Abstract

Medulloblastoma is the most typical malignant brain tumor in kids. Younger children, especially babies, are at a considerably increased risk of therapeutic adverse effects. Despite significant advancement in the domain of medulloblastoma molecular biology, much remains to be accomplished in comprehending the etiology, important mechanisms causing medulloblastoma, and molecular risk stratification, as well as developing strategies of treatment with improved survival and fewer long-term squeal. So, the goal of this study is to look at and assist in the Identification and annotation of differentially expressed genes in Medulloblastoma for better treatment and long-term survival of the patients. Identification of potential therapeutic targets among the differentially expressed genes can be done by RNA-seq data analysis. This is accomplished by regulating the quality of data. We read the HTseq values with the DESeq2 tool and divided the data into normal and sick categories. This utility computes the per-exon and per-gene read counts for certain genes using RNA-seq data. We used raw numbers and a discrete distribution model to test for differential expression. Finally, annotated DESeq2 was utilized to identify differentially expressed genes. The GO enrichment and KEGG pathway in the context of CC (Cellular Component), BP (Biological Process), MF (Molecular Function), and their associated KEGG pathways indicated the functional annotation of DEGs. The analysis of the functional enrichment of DEGs revealed that the genes were engaged in oxidative Phosphorylation, Cytoplasmic Translation, and negative regulation of Ubiquitin-Protein ligase activity in terms of BP. This study will provide an overview of the current understanding of Medulloblastoma using a bioinformatics approach, as well as therapeutic prospects now being researched in surgery, radiation, and targeted medicines to maximize medulloblastoma treatment using a multidisciplinary approach. Keywords:

Keywords: RNA-seq; Galaxy