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

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.

小细胞肺癌（Small-cell lung carcinoma, SCLC）与大细胞神经内分泌肺癌（Large-cell neuroendocrine lung carcinoma, LCNEC）均属于高级别肺神经内分泌肿瘤（lung neuroendocrine tumors, NET）。然而，目前针对二者的比较蛋白质组表达特征仍尚未阐明。本研究通过基于质谱的蛋白质组学分析获取了蛋白质表达谱，借助加权基因共表达网络分析（Weighted gene co-expression network analysis, WGCNA）识别出共表达模块与核心基因（hub gene）。在共计34个被识别的模块中，6个具有显著统计学意义的模块被筛选出来，用于开展蛋白质-蛋白质相互作用（protein–protein interaction, PPI）网络分析与通路富集分析。在上述6个显著模块中，诸多细胞进程与复合物的激活与小细胞肺癌密切相关，包括可变mRNA剪接、翻译起始、核小体重塑与去乙酰化复合物（nucleosome remodeling and deacetylase, NuRD）、SWI/SNF（SWItch/Sucrose Non-Fermentable, SWI/SNF）超家族型复合物、染色质重塑通路以及mRNA代谢进程等。而富集于信号识别颗粒（signal recognition particle, SRP）依赖的共翻译蛋白质靶向膜过程、无义介导的mRNA降解（nonsense-mediated decay, NMD）相关的细胞核转录mRNA分解代谢过程，以及细胞大分子分解代谢过程的模块，则为大细胞神经内分泌肺癌的特征性激活模块。本研究为每个共表达模块识别出了全新的高连接度核心基因，并通过因果网络分析预测得到了主调控因子与上游调控因子。本研究阐明了小细胞肺癌与大细胞神经内分泌肺癌在肿瘤发生与恶性表型层面的分子差异，可为潜在治疗靶点的发掘提供理论支撑。