。使用分子复合物检测（MCODE）方法，我们从构建的PPI网络中识别出聚类。最后，进行生存分析以获取关键基因的总生存期（OS）值。应用统计标准后，共揭示了1082个DEG。功能分析表明，过表达的DEG与表皮发育和角质形成细胞分化密切相关；低表达的DEG主要与一氧化氮生物合成过程和信号转导的正调控相关。京都基因与基因组百科全书（KEGG）通路研究发现，过表达的DEG与细胞周期高度相关；低表达的DEG与细胞粘附分子相关。构建的PPI网络有474个节点和2233条连接。
使用连通性方法，12个基因被视为枢纽基因。生存分析显示，SFN、DSP和PHGDH的OS值较差。结果表明，Stratifin可能在NSCLC的发展中起关键作用。
