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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:BackgroundChronic 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.MethodsWe 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.ResultsA 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.ConclusionThese 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:Introduction: Acute myeloid leukemia (AML) is the most common type of leukemia in adults. However, there is a gap in understanding the molecular basis of the disease, partly because key genes associated with AML have not been extensively explored. In the current study, we aimed to identify genes that have strong association with AML based on a cross-species integrative approach. Methods: We used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify co-expressed gene modules significantly correlated with human AML, and further selected the genes exhibiting a significant difference in expression between AML and healthy mouse. Protein-protein interactions, transcription factors, gene function, genetic regulation, and coding sequence variants were integrated to identify key hub genes in AML. Results: The cross-species approach identified a total of 412 genes associated with both human and mouse AML. Enrichment analysis confirmed an association of these genes with hematopoietic and immune-related functions, phenotypes, processes, and pathways. Further, the integrated analysis approach identified a set of important module genes including Nfe2, Trim27, Mef2c, Ets1, Tal1, Foxo1, and Gata1 in AML. Six of these genes (except ETS1) showed significant differential expression between human AML and healthy samples in an independent microarray dataset. All of these genes are known to be involved in immune/hematopoietic functions, and in transcriptional regulation. In addition, Nfe2, Trim27, Mef2c, and Ets1 harbor coding sequence variants, whereas Nfe2 and Trim27 are cis-regulated, making them attractive candidates for validation. Furthermore, subtype-specific analysis of the hub genes in human AML indicated high expression of NFE2 across all the subtypes (M0 through M7) and enriched expression of ETS1, LEF1, GATA1, and TAL1 in M6 and M7 subtypes. A significant correlation between methylation status and expression level was observed for most of these genes in AML patients. Conclusion: Findings from the current study highlight the importance of our cross-species approach in the identification of multiple key candidate genes in AML, which can be further studied to explore their detailed role in leukemia/AML.

Project description:BackgroundBasal-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.ObjectiveThe aim of this study was to identify potential gene signatures of the development and progression of the BLBC via bioinformatics analysis.Methods and resultsThe 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.ConclusionOur 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.

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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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