Multi-omics pan-cancer profiling of CDK2 and <i>in silico</i> identification of plant-derived inhibitors using machine learning approaches

Cancer is a complex disease characterized by uncontrolled cell proliferation, often driven by dysregulated cyclin-dependent kinases (CDKs), particularly CDK2, which plays a crucial role in cell cycle progression. Aberrant CDK2 activity is associated with tumor growth and resistance to therapy, making CDK2 a promising therapeutic target. The main focus of this research is to integrate the multi-omics-based pan-cancer analysis of CDK2 to identify novel plant-derived inhibitors, bridging the prognostic and therapeutic relevance of CDK2 across various cancer types. In this study, to evaluate CDK2's expression, prognostic behavior, genetic alterations, and immune infiltrations, we performed pan-cancer analysis. The oncogenic analysis showed that CDK2 is significantly overexpressed in multiple tumor types and, in some cancers, which correlated with poor overall and disease-free survival, indicating its potential as a context-dependent prognostic biomarker. The involvement of CDK2 in key cell cycle and oncogenic pathways was investigated, highlighting its centrality in tumor proliferation networks. Additionally, cheminformatics and machine learning approaches were applied to screen phytocompounds from six medicinal plants, and the top phytocompounds (>pIC<sub>50</sub> = 5.1) were then subjected to molecular docking, pharmacodynamics, pharmacokinetics, and dynamics simulation studies. Docking results revealed that withanolide M, withanolide K, and ergosterol showed the highest binding affinities against CDK2, with scores of -10.2, -10.1, and -9.9 kcal mol<sup>-1</sup>, respectively. These lead phytocompounds exhibited high potency, excellent pharmacokinetic properties, and minimal predicted toxicity as compared with the control inhibitor of CDK2. The binding stability of the protein-ligand complexes was confirmed by dynamic simulations along with MM-GBSA calculations, with the results supporting our previously reported affinity score. Therefore, these phytocompounds could be potential CDK2 inhibitors, warranting exploration in future cancer research. Furthermore, additional experimental and clinical validations are required to confirm the efficacy and efficiency of these potential lead compounds.

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Publication Details

Journal
RSC Advances
Published
2025-01-01
DOI
https://doi.org/10.1039/d5ra05535k
Citations
5
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
4.03
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article

Multi-omics pan-cancer profiling of CDK2 and in silico identification of plant-derived inhibitors using machine learning approaches

Neeraj Kumar, Sadia Afrin, Md. Ahad Ali, Hillol Sarker et al.
5 citations
RSC Advances
Bioinformatics and Genomic Networks
4.03
article

Multi-omics pan-cancer profiling of CDK2 and in silico identification of plant-derived inhibitors using machine learning approaches

Neeraj Kumar, Sadia Afrin, Md. Ahad Ali, Hillol Sarker, F. Farid, Ahmed Saif, Humaira Sheikh, Tania Khan, Most. Asha Khatun
article en
5 citations

Abstract

Cancer is a complex disease characterized by uncontrolled cell proliferation, often driven by dysregulated cyclin-dependent kinases (CDKs), particularly CDK2, which plays a crucial role in cell cycle progression. Aberrant CDK2 activity is associated with tumor growth and resistance to therapy, making CDK2 a promising therapeutic target. The main focus of this research is to integrate the multi-omics-based pan-cancer analysis of CDK2 to identify novel plant-derived inhibitors, bridging the prognostic and therapeutic relevance of CDK2 across various cancer types. In this study, to evaluate CDK2's expression, prognostic behavior, genetic alterations, and immune infiltrations, we performed pan-cancer analysis. The oncogenic analysis showed that CDK2 is significantly overexpressed in multiple tumor types and, in some cancers, which correlated with poor overall and disease-free survival, indicating its potential as a context-dependent prognostic biomarker. The involvement of CDK2 in key cell cycle and oncogenic pathways was investigated, highlighting its centrality in tumor proliferation networks. Additionally, cheminformatics and machine learning approaches were applied to screen phytocompounds from six medicinal plants, and the top phytocompounds (>pIC<sub>50</sub> = 5.1) were then subjected to molecular docking, pharmacodynamics, pharmacokinetics, and dynamics simulation studies. Docking results revealed that withanolide M, withanolide K, and ergosterol showed the highest binding affinities against CDK2, with scores of -10.2, -10.1, and -9.9 kcal mol<sup>-1</sup>, respectively. These lead phytocompounds exhibited high potency, excellent pharmacokinetic properties, and minimal predicted toxicity as compared with the control inhibitor of CDK2. The binding stability of the protein-ligand complexes was confirmed by dynamic simulations along with MM-GBSA calculations, with the results supporting our previously reported affinity score. Therefore, these phytocompounds could be potential CDK2 inhibitors, warranting exploration in future cancer research. Furthermore, additional experimental and clinical validations are required to confirm the efficacy and efficiency of these potential lead compounds.

RSC AdvancesVol. 15(44)
Panacea Biotec (India) (IN), Gopalganj Science and Technology University (BD), Pabna University of Science and Technology (BD), University of Development Alternative (BD), University of Rajshahi (BD)
Openalex Percentile: Top 6%
Bioinformatics and Genomic Networks
4.03
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