Therapeutic Target Identification in Pancreatic Ductal Adenocarcinoma: A Comprehensive In-Silico Study employing WGCNA and Trader
Research Square
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Pancreatic ductal adenocarcinoma (PDAC) is recognized as a highly aggressive fatal disease accounting for more than 90% of all pancreatic malignancies. Considering the limited effective treatment options and its low survival rate, studying PDAC's underlying mechanisms is of utmost importance. The present study focused on investigating PDAC expression data using WGCNA and Trader algorithms to shed light on the underlying mechanisms and identify the most reliable therapeutic candidates in PDAC. After analyzing a recently generated PDAC dataset (GSE132956), the obtained differentially expressed genes (DEGs) were subjected to different exploration steps. WGCNA was applied to cluster the co-expressed DEGs and found the disease's most correlated module and genes. The trader algorithm was utilized to analyze the constructed network of DEGs in STRING and identified the top 30 DEGs whose removal causes a maximum number of separate sub-networks. Hub genes were selected considering the lists of top identified nodes by the two algorithms. "Signaling by Rho GTPases," "Signaling by receptor tyrosine kinases," and "immune system" were top enriched gene ontology terms for the DEGs in the PDAC most correlated module and nine hub genes, including FYN, MAPK3, CDK2, SNRPG, GNAQ, PAK1, LPCAT4, MAP1LC3B, and FBN1 were identified by considering the top spotted DEGs by two algorithms. The findings provided evidence about the involvement of some pathways in the pathogenesis of PDAC and suggested several hub genes as therapeutic candidates via a comprehensive approach analyzing both the co-expression and PPI networks of DEGs in this cancer.
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