Abstract
BackgroundPlatinum 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.MethodsGenes 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.ResultsGenes 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.ConclusionsPlatinum 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.
Highlights
Platinum resistance is an important cause of clinical recurrence and death for ovarian cancer
Nowadays cytoreducitve surgery combined with chemotherapy has been accepted as a standard treatment of this disease, where platinum-based agents such as cisplatin and carboplatin are considered to be the essential components of most chemotherapy regimens [4,5,6]
A plenty of genes and pathways have been investigated for platinum resistance in ovarian cancer, mechanisms of drug resistance are still not fully understood
Summary
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. Ovarian cancer is the most lethal cause of all gynecological malignancies [1]. A plenty of genes and pathways have been investigated for platinum resistance in ovarian cancer, mechanisms of drug resistance are still not fully understood. Most researchers examined only a small part of genes, the majority of them focused on specific subtypes of ovarian cancer. As platinum resistance seems to be regulated by sophisticate molecular networks, we try to systematically assess reported genes with text mining and other bioinformatics methods, quantitatively describe
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