Evidence map›Paper›PMID 42749415›Full record

ArticleJournal, genetic engineering & biotechnology2026

Integrative multi-transcriptomics and network pharmacology reveal natural therapeutics as anti-cancer agents targeting AURKA for ovarian cancer treatment.

Hriddhi Sarker, Md Ahad Ali, Md Fakhrul Islam, Enam Ahmed, Amlan Ganguly, Md Nazmul Hasan Zilani

Abstract read
In one paragraph

Article in Journal, genetic engineering & biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Hriddhi SarkerDepartment of Biochemistry & Molecular Biology, University of Rajshahi, Rajshahi 6205, Bangladesh; Department of Computational Chemistry and Drug Design, Panacea Research Center, Rajshahi 6206, Bangladesh. Electronic address: hriddhisarkerborno@gmail.com.
Md Ahad AliDepartment of Computational Chemistry and Drug Design, Panacea Research Center, Rajshahi 6206, Bangladesh; Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh. Electronic address: ahad.chembd@gmail.com.
Md Fakhrul IslamDepartment of Computational Chemistry and Drug Design, Panacea Research Center, Rajshahi 6206, Bangladesh. Electronic address: fakhrulislam.fn180@gmail.com.
Enam AhmedDepartment of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Kumargaon, Sylhet 3114, Bangladesh. Electronic address: enam41929@bau.edu.bd.
Amlan GangulyDepartment of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka 1000, Bangladesh. Electronic address: gangulyamlan@du.ac.bd.
Md Nazmul Hasan ZilaniDepartment of Pharmacy, Faculty of Biological Science and Technology, Jashore University of Science and Technology, Jashore 7408, Bangladesh. Electronic address: zilani.phar@just.edu.bd.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer (OC) remains one of the most lethal gynecologic malignancies, largely because it is often diagnosed late, recurs frequently, and develops resistance to conventional chemotherapy. These challenges emphasize the urgent need for novel and less toxic therapeutic strategies. In this study, we applied an integrative in silico workflow combining transcriptomic analysis, network biology, pan-cancer exploration, and computational drug discovery to identify potential phytochemical inhibitors targeting Aurora kinase A (AURKA) in OC. Differential gene expression analysis of three GEO datasets (GSE14407, GSE18520, and GSE26712) identified common dysregulated genes associated with OC progression. Functional enrichment and regulatory network analyses revealed strong involvement of these genes in cell-cycle regulation, mitotic progression, and cancer-related signaling pathways. Protein-protein interaction analysis identified AURKA as a central hub gene with high network connectivity and strong overexpression across multiple cancer types. Further pan-cancer analyses demonstrated significant associations of AURKA with immune infiltration patterns and recurrent genetic alterations, supporting its therapeutic relevance. After removing duplicates from the 6834 collected phytochemicals, a total of 2046 unique compounds derived from medicinal plants of the Indian subcontinent were screened against the AURKA crystal structure using molecular docking to identify potential inhibitors. Several compounds demonstrated binding affinities comparable to the reference inhibitor Y3M. Subsequent drug-likeness, ADMET, toxicity prediction, molecular dynamics simulation, MM-GBSA binding energy analysis, and quantum mechanical evaluations identified CID-87014 (2-(Hydroxymethyl)anthraquinone) as the most promising candidate. This compound exhibited stable protein-ligand interactions, favorable binding free energy, acceptable predicted pharmacokinetic properties, and balanced electronic stability throughout the analyses. Although the findings are computational and require experimental validation, CID-87014 emerged as a promising lead compound targeting AURKA and may serve as a foundation for future ovarian cancer drug development.

Indexed as

MD simulationMolecular dockingNetwork pharmacologyOvarian CancerPan-cancer analysis

Identifiers

PMID42749415
PMCPMC13314747

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.