ArticlePloS one2012
Integrated analysis of gene expression profiles associated with response of platinum/paclitaxel-based treatment in epithelial ovarian cancer.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- The role and application of task-state fNIRS in identifying biomarkers in children and adolescents with ADHD: a systematic review.European child & adolescent psychiatry · 2026Pooled it
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Pooled it
- Identification of an Immune Gene-Based Cisplatin Response Model and CD27 as a Therapeutic Target against Cisplatin Resistance for Ovarian Cancer.Journal of immunology research · 2022Article
- Identifying gene expression patterns associated with drug-specific survival in cancer patients.Scientific reports · 2021Article
- PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients.Frontiers in cell and developmental biology · 2020Article
- Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model.Cancers · 2019Article
- NRP1 is targeted by miR-130a and miR-130b, and is associated with multidrug resistance in epithelial ovarian cancer based on integrated gene network analysis.Molecular medicine reports · 2016Article
- Genetic and molecular analyses reveal G6PC as a key element connecting glucose metabolism and cell cycle control in ovarian cancer.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2015Article
- Article
- Connexin 30 expression inhibits growth of human malignant gliomas but protects them against radiation therapy.Neuro-oncology · 2015Article
- Optimized Prediction of Extreme Treatment Outcomes in Ovarian Cancer.Cancer informatics · 2015Article
- Treatment options in recurrent ovarian cancer: latest evidence and clinical potential.Therapeutic advances in medical oncology · 2014Review
- Machine learning methods in the computational biology of cancer.Proceedings. Mathematical, physical, and engineering sciences · 2014Review
- Eureka-DMA: an easy-to-operate graphical user interface for fast comprehensive investigation and analysis of DNA microarray data.BMC bioinformatics · 2014Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aims to explore gene expression signatures and serum biomarkers to predict intrinsic chemoresistance in epithelial ovarian cancer (EOC). PATIENTS AND
methodsGene expression profiling data of 322 high-grade EOC cases between 2009 and 2010 in The Cancer Genome Atlas project (TCGA) were used to develop and validate gene expression signatures that could discriminate different responses to first-line platinum/paclitaxel-based treatments. A gene regulation network was then built to further identify hub genes responsible for differential gene expression between the complete response (CR) group and the progressive disease (PD) group. Further, to find more robust serum biomarkers for clinical application, we integrated our gene signatures and gene signatures reported previously to identify secretory protein-encoding genes by searching the DAVID database. In the end, gene-drug interaction network was constructed by searching Comparative Toxicogenomics Database (CTD) and literature.
resultsA 349-gene predictive model and an 18-gene model independent of key clinical features with high accuracy were developed for prediction of chemoresistance in EOC. Among them, ten important hub genes and six critical signaling pathways were identified to have important implications in chemotherapeutic response. Further, ten potential serum biomarkers were identified for predicting chemoresistance in EOC. Finally, we suggested some drugs for individualized treatment.
conclusionWe have developed the predictive models and serum biomarkers for platinum/paclitaxel response and established the new approach to discover potential serum biomarkers from gene expression profiles. The potential drugs that target hub genes are also suggested.
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