Zhang Lin, Liu Yuan, Zhuang Jian-Guo, Guo Jie, Li Yan-Tao, Dong Yan, Song Gang
Department of Critical Medicine, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang 050000, Hebei Province, China.
Department of Internal Medicine, Xiongxian Hospital of Traditional Chinese Medicine, Baoding 071800, Hebei Province, China.
World J Clin Cases. 2023 Aug 16;11(23):5504-5518. doi: 10.12998/wjcc.v11.i23.5504.
The objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma (LUAD) bioinformatics analysis, and investigate potential therapeutic targets.
To determine reliable prognostic biomarkers for early diagnosis and treatment of LUAD.
To identify potential therapeutic targets for LUAD, two microarray datasets derived from the Gene Expression Omnibus (GEO) database were analyzed, GSE3116959 and GSE118370. Differentially expressed genes (DEGs) in LUAD and normal tissues were identified using the GEO2R tool. The Hiplot database was then used to generate a volcanic map of the DEGs. Weighted gene co-expression network analysis was conducted to cluster the genes in GSE116959 and GSE118370 into different modules, and identify immune genes shared between them. A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database, then the CytoNCA and CytoHubba components of Cytoscape software were used to visualize the genes. Hub genes with high scores and co-expression were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform enrichment analysis of these genes. The diagnostic and prognostic values of the hub genes were calculated using receiver operating characteristic curves and Kaplan-Meier survival analysis, and gene-set enrichment analysis was conducted. The University of Alabama at Birmingham Cancer data analysis portal was used to analyze relationships between the hub genes and normal specimens, as well as their expression during tumor progression. Lastly, validation of protein expression was conducted on the identified hub genes the Human Protein Atlas database.
Three hub genes with high connectivity were identified; cellular retinoic acid binding protein 2 (CRABP2), matrix metallopeptidase 12 (MMP12), and DNA topoisomerase II alpha (TOP2A). High expression of these genes was associated with a poor LUAD prognosis, and the genes exhibited high diagnostic value.
Expression levels of CRABP2, MMP12, and TOP2A in LUAD were higher than those in normal lung tissue. This observation has diagnostic value, and is linked to poor LUAD prognosis. These genes may be biomarkers and therapeutic targets in LUAD, but further research is warranted to investigate their usefulness in these respects.
本研究的目的是通过生物信息学分析确定参与肺腺癌（LUAD）的关键基因和生物学途径，并研究潜在的治疗靶点。
确定用于LUAD早期诊断和治疗的可靠预后生物标志物。
为了确定LUAD的潜在治疗靶点，分析了来自基因表达综合数据库（GEO）的两个微阵列数据集，即GSE31169和GSE118370。使用GEO2R工具识别LUAD和正常组织中的差异表达基因（DEG）。然后使用Hiplot数据库生成DEG的火山图。进行加权基因共表达网络分析，将GSE11695和GSE118370中的基因聚类到不同模块，并识别它们之间共享的免疫基因。使用检索相互作用基因数据库的搜索工具建立蛋白质-蛋白质相互作用网络，然后使用Cytoscape软件的CytoNCA和CytoHubba组件可视化这些基因。识别出得分高且共表达的关键基因，并使用注释、可视化和综合发现数据库对这些基因进行富集分析。使用受试者工作特征曲线和Kaplan-Meier生存分析计算关键基因的诊断和预后价值，并进行基因集富集分析。使用阿拉巴马大学伯明翰分校癌症数据分析门户分析关键基因与正常标本之间的关系，以及它们在肿瘤进展过程中的表达。最后，在人类蛋白质图谱数据库中对鉴定出的关键基因进行蛋白质表达验证。
鉴定出三个具有高连通性的关键基因；细胞视黄酸结合蛋白2（CRABP2）、基质金属蛋白酶12（MMP12）和DNA拓扑异构酶IIα（TOP2A）。这些基因的高表达与LUAD预后不良相关，且这些基因具有较高的诊断价值。
LUAD中CRABP2、MMP12和TOP2A的表达水平高于正常肺组织。这一观察结果具有诊断价值，且与LUAD预后不良有关。这些基因可能是LUAD的生物标志物和治疗靶点，但需要进一步研究以探讨它们在这些方面的实用性。
