通过系统和综合分析，揭示小细胞肺癌和大细胞神经内分泌肺癌中潜在预测性生物标志物的共同和独特特征。
Common and distinct features of potentially predictive biomarkers in small cell lung carcinoma and large cell neuroendocrine carcinoma of the lung by systematic and integrated analysis.
机构信息
Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Department of Oncology, Peking University International Hospital, Beijing, China.
出版信息
Mol Genet Genomic Med. 2020 Mar;8(3):e1126. doi: 10.1002/mgg3.1126. Epub 2020 Jan 25.
BACKGROUND
Large-cell neuroendocrine carcinoma of the lung (LCNEC) and small-cell lung carcinoma (SCLC) are neuroendocrine neoplasms. However, the underlying mechanisms of common and distinct genetic characteristics between LCNEC and SCLC are currently unclear. Herein, protein expression profiles and possible interactions with miRNAs were provided by integrated bioinformatics analysis, in order to explore core genes associated with tumorigenesis and prognosis in SCLC and LCNEC.
METHODS
GSE1037 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in LCNEC and SCLC, as compared with normal lung tissues, were selected using the GEO2R online analyzer and Venn diagram software. Gene ontology (GO) analysis was performed using Database for Annotation, Visualization and Integrated Discovery. The biological pathway analysis was performed using the FunRich database. Subsequently, a protein-protein interaction (PPI) network of DEGs was generated using Search Tool for the Retrieval of Interacting Genes and displayed via Cytoscape software. The PPI network was analyzed by the Molecular Complex Detection app from Cytoscape, and 16 upregulated hub genes were selected. The Oncomine database was used to detect expression patterns of hub genes for validation. Furthermore, the biological pathways of these 16 hub genes were re-analyzed, and potential interactions between these genes and miRNAs were explored via FunRich.
RESULTS
A total of 384 DEGs were identified. A Venn diagram determined 88 common DEGs. The PPI network was constructed with 48 nodes and 221 protein pairs. Among them, 16 hub genes were extracted, 14 of which were upregulated in SCLC samples, as compared with normal lung specimens, and 10 were correlated with the cell cycle pathway. Furthermore, 57 target miRNAs for 8 hub genes were identified, among which 31 miRNAs were correlated with the progression of carcinoma, drug-resistance, radio-sensitivity, or autophagy in lung cancer.
CONCLUSION
This study provided effective biomarkers and novel therapeutic targets for diagnosis and prognosis of SCLC and LCNEC.
背景
肺大细胞神经内分泌癌（LCNEC）和小细胞肺癌（SCLC）是神经内分泌肿瘤。然而，LCNEC 和 SCLC 之间常见和独特遗传特征的潜在机制目前尚不清楚。在此，通过整合生物信息学分析提供了蛋白质表达谱和与 miRNA 可能的相互作用，以探索与 SCLC 和 LCNEC 肿瘤发生和预后相关的核心基因。
方法
从基因表达综合数据库（GEO）数据库中获取 GSE1037 基因表达谱。使用 GEO2R 在线分析器和 Venn 图软件从 GEO 数据库中选择 LCNEC 和 SCLC 与正常肺组织相比的差异表达基因（DEGs）。使用数据库注释、可视化和综合发现（Database for Annotation, Visualization and Integrated Discovery，DAVID）进行基因本体论（GO）分析。使用富恩里奇数据库（FunRich database）进行生物途径分析。随后，使用搜索工具检索相互作用基因（Search Tool for the Retrieval of Interacting Genes）生成 DEGs 的蛋白质-蛋白质相互作用（protein-protein interaction，PPI）网络，并通过 Cytoscape 软件显示。通过 Cytoscape 中的分子复合物检测应用程序分析 PPI 网络，并选择了 16 个上调的枢纽基因。使用 Oncomine 数据库检测枢纽基因的表达模式进行验证。此外，重新分析了这 16 个枢纽基因的生物途径，并通过富恩里奇探索了这些基因与 miRNA 之间的潜在相互作用。
结果
共鉴定出 384 个 DEGs。Venn 图确定了 88 个共同的 DEGs。用 48 个节点和 221 个蛋白质对构建了 PPI 网络。其中，提取了 16 个枢纽基因，其中 14 个在 SCLC 样本中上调，与正常肺标本相比，10 个与细胞周期途径相关。此外，鉴定了 8 个枢纽基因的 57 个靶 miRNA，其中 31 个 miRNA 与肺癌的癌进展、耐药性、放射敏感性或自噬相关。
结论
本研究为 SCLC 和 LCNEC 的诊断和预后提供了有效的生物标志物和新的治疗靶点。
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