基于整合生物信息学分析的非小细胞肺癌关键生物标志物和候选分子的鉴定。

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most prevalent malignant tumor of the lung cancer, for which the molecular mechanisms remain unknown. In this study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC by bioinformatics analysis. METHODS: From the Gene Expression Omnibus database, GSE118370 and GSE10072 microarray datasets were obtained. Identifying the differentially expressed genes (DEGs) between lung adenocarcinoma and normal samples was done. By using bioinformatics tools, a protein-protein interaction (PPI) network was constructed, modules were analyzed, and enrichment analyses were performed. The expression and prognostic values of 14 hub genes were validated by the GEPIA database, and the correlation between hub genes and survival in lung adenocarcinoma was assessed by UALCAN, , String and Cytoscape, and Timer tools. RESULTS: We found three genes (PIK3R1, SPP1, and PECAM1) that have a clear correlation with OS in the lung adenocarcinoma patient. It has been found that lung adenocarcinoma exhibits high expression of SPP1 and that this has been associated with poor prognosis, while low expression of PECAM1 and PIK3R1 is associated with poor prognosis ( < 0.05). We also found that the expression of SPP1 was associated with miR-146a-5p, while the high expression of miR-146a-5p was related to good prognosis ( < 0.05). On the contrary, the lower miR-21-5p on upstream of PIK3R1 is associated with a higher surviving rate in cancer patients ( < 0.05). Finally, we found that the immune checkpoint genes CD274(PD-L1) and PDCD1LG2(PD-1) were also related to SPP1 in lung adenocarcinoma. CONCLUSIONS: The results indicated that SPP1 is a cancer promoter (oncogene), while PECAM1 and PIK3R1 are cancer suppressor genes. These genes take part in the regulation of biological activities in lung adenocarcinoma, which provides a basis for improving detection and immunotherapeutic targets for lung adenocarcinoma.

背景：非小细胞肺癌（NSCLC）是最常见的肺癌恶性肿瘤，其发病机制尚不清楚。本研究通过生物信息学分析，旨在找到与 NSCLC 发病机制相关的新型生物标志物，为 NSCLC 提供新的诊断和治疗方法。

方法：从基因表达综合数据库（GEO）中获取 GSE118370 和 GSE10072 微阵列数据集。识别肺腺癌与正常样本之间的差异表达基因（DEGs）。利用生物信息学工具构建蛋白质-蛋白质相互作用（PPI）网络，分析模块，并进行富集分析。利用 GEPIA 数据库验证 14 个枢纽基因的表达和预后价值，利用 UALCAN、String 和 Cytoscape 以及 Timer 工具评估枢纽基因与肺腺癌患者生存的相关性。

结果：我们发现了三个与肺腺癌患者 OS 有明显相关性的基因（PIK3R1、SPP1 和 PECAM1）。研究发现，肺腺癌中 SPP1 表达较高，与预后不良有关，而 PECAM1 和 PIK3R1 表达较低与预后不良有关（<0.05）。我们还发现 SPP1 的表达与 miR-146a-5p 有关，而 miR-146a-5p 的高表达与预后良好有关（<0.05）。相反，PIK3R1 上游 miR-21-5p 的低表达与癌症患者的生存率提高有关（<0.05）。最后，我们发现免疫检查点基因 CD274（PD-L1）和 PDCD1LG2（PD-1）也与肺腺癌中的 SPP1 有关。

结论：结果表明，SPP1 是一种癌促进基因（癌基因），而 PECAM1 和 PIK3R1 是抑癌基因。这些基因参与了肺腺癌生物活性的调节，为提高肺腺癌的检测和免疫治疗靶点提供了依据。