Network-guided identification and multi-targeting of cell cycle regulatory hubs in obesity-linked colorectal cancer

Purpose: Obesity-associated colorectal cancer (CRC) involves complex molecular networks that limit the effectiveness of single-target therapies. This study aimed to develop an integrative computational pipeline for identifying key regulatory hubs and designing a multi-target-directed ligand (MTDL) capable of simultaneously modulating critical components of the CRC interactome.Materials and Methods: Differential gene expression analysis between CRC and corresponding healthy tissue-derived samples identified upregulated genes in colon adenocarcinoma. A high-confidence protein–protein interaction network was constructed from these genes to identify central hub proteins using topological analysis. To target these hubs, structure-based de novo ligand design and molecular docking were employed to evaluate binding affinities and multi-target potential. Finally, the candidate MTDL underwent in silico pharmacokinetic (ADME) and toxicity profiling, supplemented by a similarity search within natural compound libraries.Results: A total of 786 upregulated genes were identified, of which 204 formed a dense functional interactome. Three central hubs (CDC6, CCNB1, and CDC25C), all critical to cell cycle regulation, were prioritized as therapeutic targets. The designed MTDL demonstrated favorable binding affinities across all targets, interacting with functionally conserved domains. Furthermore, the ligand exhibited a favorable drug-like profile, characterized by high gastrointestinal absorption and a low toxicity risk. Screening of natural product libraries yielded several analogs with comparable binding profiles, supporting the chemical feasibility of the proposed scaffold.Conclusion: This study presents a network-guided framework for multi-target drug design in CRC. By shifting from a "one-target, one-drug" model to a systems-level approach, the proposed MTDL and its natural analogs represent promising candidates for experimental validation in the treatment of complex, obesity-associated CRC.

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Publication Details

Journal
Çukurova medical journal (Online)/Çukurova medical journal
Published
2026-09-30
DOI
https://doi.org/10.17826/cumj.1940376
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Network-guided identification and multi-targeting of cell cycle regulatory hubs in obesity-linked colorectal cancer

Merve Uca, Athanasia Pavlopoulou, Hüseyin Güner, Reşat Ünal et al.
Çukurova medical journal (Online)/Çukurova medical journal
Computational Drug Discovery Methods
article

Network-guided identification and multi-targeting of cell cycle regulatory hubs in obesity-linked colorectal cancer

Merve Uca, Athanasia Pavlopoulou, Hüseyin Güner, Reşat Ünal, A. Georgakilas, Halil İbrahim Pazarbaşı
article en

Abstract

Purpose: Obesity-associated colorectal cancer (CRC) involves complex molecular networks that limit the effectiveness of single-target therapies. This study aimed to develop an integrative computational pipeline for identifying key regulatory hubs and designing a multi-target-directed ligand (MTDL) capable of simultaneously modulating critical components of the CRC interactome.Materials and Methods: Differential gene expression analysis between CRC and corresponding healthy tissue-derived samples identified upregulated genes in colon adenocarcinoma. A high-confidence protein–protein interaction network was constructed from these genes to identify central hub proteins using topological analysis. To target these hubs, structure-based de novo ligand design and molecular docking were employed to evaluate binding affinities and multi-target potential. Finally, the candidate MTDL underwent in silico pharmacokinetic (ADME) and toxicity profiling, supplemented by a similarity search within natural compound libraries.Results: A total of 786 upregulated genes were identified, of which 204 formed a dense functional interactome. Three central hubs (CDC6, CCNB1, and CDC25C), all critical to cell cycle regulation, were prioritized as therapeutic targets. The designed MTDL demonstrated favorable binding affinities across all targets, interacting with functionally conserved domains. Furthermore, the ligand exhibited a favorable drug-like profile, characterized by high gastrointestinal absorption and a low toxicity risk. Screening of natural product libraries yielded several analogs with comparable binding profiles, supporting the chemical feasibility of the proposed scaffold.Conclusion: This study presents a network-guided framework for multi-target drug design in CRC. By shifting from a "one-target, one-drug" model to a systems-level approach, the proposed MTDL and its natural analogs represent promising candidates for experimental validation in the treatment of complex, obesity-associated CRC.

Çukurova medical journal (Online)/Çukurova medical journalVol. 51(3)
National Technical University of Athens (GR), Dokuz Eylül University (TR), Muğla University (TR), Abdullah Gül University (TR)
Good health and well-being
Openalex Percentile: Top 9%
Computational Drug Discovery Methods
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