Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma.

in Pharmaceutics by Md Khairul Islam, Md Rakibul Islam, Md Habibur Rahman, Md Zahidul Islam, Md Al Amin, Kazi Rejvee Ahmed, Md Ataur Rahman, Mohammad Ali Moni, Bonglee Kim

TLDR

  • The study looked at how glioblastoma, a type of brain cancer, might be linked to ischemic stroke and moyamoya disease, two types of stroke. The study used bioinformatics approaches to look at gene expression data from people with these conditions and found that glioblastoma might be causing the strokes. The study also found specific genes and pathways that might be involved in the development and progression of these conditions.

Abstract

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein-protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Overview

  • The study investigates the relationship between glioblastoma, ischemic stroke, and moyamoya disease using bioinformatics approaches. The main focus is to understand the underlying deterministic details of the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease, which are accountable for by glioblastoma. The hypothesis being tested is that there are correlations among the diseases. The methodology used for the experiment includes gathering adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease from various repositories, establishing the association between the diseases using existing pipelines, and validating the data using enrichment studies such as protein-protein interaction and gold benchmark databases. The primary objective of the study is to contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Comparative Analysis & Findings

  • The study found correlations among glioblastoma, ischemic stroke, and moyamoya disease. The results suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. The study also identified specific genes and pathways that are involved in the development and progression of these diseases. The findings contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Implications and Future Directions

  • The study's findings have significant implications for the field of research and clinical practice. The results suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. The study also identified specific genes and pathways that are involved in the development and progression of these diseases. Future research directions could focus on validating these findings in clinical settings and identifying potential therapeutic targets for the treatment of these diseases. The study's findings also highlight the importance of bioinformatics approaches in understanding the complex relationships between diseases and identifying potential correlations among them.
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