ArticleFrontiers in oncology2022
Gene Coexpression Network Characterizing Microenvironmental Heterogeneity and Intercellular Communication in Pancreatic Ductal Adenocarcinoma: Implications of Prognostic Significance and Therapeutic Target.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- A multimodal radiomics model to predict disease-free survival in resected non-small cell lung cancer: integrating clinicopathology, dual-energy CT, and deep learning features.Journal of thoracic disease · 2026Article
- Machine learning was used to predict risk factors for distant metastasis of pancreatic cancer and prognosis analysis.Journal of cancer research and clinical oncology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pancreatic 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. Methods: Single-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 Results: WGCNA 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 Conclusion: By 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.
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