Regenerative Medicine Research Center, Isfahan University of Medical Sciences, Hezar Jerib Avenue, Isfahan, Iran.
Faculty of Advanced Medical Siences, Tabriz University of Medical Sciences, Tabriz, Iran.
Sci Rep. 2024 Oct 7;14(1):23292. doi: 10.1038/s41598-024-74252-4.
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, accounting for over 90% of pancreatic cancers, and is characterized by limited treatment options and poor survival rates. Systems biology provides in-depth insights into the molecular mechanisms of PDAC. In this context, novel algorithms and comprehensive strategies are essential for advancing the identification of critical network nodes and therapeutic targets within disease-related protein-protein interaction networks. This study employed a comprehensive computational strategy using the metaheuristic algorithm Trader to enhance the identification of potential therapeutic targets. Analysis of the expression data from the PDAC dataset (GSE132956) involved co-expression analysis and clustering of differentially expressed genes to identify key disease-associated modules. The STRING database was used to construct a network of differentially expressed genes, and the Trader algorithm pinpointed the top 30 DEGs whose removal caused the most significant network disconnections. Enriched gene ontology terms included "Signaling by Rho GTPases," "Signaling by receptor tyrosine kinases," and "immune system." Additionally, nine hub genes-FYN, MAPK3, CDK2, SNRPG, GNAQ, PAK1, LPCAT4, MAP1LC3B, and FBN1-were identified as central to PDAC pathogenesis. This integrated approach, combining co-expression analysis with protein-protein interaction network analysis using a metaheuristic algorithm, provides valuable insights into PDAC mechanisms and highlights several hub genes as potential therapeutic targets.
胰腺导管腺癌（PDAC）是一种高度侵袭性的恶性肿瘤，占胰腺癌的 90%以上，其治疗选择有限，生存率低。系统生物学为 PDAC 的分子机制提供了深入的见解。在这种情况下，新的算法和全面的策略对于推进疾病相关蛋白质-蛋白质相互作用网络中关键网络节点和治疗靶点的识别至关重要。本研究采用了一种全面的计算策略，使用启发式算法 Trader 来增强对潜在治疗靶点的识别。对 PDAC 数据集（GSE132956）的表达数据进行分析，包括共表达分析和差异表达基因聚类，以识别关键疾病相关模块。使用 STRING 数据库构建差异表达基因网络，并使用 Trader 算法确定去除后导致网络断开最严重的前 30 个差异表达基因。富集的基因本体术语包括“Rho GTPases 的信号转导”、“受体酪氨酸激酶的信号转导”和“免疫系统”。此外，还鉴定了九个枢纽基因-FYN、MAPK3、CDK2、SNRPG、GNAQ、PAK1、LPCAT4、MAP1LC3B 和 FBN1-作为 PDAC 发病机制的核心。这种综合方法结合了共表达分析和使用启发式算法的蛋白质-蛋白质相互作用网络分析，为 PDAC 机制提供了有价值的见解，并突出了几个枢纽基因作为潜在的治疗靶点。
