Table_5_Gene Coexpression Network Characterizing Microenvironmental Heterogeneity and Intercellular Communication in Pancreatic Ductal Adenocarcinoma: Implications of Prognostic Significance and Therapeutic Target.xls

BackgroundPancreatic ductal adenocarcinoma (PDAC) is characterized by intensive stromal involvement and heterogeneity. Pancreatic cancer cells interact with the surrounding tumor microenvironment (TME), leading to tumor development, unfavorable prognosis, and therapy resistance. Herein, we aim to clarify a gene network indicative of TME features and find a vulnerability for combating pancreatic cancer.
MethodsSingle-cell RNA sequencing data processed by the Seurat package were used to retrieve cell component marker genes (CCMGs). The correlation networks/modules of CCMGs were determined by WGCNA. Neural network and risk score models were constructed for prognosis prediction. Cell–cell communication analysis was achieved by NATMI software. The effect of the ITGA2 inhibitor was evaluated in vivo by using a KrasG12D-driven murine pancreatic cancer model.
ResultsWGCNA categorized CCMGs into eight gene coexpression networks. TME genes derived from the significant networks were able to stratify PDAC samples into two main TME subclasses with diverse prognoses. Furthermore, we generated a neural network model and risk score model that robustly predicted the prognosis and therapeutic outcomes. A functional enrichment analysis of hub genes governing gene networks revealed a crucial role of cell junction molecule–mediated intercellular communication in PDAC malignancy. The pharmacological inhibition of ITGA2 counteracts the cancer-promoting microenvironment and ameliorates pancreatic lesions in vivo.
ConclusionBy utilizing single-cell data and WGCNA to deconvolute the bulk transcriptome, we exploited novel PDAC prognosis–predicting strategies. Targeting the hub gene ITGA2 attenuated tumor development in a PDAC mouse model. These findings may provide novel insights into PDAC therapy.

研究背景：胰腺导管腺癌（Pancreatic ductal adenocarcinoma, PDAC）以显著的间质浸润与肿瘤异质性为核心特征。胰腺癌细胞与其周围的肿瘤微环境（Tumor Microenvironment, TME）发生相互作用，进而推动肿瘤发生发展、导致不良预后以及治疗抵抗。本研究旨在阐明能够反映TME特征的基因调控网络，并寻找对抗胰腺癌的潜在治疗靶点。
研究方法：本研究采用经Seurat软件包处理的单细胞RNA测序数据，提取细胞组分标记基因（Cell Component Marker Genes, CCMGs）；通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）构建CCMGs的关联网络及模块。分别构建神经网络模型与风险评分模型用于预后预测；借助NATMI软件完成细胞间通讯分析。利用KrasG12D驱动的小鼠胰腺癌模型，在体内评估ITGA2抑制剂的抗肿瘤效果。
研究结果：WGCNA将CCMGs划分为8个基因共表达网络。从显著共表达网络中提取的TME相关基因，可将PDAC样本分为两类具有不同预后特征的TME亚型。此外，本研究构建的神经网络模型与风险评分模型可稳定预测患者预后与治疗结局。对调控基因共表达网络的核心基因进行功能富集分析后发现，细胞连接分子介导的细胞间通讯在PDAC恶性进程中发挥关键作用。体内实验证实，对ITGA2进行药理学抑制可逆转促癌肿瘤微环境，并减轻胰腺病变程度。
研究结论：本研究通过单细胞测序数据结合WGCNA对批量转录组进行细胞组分反卷积分析，开发了全新的PDAC预后预测策略。靶向核心基因ITGA2可在PDAC小鼠模型中抑制肿瘤生长。本研究结果可为胰腺癌治疗提供全新的研究思路与理论依据。