ArticleJournal of ovarian research2013
Screening of feature genes of the ovarian cancer epithelia with DNA microarray.
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. Cited by 3 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.
The trial behind it
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Who cites it
3 citing papers in PubMed, 21 citations in OpenAlex.
- Plasma-derived exosomal miR-4732-5p is a promising noninvasive diagnostic biomarker for epithelial ovarian cancer.Journal of ovarian research · 2021Article
- Gene-gene interaction network analysis of ovarian cancer using TCGA data.Journal of ovarian research · 2013Article
- The Progression and Prospects of the Gene Expression Profiling in Ovarian Epithelial Cancer.Gynecology and minimally invasive therapyReview
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed to screen differentially expressed genes (DEGs) of ovarian surface epithelia in order to provide beneficial help for early diagnosis and treatment of ovarian cancer with DNA microarrays.
methodsWe extracted the microarray expression profile GSE14407 from Gene Expression Omnibus database which conducted gene expression profiling analysis of 12 ovarian surface epithelia (OSE) and 12 laser capture microdissected serous ovarian cancer epithelia (CEPI) samples. The DEGs between OSE and CEPI were identified by Limma package of R language. Cluster analysis was employed to compare the differences of gene expression patterns between OSE and CEPI. Furthermore, DEGs were analyzed with Functional classification tool, GenMAPP software and GENECODIS.
resultsWe identified 1229 DEGs including 592 down-regulated genes and 637 up-regulated genes. Pathway analysis showed that cell cycle was the most significant pathway and the DEGs related with cell cycle were almost up-regulated. Module mining analysis showed that the up-regulated DEGs were related with signal transduction while the down-regulated DEGs were related with lipid metabolism pathway and cytoskeletal structure.
conclusionThe genes related with cell cycle, lipid metabolism and cytoskeletal structure may be the treatment targets for ovarian cancer.
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Registered trials
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