Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
Department of Thoracic Surgery, Navy Military Medical University Affiliated Changhai Hospital, Shanghai 200433, China.
Dis Markers. 2021 Jan 6;2021:6696198. doi: 10.1155/2021/6696198. eCollection 2021.
Non-small-cell lung cancer (NSCLC) is one of the most devastating diseases worldwide. The study is aimed at identifying reliable prognostic biomarkers and to improve understanding of cancer initiation and progression mechanisms. RNA-Seq data were downloaded from The Cancer Genome Atlas (TCGA) database. Subsequently, comprehensive bioinformatics analysis incorporating gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the protein-protein interaction (PPI) network was conducted to identify differentially expressed genes (DEGs) closely associated with NSCLC. Eight hub genes were screened out using Molecular Complex Detection (MCODE) and cytoHubba. The prognostic and diagnostic values of the hub genes were further confirmed by survival analysis and receiver operating characteristic (ROC) curve analysis. Hub genes were validated by other datasets, such as the Oncomine, Human Protein Atlas, and cBioPortal databases. Ultimately, logistic regression analysis was conducted to evaluate the diagnostic potential of the two identified biomarkers. Screening removed 1,411 DEGs, including 1,362 upregulated and 49 downregulated genes. Pathway enrichment analysis of the DEGs examined the Ras signaling pathway, alcoholism, and other factors. Ultimately, eight prioritized genes (GNGT1, GNG4, NMU, GCG, TAC1, GAST, GCGR1, and NPSR1) were identified as hub genes. High hub gene expression was significantly associated with worse overall survival in patients with NSCLC. The ROC curves showed that these hub genes had diagnostic value. The mRNA expressions of GNGT1 and NMU were low in the Oncomine database. Their protein expressions and genetic alterations were also revealed. Finally, logistic regression analysis indicated that combining the two biomarkers substantially improved the ability to discriminate NSCLC. GNGT1 and NMU identified in the current study may empower further discovery of the molecular mechanisms underlying NSCLC's initiation and progression.
非小细胞肺癌（NSCLC）是全球最具破坏性的疾病之一。本研究旨在鉴定可靠的预后生物标志物，并深入了解癌症发生和进展的机制。从癌症基因组图谱（TCGA）数据库下载 RNA-Seq 数据。随后，通过综合的生物信息学分析，包括基因本体论（GO）、京都基因与基因组百科全书（KEGG）和蛋白质-蛋白质相互作用（PPI）网络，鉴定与 NSCLC 密切相关的差异表达基因（DEG）。使用分子复合物检测（MCODE）和 cytoHubba 筛选出 8 个枢纽基因。通过生存分析和接收者操作特征（ROC）曲线分析进一步验证枢纽基因的预后和诊断价值。使用 Oncomine、人类蛋白质图谱和 cBioPortal 数据库等其他数据集验证枢纽基因。最终，进行逻辑回归分析评估两个鉴定的生物标志物的诊断潜力。筛选去除了 1411 个 DEG，包括 1362 个上调基因和 49 个下调基因。对 DEG 的通路富集分析检查了 Ras 信号通路、酗酒等因素。最终，确定了 8 个优先基因（GNGT1、GNG4、NMU、GCG、TAC1、GAST、GCGR1 和 NPSR1）为枢纽基因。高枢纽基因表达与 NSCLC 患者总体生存率差显著相关。ROC 曲线显示这些枢纽基因具有诊断价值。在 Oncomine 数据库中，GNGT1 和 NMU 的 mRNA 表达较低。还揭示了它们的蛋白质表达和遗传改变。最后，逻辑回归分析表明，结合这两个生物标志物可以显著提高鉴别 NSCLC 的能力。本研究中鉴定的 GNGT1 和 NMU 可能有助于进一步发现 NSCLC 发生和发展的分子机制。
