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Construction of a novel prognostic-predicting model correlated to ovarian cancer

Authors
  • Tang, Weichun1, 2
  • Li, Jie3
  • Chang, Xinxia1, 2
  • Jia, Lizhou1
  • Tang, Qi1, 2
  • Wang, Ying4
  • Zheng, Yanli4
  • Sun, Lizhou5
  • Feng, Zhenqing1, 2
  • 1 National Health Commission Key Laboratory of Antibody Technique, Nanjing Medical University, Nanjing, People’s Republic of China
  • 2 Department of Pathology, Nanjing Medical University, Nanjing, People’s Republic of China
  • 3 Department of Nursing, The Second Affiliated Hospital of Nantong University, Nantong, People’s Republic of China
  • 4 Department of Gynaecology and Obstetrics, The Second Affiliated Hospital of Nantong University, Nantong, People’s Republic of China
  • 5 Department of Obstetrics and Gynecology, First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China
Type
Published Article
Journal
Bioscience Reports
Publisher
Portland Press
Publication Date
Aug 07, 2020
Volume
40
Issue
8
Identifiers
DOI: 10.1042/BSR20201261
PMID: 32716025
PMCID: PMC7414523
Source
PubMed Central
Keywords
License
Green

Abstract

Background : Ovarian cancer (OC) is one of the most lethal gynecological cancers worldwide. The pathogenesis of the disease and outcomes prediction of OC patients remain largely unclear. The present study aimed to explore the key genes and biological pathways in ovarian carcinoma development, as well as construct a prognostic model to predict patients’ overall survival (OS). Results: We identified 164 up-regulated and 80 down-regulated differentially expressed genes (DEGs) associated with OC. Gene Ontology (GO) term enrichment showed DEGs mainly correlated with spindle microtubes. For Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cell cycle was mostly enriched for the DEGs. The protein–protein interaction (PPI) network yielded 238 nodes and 1284 edges. Top three modules and ten hub genes were further filtered and analyzed. Three candidiate drugs targeting for therapy were also selected. Thirteen OS-related genes were selected and an eight-mRNA model was present to stratify patients into high- and low-risk groups with significantly different survival. Conclusions: The identified DEGs and biological pathways may provide new perspective on the pathogenesis and treatments of OC. The identified eight-mRNA signature has significant clinical implication for outcome prediction and tailored therapy guidance for OC patients.

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