Scholarlink

Background <p>Colorectal cancer (CRC) is a significant contributor to global cancer mortality. Although numerous transcriptomic studies have identified differentially expressed genes (DEGs), few have progressed to experimentally validated, druggable targets. A critical gap lies in bridging large-scale gene network analysis with in silico drug prediction and experimental confirmation to accelerate precision therapy.</p> Methods <p>We applied an integrative systems biology framework combining transcriptomic profiling, protein–protein interaction (PPI) network modeling, enrichment analysis, drug–target prediction, and experimental validation. From the GSE227550 dataset of paired CRC and normal tissues, DEGs were identified, hub genes ranked with CytoHubba, and biological significance assessed via Gene Ontology/Reactome. Expression was confirmed via UALCAN and real-time PCR, while molecular docking evaluated the binding affinities of therapeutically significant ligands.</p> Results <p>The differential analysis identified over 200 DEGs, with ten hub genes situated at the core of the PPI network. Enrichment indicated that these hubs and clusters converge on dysregulated cell cycle control and DNA replication fidelity, while also implicating immune-associated processes and metastatic signaling pathways. TTK, CKS2, TPX2, and MYC emerged as prioritized tumor-associated candidates with potential diagnostic and therapeutic relevance. Molecular docking predicted favorable binding energies for several ligand-target pairs, particularly BAY-1217389 and BAY-1161909 with TTK, CX-4945 for CKS2, and MYCi975 and PROTAC-based degraders for MYC, while dacomitinib targeted TPX2. These compounds should be considered computationally prioritized candidates rather than validated CRC therapeutics, and further biochemical, cellular, and in vivo studies are required to confirm target engagement and therapeutic relevance. Real-time PCR confirmed ~ 50-fold upregulation of TTK in CRC tissues, corroborating computational predictions.</p> Conclusions <p>Rather than proposing these genes as entirely novel CRC-associated genes, this study provides an integrated prioritization framework that combines network centrality, external expression validation, druggability assessment, molecular docking, and RT-PCR confirmation to identify the most translationally relevant candidates, with TTK emerging as a particularly actionable target. Our concept offers a scalable approach for biomarker identification and targeted therapy development in cancer by focusing on druggable CRC drivers and linking them with candidate compounds requiring further validation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Systems biology approach to gene network analysis identifies therapeutic and diagnostic targets in colorectal cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

查看全文
  • Parisa Abedi,
  • Shiva Shiravi,
  • Negar Mottaghi-Dastjerdi,
  • Mohammad Soltany-Rezaee-Rad,
  • Behzad Shahbazi,
  • Nahid Ahmadi,
  • Mohammad-Javad Niazi,
  • Babak Sabet,
  • Vahideh Montazeri,
  • Hamed Montazeri,
  • Reza Baradaran Eftekhari,
  • Elaheh Abdollahi

摘要

Background

Colorectal cancer (CRC) is a significant contributor to global cancer mortality. Although numerous transcriptomic studies have identified differentially expressed genes (DEGs), few have progressed to experimentally validated, druggable targets. A critical gap lies in bridging large-scale gene network analysis with in silico drug prediction and experimental confirmation to accelerate precision therapy.

Methods

We applied an integrative systems biology framework combining transcriptomic profiling, protein–protein interaction (PPI) network modeling, enrichment analysis, drug–target prediction, and experimental validation. From the GSE227550 dataset of paired CRC and normal tissues, DEGs were identified, hub genes ranked with CytoHubba, and biological significance assessed via Gene Ontology/Reactome. Expression was confirmed via UALCAN and real-time PCR, while molecular docking evaluated the binding affinities of therapeutically significant ligands.

Results

The differential analysis identified over 200 DEGs, with ten hub genes situated at the core of the PPI network. Enrichment indicated that these hubs and clusters converge on dysregulated cell cycle control and DNA replication fidelity, while also implicating immune-associated processes and metastatic signaling pathways. TTK, CKS2, TPX2, and MYC emerged as prioritized tumor-associated candidates with potential diagnostic and therapeutic relevance. Molecular docking predicted favorable binding energies for several ligand-target pairs, particularly BAY-1217389 and BAY-1161909 with TTK, CX-4945 for CKS2, and MYCi975 and PROTAC-based degraders for MYC, while dacomitinib targeted TPX2. These compounds should be considered computationally prioritized candidates rather than validated CRC therapeutics, and further biochemical, cellular, and in vivo studies are required to confirm target engagement and therapeutic relevance. Real-time PCR confirmed ~ 50-fold upregulation of TTK in CRC tissues, corroborating computational predictions.

Conclusions

Rather than proposing these genes as entirely novel CRC-associated genes, this study provides an integrated prioritization framework that combines network centrality, external expression validation, druggability assessment, molecular docking, and RT-PCR confirmation to identify the most translationally relevant candidates, with TTK emerging as a particularly actionable target. Our concept offers a scalable approach for biomarker identification and targeted therapy development in cancer by focusing on druggable CRC drivers and linking them with candidate compounds requiring further validation.