ArticleOncology letters2016
Gene expression profiling analysis of ovarian cancer.
Article in Oncology letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Low LAMA2 expression may affect drug resistance and prognosis in ovarian cancer via positive LGR5 coexpression.Translational cancer research · 2026Article
- Network-Based Analysis of Cognitive Impairment and Memory Deficits from Transcriptome Data.Journal of molecular neuroscience : MN · 2021Article
- Transcriptome Profiling Reveals Matrisome Alteration as a Key Feature of Ovarian Cancer Progression.Cancers · 2019Article
- Traps and trumps from adjacent-to-tumor samples in gastric cancer research.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2018Article
- Matrix metalloproteinase-7 may serve as a novel biomarker for cervical cancer.OncoTargets and therapy · 2018Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a gynecological oncology, ovarian cancer has high incidence and mortality. To study the mechanisms of ovarian cancer, the present study analyzed the GSE37582 microarray. GSE37582 was downloaded from Gene Expression Omnibus and included data from 74 ovarian cancer cases and 47 healthy controls. The differentially-expressed genes (DEGs) were screened using linear models for microarray data package in R and were further screened for functional annotation. Next, Gene Ontology and pathway enrichment analysis of the DEGs was conducted. The interaction associations of the proteins encoded by the DEGs were searched using the Search Tool for the Retrieval of Interacting Genes, and the protein-protein interaction (PPI) network was visualized by Cytoscape. Moreover, module analysis of the PPI network was performed using the BioNet analysis tool in R. A total of 284 DEGs were screened, consisting of 145 upregulated genes and 139 downregulated genes. In particular, downregulated FBJ murine osteosarcoma viral oncogene homolog (FOS) was an oncogene, while downregulated cyclin-dependent kinase inhibitor 1A (CDKN1A) was a tumor suppressor gene and upregulated cluster of differentiation 44 (CD44) was classed as an 'other' gene. The enriched functions included collagen catabolic process, stress-activated mitogen-activated protein kinases cascade and insulin receptor signaling pathway. Meanwhile, FOS (degree, 15), CD44 (degree, 9), B-cell CLL/lymphoma 2 (BCL2; degree, 7), CDKN1A (degree, 7) and matrix metallopeptidase 3 (MMP3; degree, 6) had higher connectivity degrees in the PPI network for the DEGs. These genes may be involved in ovarian cancer by interacting with other genes in the module of the PPI network (e.g., BCL2-FOS, BCL2-CDKN1A, FOS-CDKN1A, FOS-CD44, MMP3-MMP7 and MMP7-CD44). Overall, BCL2, FOS, CDKN1A, CD44, MMP3 and MMP7 may be correlated with ovarian cancer.
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