Gene Co-expression Network Characterizing Microenvironmental Heterogeneity and Intercellular Communication in Pancreatic Ductal Adenocarcinoma: Implications of Prognostic Significance and Therapeutic Target
Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is characterized by intensive stroma involvement and heterogeneity. Pancreatic cancer cells interplay with surrounding tumor micro-environment (TME), leading to exacerbated tumorigenesis, dismal prognosis and tenacious therapy resistance. Herein, we aim to ascertain a gene-network indicative of vicious features of TME, then find a vulnerability for pancreatic cancer. Methods Single cell RNA sequencing data was processed by Seurat package, retrieving the cell component marker genes (CCMGs). Correlation networks/modules of CCMGs were determined by WGCNA algorithm in a combined PDAC mRNA expression dataset. The gene modules that statistically associate with prognosis were chosen for classifying TME subgroups, constructing neural network and designing the risk score system. Cell-cell communication analysis was achieved by NATMI software. The tumor suppressive effect of ITGA2 inhibitor was evaluated in vivo by using a Kras G12D -driven murine pancreatic cancer model.Results WGCNA analysis categorized cell component marker genes into eight co-expression networks. From gene modules with the maximum and minimum hazard ratio, we stratify PDAC samples based on TME gene patterns, resulting in two main TME subclasses with contrasting survival periods. Furthermore, we generated a neural network model and a risk score model which robustly predict prognosis and therapeutic outcomes. The hub genes in both gene modules were also gathered for functional enrichment analysis, elucidating a crucial role of cell communication-mediating integrins in TME associated PDAC malignancy. To perform a confirmatory experiment underpinning the significance of hub gene targeting, the mice with spontaneously developed pancreatic cancer were orally treated with an integrin inhibitor. The in vivo assays unraveled that pharmacologically inhibiting ITGA2 counteracts cancer-promoting micro-environment, and ameliorates pancreatic lesions. Conclusions By recapitulating gene-network across various cell types, we exploited novel PDAC prognosis-predicting strategies. Medically interfering ITGA2, a key factor guiding cellular reciprocal interaction, attenuated tumor development. These findings may open new avenue about PDAC targeting therapy.