<p>Downregulated-miR.</p>

Background Lung cancer exhibits highest incidence among all cancer types worldwide and even after rigorous research and advanced treatment strategies, it constitutes a primary cause of cancer-related mortality. Non-small cell lung cancer is the predominant subtype, constituting the majority of lung cancer cases. Therefore, exploring novel biomarkers is crucial for betterment of diagnostic and therapeutic approaches. Methods The meta-analysis was performed using GEO datasets, to explore the differentially expressed genes (DEGs) and miRNAs (DEMs) in the non-small cell lung cancer (NSCLC) cases. We explored the ChEA database to extract the relevant transcription factors regulating the expression of our hub genes. Further, based on the highest degree of centrality, the feed-forward loop was identified with highest sub-network motif comprising of gene-TF-miRNA. We used pathway and GO term enrichment analysis to determine the importance of these DEGs in different biological processes. Results In NSCLC, we found 950 differentially expressed miRNAs and 1761 genes were recognized exhibiting the significant change in expression (p < 0.05). Further, we investigated the role of sub-network motif in patient survival, hsa-miR-5010 was found to be significantly linked with patient outcome in Lung Adenocarcinoma (LUAD) (p = 0.033) and Lung Squamous Cell Carcinoma (LUSC) (p = 0.013) while SMAD4 (p < 0.001) and NRG1 (p < 0.001) expression exhibited prognostic significance in LUAD cohort only. Conclusion Our data indicated that NRG1-SMAD4-miR-5010-5p was the most prominent sub-network motif engaged in NSCLC patients based on the degree of centrality. In vitro mechanistic studies will provide better understanding on the role of NRG1-SMAD4-miR-5010-5p motif in NSCLC cases.研究背景 肺癌是全球范围内发病率最高的恶性肿瘤，即便经过深入研究与先进治疗策略的探索，其仍是癌症相关死亡的首要病因。非小细胞肺癌（Non-small cell lung cancer, NSCLC）是肺癌最常见的病理亚型，占肺癌总病例数的绝大多数。因此，挖掘新型生物标志物对于优化肺癌的诊断与治疗策略至关重要。 研究方法 本研究采用基因表达综合数据库（Gene Expression Omnibus, GEO）数据集开展荟萃分析，以探究非小细胞肺癌（NSCLC）组织中的差异表达基因（differentially expressed genes, DEGs）与差异表达微小RNA（differentially expressed miRNAs, DEMs）。我们通过转录因子结合富集分析数据库（ChIP-X Enrichment Analysis, ChEA）提取调控核心基因表达的相关转录因子。进一步基于最高中心性得分，筛选出包含基因-转录因子-微小RNA的最高得分亚网络基序，即前馈环路。我们通过通路富集分析与基因本体（Gene Ontology, GO）功能富集分析，明确这些DEGs在不同生物学过程中的核心作用。 研究结果 在NSCLC组织中，共筛选得到950个差异表达微小RNA与1761个差异表达基因，其表达水平均存在显著差异（p<0.05）。进一步探究亚网络基序对患者生存的影响，结果显示hsa-miR-5010的表达水平与肺腺癌（Lung Adenocarcinoma, LUAD）（p=0.033）及肺鳞状细胞癌（Lung Squamous Cell Carcinoma, LUSC）患者的预后均存在显著关联；而SMAD4与NRG1的表达仅在LUAD队列中体现出预后价值（p<0.001）。 研究结论 本研究结果表明，基于中心性得分，NRG1-SMAD4-miR-5010-5p是参与NSCLC发生发展的最关键亚网络基序。后续体外机制研究将进一步阐明该基序在NSCLC中的具体作用机制。