Level D· Scientific groundwork from lab and animal studiesLaboratory StudyEurope PMCOpen access

Unraveling the molecular mechanisms of paclitaxel in high-grade serous ovarian cancer through network pharmacology

Pei Y., Yang Z., Li B., Chen X., Mao Y., Ding Y.

Laboratory Study on Hip, published in Sci Rep (2025) — summary generated from the PubMed abstract.

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Level D· Scientific groundwork from lab and animal studiesEvidence level of this study

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

  • Level A · Stronger Clinical Evidence
  • Level B · Emerging clinical evidence with positive signals
  • Level C · Early human research exploring benefits
  • Level D · Scientific groundwork from lab and animal studies
  • Emerging · Emerging topic under active research
Read the A–D evidence level guide

This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.

Study type
Laboratory Study
Journal
Sci Rep (2025)
Reported sample size
—
Source database
Europe PMC
PMID
40355485
PMCID
PMC12069709
DOI
10.1038/s41598-025-00658-3
Citations
2

Abstract (original English)

High-grade serous ovarian cancer (HGSOC) is the most common and aggressive subtype of epithelial ovarian cancer, often diagnosed at advanced stages with a poor prognosis. Paclitaxel (PTX), a standard chemotherapeutic agent for HGSOC, exerts cytotoxic effects on cancer cells and modulates the tumor microenvironment. This study aimed to elucidate the molecular mechanisms of PTX in HGSOC using bioinformatics, machine learning, network pharmacology, and molecular docking, to identify potential diagnostic biomarkers and therapeutic targets. We identified differentially expressed genes (DEGs) between HGSOC and normal ovarian tissues using the GSE54388 dataset from the Gene Expression Omnibus database. The intersection of these DEGs with PTX targets, identified from the Swiss Target Prediction database, yielded 15 overlapping genes. These genes were analyzed via protein-protein interaction (PPI) network analysis to identify significant interaction relationships. Kaplan-Meier survival analysis was then performed to assess the prognostic significance of these genes. Their protein expression patterns in HGSOC tissues were validated using the Human Protein Atlas (HPA) database. Functional enrichment analysis was conducted using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. A combined diagnostic model was developed using LASSO regression and validated in two independent ex

What this study does not prove

  • • This study does not prove SVF is an approved treatment or a replacement for standard care.
  • • This is preclinical work; animal or laboratory results cannot be applied to humans.

Evidence level

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

How we grade evidence
HumansCystadenocarcinoma, SerousOvarian NeoplasmsPaclitaxelAntineoplastic Agents, PhytogenicPrognosisComputational BiologyGene Expression Regulation, NeoplasticFemaleGene Regulatory Networks

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