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Identification of key modules and hub genes for small-cell lung carcinoma and large-cell neuroendocrine lung carcinoma by weighted gene co-expression network analysis of clinical tissue-proteomes

机译:通过临床组织蛋白质组学的加权基因共表达网络分析鉴定小细胞肺癌和大细胞神经内分泌肺癌的关键模块和中心基因

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摘要

Small-cell lung carcinoma (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC) are high-grade lung neuroendocrine tumors (NET). However, comparative protein expression within SCLC and LCNEC remains unclear. Here, protein expression profiles were obtained via mass spectrometry-based proteomic analysis. Weighted gene co-expression network analysis (WGCNA) identified co-expressed modules and hub genes. Of 34 identified modules, six were significant and selected for protein–protein interaction (PPI) network analysis and pathway enrichment. Within the six modules, the activation of cellular processes and complexes, such as alternative mRNA splicing, translation initiation, nucleosome remodeling and deacetylase (NuRD) complex, SWItch/Sucrose Non-Fermentable (SWI/SNF) superfamily-type complex, chromatin remodeling pathway, and mRNA metabolic processes, were significant to SCLC. Modules enriched in processes, including signal recognition particle (SRP)-dependent co-translational protein targeting to membrane, nuclear-transcribed mRNA catabolic process of nonsense-mediated decay (NMD), and cellular macromolecule catabolic process, were characteristically activated in LCNEC. Novel high-degree hub genes were identified for each module. Master and upstream regulators were predicted via causal network analysis. This study provides an understanding of the molecular differences in tumorigenesis and malignancy between SCLC and LCNEC and may help identify potential therapeutic targets.
机译:小细胞肺癌(SCLC)和大细胞神经内分泌肺癌(LCNEC)是高级肺神经内分泌肿瘤(NET)。然而,SCLC和LCNEC中的蛋白质比较表达仍不清楚。在这里,蛋白质表达谱是通过基于质谱的蛋白质组学分析获得的。加权基因共表达网络分析(WGCNA)确定了共表达的模块和中心基因。在确定的34个模块中,有6个是重要的模块,并选择了这些模块进行蛋白质间相互作用(PPI)网络分析和途径富集。在这六个模块中,细胞过程和复合物的激活,例如替代性的mRNA剪接,翻译起始,核小体重塑和脱乙酰酶(NuRD)复合物,SWItch /蔗糖不可发酵(SWI / SNF)超家族型复合物,染色质重塑途径以及mRNA代谢过程对SCLC至关重要。在LCNEC中已激活了丰富的模块,这些模块包括信号识别颗粒(SRP)依赖的共翻译蛋白靶向膜,无义介导的衰变(NMD)的核转录mRNA分解过程以及细胞大分子分解过程。为每个模块鉴定了新型的高度集线器基因。通过因果网络分析来预测主调节器和上游调节器。这项研究提供了对SCLC和LCNEC之间肿瘤发生和恶性肿瘤分子差异的理解,并可能有助于确定潜在的治疗靶点。

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