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, cBioPortal, 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 (P < 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 (P < 0.05). On the contrary, the lower miR-21-5p on upstream of PIK3R1 is associated with a higher surviving rate in cancer patients (P < 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）是肺癌中最常见的恶性肿瘤，其分子机制仍不清楚。在本研究中，我们鉴定了与NSCLC发病机制相关的新型生物标志物，旨在通过生物信息学分析为NSCLC提供新的诊断和治疗方法。方法。从基因表达综合数据库中获得了 GSE118370 和 GSE10072 微阵列数据集。鉴定了肺腺癌和正常样本之间的差异表达基因（DEG）。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。通过 GEPIA 数据库验证了 14 个 hub 基因的表达和预后价值，并通过 UALCAN、 cBioPortal、String 和 Cytoscape 以及 Timer 工具评估了 hub 基因与肺腺癌生存之间的相关性。结果。我们发现三个基因（PIK3R1、SPP1 和 PECAM1）与肺腺癌患者的 OS 具有明显的相关性。研究发现，肺腺癌中SPP1高表达且预后不良，而PECAM1和PIK3R1低表达则与预后不良相关（P < 0.05）。我们还发现 SPP1 的表达与 miR-146a-5p 相关，而 miR-146a-5p 的高表达与良好的预后相关（P < 0.05）。相反，PIK3R1 上游的 miR-21-5p 较低，癌症患者的生存率较高（P < 0.05）。最后，我们发现肺腺癌中免疫检查点基因CD274（PD-L1）和PDCD1LG2（PD-1）也与SPP1相关。结论。结果表明，SPP1是癌症促进基因（癌基因），而PECAM1和PIK3R1是癌症抑制基因。这些基因参与肺腺癌生物活性的调控，为改进肺腺癌的检测和免疫治疗靶点提供了基础。