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. 2022 Jan 4:2022:1776082.
doi: 10.1155/2022/1776082. eCollection 2022.

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

Md Rakibul Islam  1 , Lway Faisal Abdulrazak  2 , Mohammad Khursheed Alam  3   4   5 , Bikash Kumar Paul  1   6 , Kawsar Ahmed  6   7 , Francis M Bui  7 , Mohammad Ali Moni  8
Affiliations

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

Md Rakibul Islam et al. Biomed Res Int. .

Retraction in

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 P < 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.

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Conflict of interest statement

All the authors have read the manuscript and approved this for submission; also, there are no competing interests.

Figures

Figure 1
Figure 1
Overall workflow of identification of potential genes for MB.
Figure 2
Figure 2
Identification of DEGs. Volcano plots of the distribution of DEGs in GSE22139 (a), GSE37418 (b), and GSE86574 (c). Red and green dots represent the overexpressed and underexpressed DEGs, respectively. (d) The Venn diagram to identify mutual DEGs from the datasets.
Figure 3
Figure 3
Gene Ontology function analysis results for (a) overexpressed DEGs and (b) underexpressed DEGs. Different colors indicate different categories of GO terms.
Figure 4
Figure 4
Pathway enrichment analysis: (a) KEGG and (b) WikiPathways results. Red-colored dot for underexpressed DEG pathway terms and violet-colored dot for overexpressed DEG pathway terms.
Figure 5
Figure 5
BiNGO GO analysis results for all mutual DEGs. Dot size indicates the GeneCount numbers. Dot color demonstrates the GO categories.
Figure 6
Figure 6
Constructed protein-protein interaction network. In this network, there are 431 nodes and 1440 connections. Green nodes for overexpressed genes and violet-colored nodes for underexpressed genes. The size of a node indicates the connectivity values of a node.
Figure 7
Figure 7
(a) Top 35 hub gene network. (b) A plot that demonstrates every hub gene stress and connectivity value. Dot size represents the stress value.
Figure 8
Figure 8
Significant clusters from the PPI network (a–d). Pathway analysis of clusters (e). Each color represents a different cluster.
Figure 9
Figure 9
TF-gene network construction (a). A visualization to present which TFs are connected with which hub genes (b).
Figure 10
Figure 10
Survival analysis of hub genes (P < 0.05).
See this image and copyright information in PMC

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