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Metadata Analysis to Get Insight into Drug Resistant Ovarian Cancer

Publication Type : Conference Paper

Publisher : IIETA

Source : International Conference on Bio-Neuro Informatics and Algorithms

Campus : Chennai

School : School of Engineering

Department : Computer Science and Engineering

Year : 2022

Abstract : The most prevalent kind of ovarian cancer is high-grade serous ovarian cancer. Drug resistance is the major issue in this cancer. Transcriptional fusions involving SLC25A40- ABCB1 is a leading cause of this cancer. To understand the phenotypic consequences, transcriptional profile was studied using high throughput sequencing technologies. Here we have used that data to understand co-expressed genes and their functional role in two different cell types, fusion positive and fusion negative using WGCNA analysis. The major biological processes which are correlated with fusion positive cells are extracellular structure organization, external encapsulating structure organization, regulation of cell migration and axon guidance etc. In addition to these investigations, gene expression data of a PARPi-sensitive cell line and resistance was analyzed to determine the role and capabilities of PARP-inhibitors in controlling drug-resistant High-grade serous ovarian cancer. This investigation also shed light on the possible mechanism of PARPi resistant cases and concluded that the resistance comes from the dynamics of four biological processes like regulation of cell junction assembly, cell-cell adhesion, tissue morphogenesis, neuron projection development and negative regulation of cellular component organization. Further analysis with different Gene Set Enrichment analysis illustrates that four processes, negative regulation of lens fiber cell differentiation, sarcoplasmic reticulum lumen, presynaptic membrane assembly and nitrobenzene metabolic process are activated in PARPi resistance. These processes are connected to each other through an important kinase protein ERBB2 which is interpreted as a key protein in PARPi resistance.

Cite this Research Publication : Sujata Roy, J. Jeyalakshmi, S. Poonkuzhali, M. Michael Gromiha, “Metadata Analysis to Get Insight into Drug Resistant Ovarian Cancer”, 2022 International Conference on Bio-Neuro Informatics and Algorithms (iCBNA 2022), Pune, India 21 – 22 June 2022 , IEEE , pp- 467-471(SCOPUS)

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