ArticleJournal of translational medicine2024
The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
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- BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.Journal of translational medicine · 2025Article
- Targeting PGE2 mediated senescent neuron improves tumor therapy.Neuro-oncology · 2025Article
- Integrating bulk, single-cell, and spatial transcriptomics to identify and functionally validate novel targets to enhance immunotherapy in NSCLC.NPJ precision oncology · 2025Article
- Integrating single-cell RNA-Seq and bulk RNA-Seq data to explore the key role of fatty acid metabolism in hepatocellular carcinoma.Scientific reports · 2025Article
- Machine learning integration with multi-omics data constructs a robust prognostic model and identifies PTGES3 as a therapeutic target for precision oncology in lung adenocarcinoma.Frontiers in immunology · 2025Article
- PTGES3 proteolysis using the liposomal peptide-PROTAC approach.Biology direct · 2024Article
- Advanced machine learning unveils CD8 + T cell genetic markers enhancing prognosis and immunotherapy efficacy in breast cancer.BMC cancer · 2024Article
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches.
methodsDifferentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated.
resultsA novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3.
conclusionsPTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC.
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