基于综合生物信息学方法鉴定髓母细胞瘤的潜在关键基因和分子机制。

Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach.

机构信息

Department of Software Engineering, Daffodil International University (DIU), Ashulia, Savar, Dhaka 1342, Bangladesh.

Department of Computer Science, Cihan University Sulaimaniya, Sulaimaniya, 46001 Kurdistan Region, Iraq.

出版信息

Biomed Res Int. 2022 Jan 4;2022:1776082. doi: 10.1155/2022/1776082. eCollection 2022.

DOI:10.1155/2022/1776082

PMID:35127939

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8816556/

Abstract

BACKGROUND

Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB.

METHODS

A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣log fold change | >1 and < 0.05. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network.

RESULTS

Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

摘要

背景

髓母细胞瘤（MB）是最常见的脑癌，主要发生在儿童时期。这种癌症始于大脑的小脑部分。本研究旨在筛选新的、有意义的生物标志物，这些标志物可能作为潜在的预后生物标志物和 MB 的治疗靶点。

方法

从基因表达谱 GSE22139、GSE37418 和 GSE86574 中总共下载了 103 个与 MB 相关的样本，这些样本来自基因表达综合数据库（GEO）。应用 limma 包分析了所有三个数据集，共鉴定出 1065 个共同差异表达基因，包括 408 个上调基因和 657 个下调基因，最小截距标准为∣log 倍变化 | >1 和 < 0.05。执行基因本体论（GO）、京都基因与基因组百科全书（KEGG）和 WikiPathways 富集分析，以发现共同差异表达基因的内在功能。富集分析的结果表明，共同差异表达基因与 MB 的进展和发展显著相关。使用 Search Tool for Retrieval of Interacting Genes (STRING) 数据库构建互作网络，并用 Cytoscape 工具显示，应用 cytoHubba 插件的连通性和压力值方法，从整个网络中鉴定出 35 个枢纽基因。