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Background <p>Due to late diagnosis and limited treatment options for pancreatic ductal adenocarcinoma (PDAC), identifying novel therapeutic targets is crucial to improving patient outcomes. This study aims to analyze differentially expressed genes (DEGs) in PDAC, identify key hub genes through protein-protein interaction (PPI) network analysis, and propose potential therapeutic targets using drug analysis databases.</p> Methods <p>Gene expression data were obtained from the Gene Expression Omnibus (GEO) database (GSE101448). Gene ontology (GO) and pathway enrichment analyses were conducted using the Enrichr database. The STRING database was used to construct the PPI network, and hub genes were identified via Cytoscape’s CytoHubba plugin. Promoter analysis of hub genes was conducted using the Tomtom and GOMO tools to identify transcription factors potentially regulating these genes. Cytocluster analysis was performed to identify functional clusters within the protein interaction network. Drug target analysis was conducted through the DrugBank database to identify potential therapeutic compounds for the identified hub genes.</p> Results <p>Fifteen hub genes were identified, including TP53, FN1, SRC, PTPRC, CD8A, RRM2, KIF20A, AURKA, CCNB1, BUB1, CDCA3, FAM83D, PIMREG, CDCA2, and CKAP2L. Promoter analysis revealed key transcription factors involved in the regulation of these genes, suggesting their role in PDAC progression. Drug analysis revealed several promising therapeutic candidates, such as Nutlin-3, Siremadlin, Dasatinib, Clofarabine, and Ocriplasmin, which target key regulatory pathways involved in tumor progression and cell cycle dysregulation.</p> Conclusion <p>This study provides valuable insights into the molecular mechanisms underlying pancreatic cancer and identifies potential drug candidates targeting key regulatory genes. Further experimental validation and clinical trials are needed to assess the effectiveness of these identified therapeutic options in improving PDAC treatment outcomes.</p>

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Unraveling the molecular landscape of pancreatic cancer: a systems biology approach to identify therapeutic targets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月27日

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  • Elham Ahmadipour,
  • Sepideh Ebrahimi,
  • Hamid Nasri,
  • Forouzan Amerizadeh

摘要

Background

Due to late diagnosis and limited treatment options for pancreatic ductal adenocarcinoma (PDAC), identifying novel therapeutic targets is crucial to improving patient outcomes. This study aims to analyze differentially expressed genes (DEGs) in PDAC, identify key hub genes through protein-protein interaction (PPI) network analysis, and propose potential therapeutic targets using drug analysis databases.

Methods

Gene expression data were obtained from the Gene Expression Omnibus (GEO) database (GSE101448). Gene ontology (GO) and pathway enrichment analyses were conducted using the Enrichr database. The STRING database was used to construct the PPI network, and hub genes were identified via Cytoscape’s CytoHubba plugin. Promoter analysis of hub genes was conducted using the Tomtom and GOMO tools to identify transcription factors potentially regulating these genes. Cytocluster analysis was performed to identify functional clusters within the protein interaction network. Drug target analysis was conducted through the DrugBank database to identify potential therapeutic compounds for the identified hub genes.

Results

Fifteen hub genes were identified, including TP53, FN1, SRC, PTPRC, CD8A, RRM2, KIF20A, AURKA, CCNB1, BUB1, CDCA3, FAM83D, PIMREG, CDCA2, and CKAP2L. Promoter analysis revealed key transcription factors involved in the regulation of these genes, suggesting their role in PDAC progression. Drug analysis revealed several promising therapeutic candidates, such as Nutlin-3, Siremadlin, Dasatinib, Clofarabine, and Ocriplasmin, which target key regulatory pathways involved in tumor progression and cell cycle dysregulation.

Conclusion

This study provides valuable insights into the molecular mechanisms underlying pancreatic cancer and identifies potential drug candidates targeting key regulatory genes. Further experimental validation and clinical trials are needed to assess the effectiveness of these identified therapeutic options in improving PDAC treatment outcomes.