Please use this identifier to cite or link to this item: http://dspace.dtu.ac.in:8080/jspui/handle/repository/19924
Title: PREDICTION OF PECILOCIN AS A POTENTIAL THERAPEUTIC REGIME IN COUNTERING GLIOBLASTOMA USING COMPUTATIONAL APPROACH
Authors: CHAUHAN, SHALLU
Keywords: PECILOCIN
GLIOBLASTOMA
COMPUTATIONAL APPROACH
DIFFERENTIAL EXPRESSION GENES
BIOMARKER
GBM SURVIVAL
Issue Date: May-2023
Series/Report no.: TD-6493;
Abstract: The utmost detrimental form of brain cancer is glioblastoma. Current GBM survival rates are around two years because of fast cellular migration and genetically programmed therapeutic escape mechanisms. GBM has been considered to originate in central nervous system (CNS) neuroglia cells. Glioma stem cells (GSCs) have also been found in several studies, a small subset of tumor-initiating and tumor-propagating cells with features similar to neural stem cells. This study aims to identify genetic markers associated with GBM survival by analyzing publicly available gene expression and clinical data from the Gene Expression Omnibus (GEO) datasets. The differential expression genes (DEGs) among mutant and normal groups were identified, and their survival rates in patients were evaluated. Additionally, enrichment analysis using Enrichr and protein-protein interaction (PPI) were executed to determine significance of these genes. Through the analysis of the GDC Data portal, seven genes (GRIN2A, BCL11A, CAMTA1, ERBB3, WIF1, HLF, and CHN1) were identified as having a substantial impact on GBM development. Furthermore, docking research showed the interaction among natural substances and CAMTA1 protein, revealing a strong affinity between them. The ADME/T study highlighted the probable of Pecilocin as a glioblastoma therapy option. The dissertation's outline includes an introduction that provides background information on GBM and its current treatment challenges, followed by a literature review that covers various aspects of GBM. The materials and methods section viii describes the datasets used to identify DEGs and the workflow followed for data analysis of GBM patient. The results and discussion section presents the findings of sequence similarity analysis, target receptor structures, and the analysis of receptor ligand interactions. In conclusion, this study identifies potential genetic markers associated with GBM survival and highlights Pecilocin as a promising therapeutic option. The research provides insights into the molecular mechanisms underlying GBM and opens avenues for further investigation in the field. The dissertation emphasizes the importance of understanding GBM biology to develop effective treatments and improve patient outcomes.
URI: http://dspace.dtu.ac.in:8080/jspui/handle/repository/19924
Appears in Collections:M Sc

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