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Identification of the hub genes and pathways involved in acute myeloid leukemia using bioinformatics analysis.

ABSTRACT: BACKGROUND:We identified the hub genes and pathways dysregulated in acute myeloid leukemia and the potential molecular mechanisms involved. METHODS:We downloaded the GSE15061 gene expression dataset from the Gene Expression Omnibus database and used weighted gene co-expression network analysis to identify hub genes. Differential expression of the genes was evaluated using the limma package in R software. Subsequently, we built a protein-protein interaction network followed by functional enrichment analysis. Then, the prognostic significance of gene expression was explored in terms of overall survival. Finally, transcription factor-mRNA (ribonucleic acid) and microRNA-mRNA interaction analysis was also explored. RESULTS:We identified 100 differentially expressed hub genes. Functional enrichment analysis indicated that the genes were principally involved in immune system regulation, host defense, and negative regulation of apoptosis and myeloid cell differentiation. We identified 4 hub genes, the expression of which was significantly correlated with overall survival. Finally, 26 key regulators for hub genes and 38 microRNA-mRNA interactions were identified. CONCLUSION:We performed a comprehensive bioinformatics analysis of hub genes potentially involved in acute myeloid leukemia development. Further molecular biological experiments are required to confirm the roles played by these genes.

SUBMITTER: Tan Y

PROVIDER: S-EPMC7458232 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identification of the hub genes and pathways involved in acute myeloid leukemia using bioinformatics analysis.

Tan Youping Y Zheng Liling L Du Yuanyuan Y Zhong Qi Q Zhu Yangmin Y Liu Zhi Z Liu Shuang S Zhang Qing Q

Medicine 20200801 35

<h4>Background</h4>We identified the hub genes and pathways dysregulated in acute myeloid leukemia and the potential molecular mechanisms involved.<h4>Methods</h4>We downloaded the GSE15061 gene expression dataset from the Gene Expression Omnibus database and used weighted gene co-expression network analysis to identify hub genes. Differential expression of the genes was evaluated using the limma package in R software. Subsequently, we built a protein-protein interaction network followed by func ...[more]

PMID: 32871963

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Project description:Background Chronic myeloid leukemia (CML) is a malignant hyperplastic tumor of the bone marrow originating from pluripotent hematopoietic stem cells. The advent of tyrosine kinase inhibitors (TKIs) has greatly improved the survival rate of patients with CML. However, TKI-resistance leads to the disease recurrence and progression. This study aimed to identify immune-related genes (IRGs) associated with CML progression. Methods We extracted the gene’s expression profiles from the Gene Expression Omnibus (GEO). Bioinformatics analysis was used to determine the differentially expressed IRGs of CML and normal peripheral blood mononuclear cells (PBMCs). Functional enrichment and gene set enrichment analysis (GSEA) were used to explore its potential mechanism. Hub genes were identified using Molecular Complex Detection (MCODE) and the CytoHubba plugin. The hub genes’ diagnostic value was evaluated using the receiver operating characteristic (ROC). The relative proportions of infiltrating immune cells in each CML sample were evaluated using CIBERSORT. Quantitative real-time PCR (RT-qPCR) was used to validate the hub gene expression in clinical samples. Results A total of 31 differentially expressed IRGs were identified. GO analyses revealed that the modules were typically enriched in the receptor ligand activity, cytokine activity, and endopeptidase activity. KEGG enrichment analysis of IRGs revealed that CML involved Th17 cell differentiation, the NF-kappa B signaling pathway, and cytokine-cytokine receptor interaction. A total of 10 hub genes were selected using the PPI network. GSEA showed that these hub genes were related to the gamma-interferon immune response, inflammatory response, and allograft rejection. ROC curve analysis suggested that six hub genes may be potential biomarkers for CML diagnosis. Further analysis indicated that immune cells were associated with the pathogenesis of CML. The RT-qPCR results showed that proteinase 3 (PRTN3), cathepsin G (CTSG), matrix metalloproteinase 9 (MMP9), resistin (RETN), eosinophil derived neurotoxin (RNase2), eosinophil cationic protein (ECP, RNase3) were significantly elevated in CML patients’ PBMCs compared with healthy controls. Conclusion These results improved our understanding of the functional characteristics and immune-related molecular mechanisms involved in CML progression and provided potential diagnostic biomarkers and therapeutic targets.

Project description:Background:Basal-like breast cancer (BLBC) is the most aggressive subtype of breast cancer (BC) and links to poor outcomes. As the molecular mechanism of BLBC has not yet been completely discovered, identification of key pathways and hub genes of this disease is an important way for providing new insights into exploring the mechanisms of BLBC initiation and progression. Objective:The aim of this study was to identify potential gene signatures of the development and progression of the BLBC via bioinformatics analysis. Methods and results:The differential expressed genes (DEGs) including 40 up-regulated and 21 down-regulated DEGs were identified between GSE25066 and GSE21422 microarrays, and these DEGs were significantly enriched in the terms related to oncogenic or suppressive roles in BLBC progression. In addition, KEGG pathway and GSEA (Gene Set Enrichment Analysis) enrichment analyses were performed for DEGs between the basal type and non-basal-type breast cancer from GSE25066 microarray. These DEGs were enriched in pathways such as cell cycle, cytokine-cytokine receptor interaction, chemokine signaling pathway, central carbon metabolism signaling and TNF signaling pathway. Moreover, the protein-protein interaction (PPI) network was constructed with those 61 DEGs using the Cytoscape software, and the biological significance of putative modules was established using MCODE. The module 1 was found to be closely related with a term of mitosis regulation and enriched in cell cycle pathway, and thus confirmed the pathological characteristic of BLBC with a high mitotic index. Furthermore, prediction values of the top 10 hub genes such as CCNB2, BUB1, NDC80, CENPE, KIF2C, TOP2A, MELK, TPX2, CKS2 and KIF20A were validated using Oncomine and Kaplan-Meier plotter. Conclusion:Our results suggest the intriguing possibility that the hub genes and modules in the PPI network contributed to in-depth knowledge about the molecular mechanism of BLBC, paving a way for more accurate discovery of potential treatment targets for BLBC patients.

Dataset Information

Identification of the hub genes and pathways involved in acute myeloid leukemia using bioinformatics analysis.

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Identification of the hub genes and pathways involved in acute myeloid leukemia using bioinformatics analysis.

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