ArticleFrontiers in oncology2014
Gene expression analysis in ovarian cancer - faults and hints from DNA microarray study.
Article in Frontiers in oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 4 of them syntheses that pooled it.
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Who cites it
70 citing papers in PubMed, 4 syntheses or guidelines pooled it, 93 citations in OpenAlex.
- Suppression of ARID1A associated with decreased CD8 T cells improves cell survival of ovarian clear cell carcinoma.Journal of gynecologic oncology · 2021Pooled it
- Increased expression of TET3 predicts unfavorable prognosis in patients with ovarian cancer-a bioinformatics integrative analysis.Journal of ovarian research · 2019Pooled it
- High mRNA expression level of IL-6R was associated with better prognosis for patients with ovarian cancer: a pooled meta-analysis.Scientific reports · 2017Pooled it
- Pooled it
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- Multi-omics integration and machine learning define an iron-sulfur cluster/zinc-binding protein prognostic signature in esophageal squamous cell carcinoma.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
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- Systems-level analysis identifies IRF6 as an inhibitor of epithelial-mesenchymal transition.Journal of biological engineering · 2026Article
- Reconstruction of septin higher-order nano-size structures in ovarian cancer cells uncover susceptibility to the septin-targeting small molecule UR214-9.bioRxiv : the preprint server for biology · 2026Article
- NEK1 Promotes Ovarian Cancer Progression via p53 Suppression While Enhancing Sensitivity to Genotoxic Therapy.Current issues in molecular biology · 2026Article
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- Investigating prognostic features in high-grade serous ovarian cancer through gene regulatory network inference with single-cell transcriptomic profiles.Scientific reports · 2025Article
- Apoptosis-inducing factor (AIF) at the crossroad of cell survival and cell death: implications for cancer and mitochondrial diseases.Cell communication and signaling : CCS · 2025Review
- Integrated analysis of single-cell and bulk transcriptomics reveals the prognostic value and underlying mechanisms of crotonylation in ovarian cancer.Frontiers in immunology · 2025Article
- Deciphering Folate Receptor alphaGene Expression and mRNA Signatures in Ovarian Cancer: Implications for Precision Therapies.International journal of molecular sciences · 2024Article
- Low FHL1 expression indicates a good prognosis and drug sensitivity in ovarian cancer.Functional & integrative genomics · 2024Article
- Signatures of tumor-associated macrophages correlate with treatment response in ovarian cancer patients.Aging · 2024Article
- Comprehensive analysis based on glycolytic and glutaminolytic pathways signature for predicting prognosis and immunotherapy in ovarian cancer.Journal of Cancer · 2024Article
- Mitophagy genes in ovarian cancer: a comprehensive analysis for improved immunotherapy.Discover oncology · 2023Article
- BDP1 as a biomarker in serous ovarian cancer.Cancer medicine · 2023Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The introduction of microarray techniques to cancer research brought great expectations for finding biomarkers that would improve patients' treatment; however, the results of such studies are poorly reproducible and critical analyses of these methods are rare. In this study, we examined global gene expression in 97 ovarian cancer samples. Also, validation of results by quantitative RT-PCR was performed on 30 additional ovarian cancer samples. We carried out a number of systematic analyses in relation to several defined clinicopathological features. The main goal of our study was to delineate the molecular background of ovarian cancer chemoresistance and find biomarkers suitable for prediction of patients' prognosis. We found that histological tumor type was the major source of variability in genes expression, except for serous and undifferentiated tumors that showed nearly identical profiles. Analysis of clinical endpoints [tumor response to chemotherapy, overall survival, disease-free survival (DFS)] brought results that were not confirmed by validation either on the same group or on the independent group of patients. CLASP1 was the only gene that was found to be important for DFS in the independent group, whereas in the preceding experiments it showed associations with other clinical endpoints and with BRCA1 gene mutation; thus, it may be worthy of further testing. Our results confirm that histological tumor type may be a strong confounding factor and we conclude that gene expression studies of ovarian carcinomas should be performed on histologically homogeneous groups. Among the reasons of poor reproducibility of statistical results may be the fact that despite relatively large patients' group, in some analyses one has to compare small and unequal classes of samples. In addition, arbitrarily performed division of samples into classes compared may not always reflect their true biological diversity. And finally, we think that clinical endpoints of the tumor probably depend on subtle changes in many and, possibly, alternative molecular pathways, and such changes may be difficult to demonstrate.
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