ArticleJournal of ovarian research2013
Investigation of the hub genes and related mechanism in ovarian cancer via bioinformatics analysis.
Article in Journal of ovarian research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Exploring miRNA profile associated with cisplatin resistance in ovarian cancer cells.Biochemistry and biophysics reports · 2025Article
- IGF-1R anti-idiotypic antibody antagonist exhibited anti-ovarian cancer bioactivity and reduced cisplatin resistance.Human cell · 2021Article
- The exosome-mediated autocrine and paracrine actions of plasma gelsolin in ovarian cancer chemoresistance.Oncogene · 2020Article
- Bayesian inference of hub nodes across multiple networks.Biometrics · 2019Article
- Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis.Journal of ovarian research · 2018Article
- Screening of potential therapy targets for prostate cancer using integrated analysis of two gene expression profiles.Oncology letters · 2017Article
- Microarray expression profiles of genes in lung tissues of rats subjected to focal cerebral ischemia-induced lung injury following bone marrow-derived mesenchymal stem cell transplantation.International journal of molecular medicine · 2017Article
- Gene-set Analysis with CGI Information for Differential DNA Methylation Profiling.Scientific reports · 2016Article
- Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets.PLoS computational biology · 2015Article
- Retraction note: Investigation of the hub genes and related mechanism in ovarian cancer via bioinformatics analysis.Journal of ovarian research · 2015Article
Corrections and comments
- Retraction · 2015-03-26Compromised Peer Review · Investigation by Company/Institution · Investigation by Journal/Publisher · Misconduct - Official Investigation(s) and/or Finding(s) · Misconduct by Author · · Update posted to notice on April 11, 2016
- Retracted
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer is a cancerous growth arising from the ovary.
objectiveThis study was aimed to explore the molecular mechanism of the development and progression of the ovarian cancer.
methodsWe first identified the differentially expressed genes (DEGs) between the ovarian cancer samples and the healthy controls by analyzing the GSE14407 affymetrix microarray data, and then the functional enrichments of the DEGs were investigated. Furthermore, we constructed the protein-protein interaction network of the DEGs using the STRING online tools to find the genes which might play important roles in the progression of ovarian cancer. In addition, we performed the enrichment analysis to the PPI network.
resultsOur study screened 659 DEGs, including 77 up- and 582 down-regulated genes. These DEGs were enriched in pathways such as Cell cycle, p53 signaling pathway, Pathways in cancer and Drug metabolism. CCNE1, CCNB2 and CYP3A5 were the significant genes identified from these pathways. Protein-protein interaction (PPI) network was constructed and network Module A was found closely associated with ovarian cancer. Hub nodes such as VEGFA, CALM1, BIRC5 and POLD1 were found in the PPI network. Module A was related to biological processes such as mitotic cell cycle, cell cycle, nuclear division, and pathways namely Cell cycle, Oocyte meiosis and p53 signaling pathway.
conclusionsIt indicated that ovarian cancer was closely associated to the dysregulation of p53 signaling pathway, drug metabolism, tyrosine metabolism and cell cycle. Besides, we also predicted genes such as CCNE1, CCNB2, CYP3A5 and VEGFA might be target genes for diagnosing the ovarian cancer.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.