SynthesisBioscience reports2020
Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer.
Synthesis in Bioscience reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis.International journal of molecular sciences · 2023Pooled it
- Transcriptomic Meta-Analysis as a Framework for Robust Cross-Study Biological Inference.International journal of molecular sciences · 2026Review
- A bioinformatics approach to identify potential biomarkers of high-grade ovarian cancer.Journal of the Turkish German Gynecological Association · 2026Article
- The Expression and Clinical Significance of C1orf106 in Low-Grade Serous Ovarian Cancer.The journal of obstetrics and gynaecology research · 2025Article
- The INAVA mRNA in Extracellular Vesicles Activates Normal Ovarian Fibroblasts by Phosphorylation-Ubiquitylation Crosstalk of HMGA2.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Investigating the function and targeting of MET protein as an oncogene kinase in pancreatic ductal adenocarcinoma: A microarray data integration.BioImpacts : BI · 2025Review
- Identification of Novel Genes and Pathways of Ovarian Cancer Using a Comprehensive Bioinformatic Framework.Applied biochemistry and biotechnology · 2024Article
- Integrative analysis of RNA-sequencing and microarray for the identification of adverse effects of UVB exposure on human skin.Frontiers in public health · 2024Article
- Comprehensively analyzing the genetic alterations, and identifying key genes in ovarian cancer.Oncology research · 2023Article
- Identification of New Molecular Biomarkers in Ovarian Cancer Using the Gene Expression Profile.Journal of clinical medicine · 2022Article
- Establishment of an optimized CTC detection model consisting of EpCAM, MUC1 and WT1 in epithelial ovarian cancer and its correlation with clinical characteristics.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2022Article
- High-grade ovarian cancer associated H/ACA snoRNAs promote cancer cell proliferation and survival.NAR cancer · 2022Article
- Integrated Bioinformatics Analysis for Identification of the Hub Genes Linked with Prognosis of Ovarian Cancer Patients.Computational and mathematical methods in medicine · 2022Article
- CDC20 is a novel biomarker for improved clinical predictions in epithelial ovarian cancer.American journal of cancer research · 2022Article
- Nicotinamide N-Methyltransferase in Acquisition of Stem Cell Properties and Therapy Resistance in Cancer.International journal of molecular sciences · 2021Review
- Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.PloS one · 2021Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray-based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets.
methodsIn the present study, the data of gene expression in ovarian cancer were downloaded from Gene Expression Omnibus (GEO) and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation.
resultsA total of 972 DEGs with P-value < 0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up- and down-regulated genes were selected for the validation of gene expression profiling. Among these genes, up-regulated CD24 molecule (CD24), SRY (sex determining region Y)-box transcription factor 17 (SOX17), WFDC2, epithelial cell adhesion molecule (EPCAM), innate immunity activator (INAVA), and down-regulated aldehyde oxidase 1 (AOX1) were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer.
conclusionOur study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.
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