DataSheet_1_Gene Coexpression Network Characterizing Microenvironmental Heterogeneity and Intercellular Communication in Pancreatic Ductal Adenocarcinoma: Implications of Prognostic Significance and Therapeutic Target.pdf

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.

背景：胰腺导管腺癌（Pancreatic ductal adenocarcinoma, PDAC）以显著的间质浸润及肿瘤异质性为核心特征。胰腺癌细胞与周围肿瘤微环境（Tumor microenvironment, TME）相互作用，可促进肿瘤发生、导致不良预后及治疗抵抗。本研究旨在阐明反映TME特征的基因网络，并筛选对抗胰腺癌的潜在治疗靶点。
方法：本研究采用经Seurat软件包预处理的单细胞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的相关调控网络与模块；搭建神经网络及风险评分模型以实现预后预测；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抑制剂的抗肿瘤效应。
结果：WGCNA将CCMGs划分为8个基因共表达网络；源自显著模块的TME基因可将PDAC样本分为两类具有迥异预后特征的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健预测患者预后及治疗转归。对调控基因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恶性进展中发挥关键作用。体内实验证实，对ITGA2进行药物抑制可逆转促癌微环境并改善胰腺病变程度。
结论：本研究通过整合单细胞测序数据与WGCNA解析整体转录组，开发了全新的PDAC预后预测策略；靶向核心基因ITGA2可在PDAC小鼠模型中有效抑制肿瘤发生。上述研究结果可为胰腺癌的临床治疗提供新的理论依据与研究思路。