ArticleJournal, genetic engineering & biotechnology2026
Integrative multi-transcriptomics and network pharmacology reveal natural therapeutics as anti-cancer agents targeting AURKA for ovarian cancer treatment.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What Socratic holds
Registered trials
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.