ArticleMolecular cancer2016
Prediction of chemo-response in serous ovarian cancer.
Article in Molecular cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- NETs in ovarian cancer progression: innovative nanoparticle-based therapeutic strategies.European journal of medical research · 2025Review
- Organoid models established from primary tumors and patient-derived xenograft tumors reflect platinum sensitivity of ovarian cancer patients.BMC cancer · 2025Article
- scRNA-seq Can Identify Different Cell Populations in Ovarian Cancer Bulk RNA-seq Experiments.International journal of molecular sciences · 2025Article
- Assessing Random Forest self-reproducibility for optimal short biomarker signature discovery.Briefings in bioinformatics · 2025Article
- Identification of Ovarian High-Grade Serous Carcinoma with Mitochondrial Gene Variation.International journal of molecular sciences · 2025Article
- Enhancing precision medicine: a nomogram for predicting platinum resistance in epithelial ovarian cancer.World journal of surgical oncology · 2024Article
- High-expressed ACAT2 predicted the poor prognosis of platinum-resistant epithelial ovarian cancer.Diagnostic pathology · 2024Article
- A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC.Frontiers in oncology · 2023Article
- Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice.Biomedicines · 2022Review
- Interpatient Heterogeneity in Drug Response and Protein Biomarker Expression of Recurrent Ovarian Cancer.Cancers · 2022Article
- A Fifteen-Gene Classifier to Predict Neoadjuvant Chemotherapy Responses in Patients with Stage IB to IIB Squamous Cervical Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Identification of Novel Fusion Transcripts in High Grade Serous Ovarian Cancer.International journal of molecular sciences · 2021Article
- A polygenic methylation prediction model associated with response to chemotherapy in epithelial ovarian cancer.Molecular therapy oncolytics · 2021Article
- Creation and validation of models to predict response to primary treatment in serous ovarian cancer.Scientific reports · 2021Article
- FOXM1 Inhibition in Ovarian Cancer Tissue Cultures Affects Individual Treatment Susceptibility Ex Vivo.Cancers · 2021Article
- Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors.Cancer informatics · 2021Article
- Dynamic analysis of N-glycomic and transcriptomic changes in the development of ovarian cancer cell line A2780 to its three cisplatin-resistant variants.Annals of translational medicine · 2020Article
- Large-scale integrated analysis of ovarian cancer tumors and cell lines identifies an individualized gene expression signature for predicting response to platinum-based chemotherapy.Cell death & disease · 2019Article
- A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables.International journal of molecular sciences · 2019Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundNearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA.
methodsWe first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets.
resultsThe 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets.
conclusionsThese prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.
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