Haixia Li1, Jinghua Li1, Wanli Gao1, Cheng Zhen2, Limin Feng3. 1. Department of Obstetrics & Gynecology, Beijing TianTan Hospital, Capital Medical University, Bejing, 100050, China. 2. The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China. zhencheng302@126.com. 3. Department of Obstetrics & Gynecology, Beijing TianTan Hospital, Capital Medical University, Bejing, 100050, China. lucyfeng66@163.com.
Abstract
BACKGROUND: Platinum resistance is an important cause of clinical recurrence and death for ovarian cancer. This study tries to systematically explore the molecular mechanisms for platinum resistance in ovarian cancer and identify regulatory genes and pathways via text mining and other methods. METHODS: Genes in abstracts of associated literatures were identified. Gene ontology and protein-protein interaction (PPI) network analysis were performed. Then co-occurrence between genes and ovarian cancer subtypes were carried out followed by cluster analysis. RESULTS: Genes with highest frequencies are mostly involved in DNA repair, apoptosis, metal transport and drug detoxification, which are closely related to platinum resistance. Gene ontology analysis confirms this result. Some proteins such as TP53, HSP90, ESR1, AKT1, BRCA1, EGFR and CTNNB1 work as hub nodes in PPI network. According to cluster analysis, specific genes were highlighted in each subtype of ovarian cancer, indicating that various subtypes may have different resistance mechanisms respectively. CONCLUSIONS: Platinum resistance in ovarian cancer involves complicated signaling pathways and different subtypes may have specific mechanisms. Text mining, combined with other bio-information methods, is an effective way for systematic analysis.
BACKGROUND:Platinum resistance is an important cause of clinical recurrence and death for ovarian cancer. This study tries to systematically explore the molecular mechanisms for platinum resistance in ovarian cancer and identify regulatory genes and pathways via text mining and other methods. METHODS: Genes in abstracts of associated literatures were identified. Gene ontology and protein-protein interaction (PPI) network analysis were performed. Then co-occurrence between genes and ovarian cancer subtypes were carried out followed by cluster analysis. RESULTS: Genes with highest frequencies are mostly involved in DNA repair, apoptosis, metal transport and drug detoxification, which are closely related to platinum resistance. Gene ontology analysis confirms this result. Some proteins such as TP53, HSP90, ESR1, AKT1, BRCA1, EGFR and CTNNB1 work as hub nodes in PPI network. According to cluster analysis, specific genes were highlighted in each subtype of ovarian cancer, indicating that various subtypes may have different resistance mechanisms respectively. CONCLUSIONS:Platinum resistance in ovarian cancer involves complicated signaling pathways and different subtypes may have specific mechanisms. Text mining, combined with other bio-information methods, is an effective way for systematic analysis.
Entities:
Keywords:
Ovarian cancer; Platinum resistance; Text mining
Authors: Zibi Marchocki; Alicia Tone; Carl Virtanen; Richard de Borja; Blaise Clarke; Theodore Brown; Taymaa May Journal: J Ovarian Res Date: 2022-05-02 Impact factor: 5.506