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  1. Home
  3. Validating Linalool As A Potential Drug For Breast Cancer Treatment Based On Machine Learning And Molecular Docking.
  1. Home
  3. Validating Linalool As A Potential Drug For Breast Cancer Treatment Based On Machine Learning And Molecular Docking.

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Validating linalool as a potential drug for breast cancer treatment based on machine learning and molecular docking.

Qian Zhang1, Dengfeng Chen1

  • 1Galactophore Department, The Second Clinical Medical College, Yangtze University, Jingzhou, China.

Drug Development Research
|June 16, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning identified prognostic biomarkers for breast cancer (BC). Linalool suppressed BC by inhibiting PGK1 expression and activating the PPAR signaling pathway, offering potential new therapeutic targets.

Keywords:
breast cancercell cyclemachine learningmolecular docking

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Area of Science:

  • Oncology
  • Bioinformatics
  • Pharmacology

Background:

  • Breast cancer (BC) is a prevalent malignancy in women, necessitating improved prognostic models and therapeutic strategies.
  • Identifying reliable prognostic biomarkers and understanding novel therapeutic mechanisms are crucial for advancing BC treatment.

Purpose of the Study:

  • To develop a prognostic risk model for breast cancer using machine learning approaches.
  • To identify key prognostic biomarkers for breast cancer.
  • To elucidate the tumor-suppressive mechanism of linalool in breast cancer.

Main Methods:

  • Utilized mRNA microarray and RNA sequencing data from public databases (GSE25055, GSE103091, TCGA-BRCA).
  • Applied univariate COX analysis and multiple machine learning methods to screen prognostic genes and construct risk models.
  • Performed molecular docking, simulations, and in vitro assays (CCK-8, Edu, Transwell, flow cytometry, Western blot) to investigate linalool's mechanism of action against BC cells, focusing on PGK1 and PPAR signaling.

Main Results:

  • Identified eight prognostic genes and constructed a predictive risk model for BC prognosis, with risk score as an independent factor.
  • The model effectively differentiated patient prognosis and revealed distinct immune cell infiltration between high and low-risk groups.
  • High expression of phosphoglycerate kinase 1 (PGK1) in BC correlated with shorter overall survival (OS) and was linked to cell cycle and PPAR signaling pathways.
  • Linalool demonstrated binding affinity with PGK1, inhibited BC cell viability, proliferation, migration, and invasion, promoted apoptosis, and induced G0/G1 cell cycle arrest.
  • Linalool upregulated PPARγ and downregulated PGK1 expression in BC cells.

Conclusions:

  • The developed machine learning-based prognostic model shows promise for predicting BC patient outcomes.
  • Phosphoglycerate kinase 1 (PGK1) is a potential prognostic biomarker and therapeutic target in breast cancer.
  • Linalool exerts significant tumor-suppressive effects in breast cancer by inhibiting PGK1 and activating the PPAR signaling pathway, highlighting its potential as a novel therapeutic agent.