Evidence map›Paper›PMID 41058670›Full record

ArticleRSC advances2025

Multi-omics pan-cancer profiling of CDK2 and

Md Ahad Ali, Hriddhi Sarker, Tania Khan, Humaira Sheikh, Ahmed Saif, Farhad Bin Farid, Sadia Afrin, Most Asha Khatun, Neeraj Kumar

Abstract read
In one paragraph

Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Md Ahad AliDepartment of Computational Chemistry and Drug Design, Panacea Research Center Bangladesh ahad.chembd@gmail.com.ORCID https://orcid.org/0000-0002-9200-7669
Hriddhi SarkerDepartment of Computational Chemistry and Drug Design, Panacea Research Center Bangladesh ahad.chembd@gmail.com.ORCID https://orcid.org/0009-0007-5394-8777
Tania KhanDepartment of Pharmacy, University of Development Alternative (UODA) Dhaka Bangladesh.
Humaira SheikhDepartment of Computational Chemistry and Drug Design, Panacea Research Center Bangladesh ahad.chembd@gmail.com.ORCID https://orcid.org/0009-0006-6953-8142
Ahmed SaifDepartment of Pharmacy, University of Rajshahi Rajshahi Bangladesh.
Farhad Bin FaridDepartment of Pharmacy, University of Development Alternative (UODA) Dhaka Bangladesh.
Sadia AfrinDepartment of Computational Chemistry and Drug Design, Panacea Research Center Bangladesh ahad.chembd@gmail.com.
Most Asha KhatunDepartment of Computational Chemistry and Drug Design, Panacea Research Center Bangladesh ahad.chembd@gmail.com.
Neeraj KumarDepartment of Pharmaceutical Chemistry, Bhupal Nobles' College of Pharmacy Udaipur 313001 Rajasthan India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Identifiers

PMID41058670
PMCPMC12498278

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.