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.

中文翻译：

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利用生物信息学和逻辑回归确定 GNGT1 和 NMU 作为非小细胞肺癌的联合诊断生物标志物

非小细胞肺癌（NSCLC）是全球最具破坏性的疾病之一。该研究旨在确定可靠的预后生物标志物，并提高对癌症发生和进展机制的了解。RNA-Seq 数据从癌症基因组图谱 (TCGA) 数据库下载。随后，结合基因本体论（GO）、京都基因和基因组百科全书（KEGG）和蛋白质-蛋白质相互作用（PPI）网络进行综合生物信息学分析，以确定与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 hub 基因。通过生存分析和受试者工作特征（ROC）曲线分析进一步证实了hub基因的预后和诊断价值。Hub 基因通过其他数据集进行了验证，例如 Oncomine、人类蛋白质图谱和 cBioPortal 数据库。最终，进行逻辑回归分析以评估两种已识别生物标志物的诊断潜力。筛选去除了 1,411 个 DEG，其中包括 1,362 个上调基因和 49 个下调基因。DEG 的通路富集分析检查了 Ras 信号通路、酗酒和其他因素。最终，八个优先基因（GNGT1、GNG4、NMU、GCG、TAC1、GAST、GCGR1 和 NPSR1）被确定为中心基因。Hub 基因高表达与 NSCLC 患者总生存率较差显着相关。ROC曲线显示这些枢纽基因具有诊断价值。Oncomine 数据库中 GNGT1 和 NMU 的 mRNA 表达量较低。他们的蛋白质表达和基因改变也被揭示。最后，逻辑回归分析表明，结合两种生物标志物大大提高了区分 NSCLC 的能力。当前研究中鉴定的 GNGT1 和 NMU 可能有助于进一步发现 NSCLC 发生和进展的分子机制。