Department of Geriatric Respiratory and Sleep, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan, China.
Department of Anesthesiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan, China.
Comput Intell Neurosci. 2022 Sep 30;2022:6295934. doi: 10.1155/2022/6295934. eCollection 2022.
The purpose of the present study was to explore the biomarkers related to lung cancer based on the bioinformatics method, which might be new targets for lung cancer treatment.
GSE17681 and GSE18842 were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) in lung cancer samples were screened via the GEO2R online tool. DEMs were submitted to the mirDIP website to predict target genes. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted via uploading DEGs to the DAVID database. The protein-protein interaction network (PPI) of the DEGs was analyzed by STRING's online tool. Then, the PPI network was visualized using Cytoscape 3.8.0.
46 DEMs were identified in GSE17681, and the website predicted that there were 873 target genes of these DEMs. 1029 DEGs were identified in the GSE18842 chip. GO analysis suggested that the co-DEGs participated in the canonical Wnt signaling pathway, regulation of the Wnt signaling pathway, a serine/threonine kinase signaling pathway, the Wnt signaling pathway, and cell-cell signaling by Wnt. KEGG analysis results showed the co-DEGs of GSE17681 and GSE18842 were related to the Hippo signaling pathway and adhesion molecules. In addition, six hub genes that were related to lung cancer were identified as hub genes, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1.
The present study identified six hub genes that were related to lung cancer, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1, which might be a potential target for lung cancer.
本研究旨在通过生物信息学方法探讨与肺癌相关的生物标志物，为肺癌治疗提供新靶点。
从基因表达综合数据库（GEO）中获取 GSE17681 和 GSE18842 数据集。利用 GEO2R 在线工具筛选肺癌样本中的差异表达 miRNA（DEMs）和基因（DEGs）。将 DEMs 提交至 mirDIP 网站预测靶基因。将 DEGs 上传至 DAVID 数据库进行基因本体论（GO）分析和京都基因与基因组百科全书（KEGG）分析。利用 STRING 的在线工具分析 DEGs 的蛋白质-蛋白质相互作用网络（PPI）。然后，使用 Cytoscape 3.8.0 可视化 PPI 网络。
在 GSE17681 中鉴定出 46 个 DEM，网站预测这些 DEM 的靶基因有 873 个。在 GSE18842 芯片中鉴定出 1029 个 DEGs。GO 分析表明，共同差异表达基因参与了经典 Wnt 信号通路、Wnt 信号通路调控、丝氨酸/苏氨酸激酶信号通路、Wnt 信号通路和 Wnt 细胞-细胞信号转导。KEGG 分析结果显示，GSE17681 和 GSE18842 的共同差异表达基因与 Hippo 信号通路和黏附分子有关。此外，还鉴定出 6 个与肺癌相关的关键基因，包括 mTOR、NF1、CHD7、ETS1、IL-6 和 COL1A1。
本研究鉴定出与肺癌相关的 6 个关键基因，包括 mTOR、NF1、CHD7、ETS1、IL-6 和 COL1A1，可能成为肺癌的潜在治疗靶点。
