Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis.
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ABSTRACT:
SUBMITTER: Yan H
PROVIDER: S-EPMC6771956 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Multiple myeloma (MM) is a hematological malignancy in which monoclonal plasma cells multiply in the bone marrow and monoclonal immunoglobulins are overproduced in older people. Several molecular and cytogenetic advances allow scientists to identify several genetic and chromosomal abnormalities that cause the disease. The comprehension of the pathophysiology of MM requires an understanding of the characteristics of malignant clones and the changes in the bone marrow microenvironment. This study aims to identify the central genes and to determine the key signaling pathways in MM by in silico approaches. A list of 114 differentially expressed genes (DEGs) is important in the prognosis of MM. The DEGs are collected from scientific publications and databases (https://www.ncbi.nlm.nih.gov/). These data are analyzed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software (https://string-db.org/) through the construction of protein-protein interaction (PPI) networks and enrichment analysis of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, by CytoHubba, AutoAnnotate, Bingo Apps plugins in Cytoscape software (https://cytoscape.org/) and by DAVID database (https://david.ncifcrf.gov/). The analysis of the results shows that there are 7 core genes, including TP53; MYC; CDND1; IL6; UBA52; EZH2, and MDM2. These top genes appear to play a role in the promotion and progression of MM. According to functional enrichment analysis, these genes are mainly involved in the following signaling pathways: Epstein-Barr virus infection, microRNA pathway, PI3K-Akt signaling pathway, and p53 signaling pathway. Several crucial genes, including TP53, MYC, CDND1, IL6, UBA52, EZH2, and MDM2, are significantly correlated with MM, which may exert their role in the onset and evolution of MM.
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Project description:BackgroundMultiple Myeloma (MM) is a hematologic malignant disease whose underlying molecular mechanism has not yet fully understood. Generally, cell adhesion plays an important role in MM progression. In our work, we intended to identify key genes involved in cell adhesion in MM.MethodsFirst, we identified differentially expressed genes (DEGs) from the mRNA expression profiles of GSE6477 dataset using GEO2R with cut-off criterion of p < 0.05 and [logFC] ≥ 1. Then, GO and KEGG analysis were performed to explore the main function of DEGs. Moreover, we screened hub genes from the protein-protein interaction (PPI) network analysis and evaluated their prognostic and diagnostic values by the PrognoScan database and ROC curves. Additionally, a comprehensive analysis including clinical correlation analysis, GSEA and transcription factor (TF) prediction, pan-cancer analysis of candidate genes was performed using both clinical data and mRNA expression data.ResultsFirst of all, 1383 DEGs were identified. Functional and pathway enrichment analysis suggested that many DEGs were enriched in cell adhesion. 180 overlapped genes were screened out between the DEGs and genes in GO terms of cell adhesion. Furthermore, 12 genes were identified as hub genes based on a PPI network analysis. ROC curve analysis demonstrated that ITGAM, ITGB2, ITGA5, ITGB5, CDH1, IL4, ITGA9, and LAMB1 were valuable biomarkers for the diagnosis of MM. Further study demonstrated that ITGA9 and LAMB1 revealed prognostic values and clinical correlation in MM patients. GSEA and transcription factor (TF) prediction suggested that MYC may bind to ITGA9 and repress its expression and HIF-1 may bind to LAMB1 to promote its expression in MM. Additionally, pan-cancer analysis showed abnormal expression and clinical outcome associations of LAMB1 and ITGA9 in multiple cancers.ConclusionIn conclusion, ITGA9 and LAMB1 were identified as potent biomarkers associated with cell adhesion in MM.
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Project description:Background: Multiple myeloma (MM) is the second most common hematological malignancy, which is still incurable and relapses inevitably, highlighting further understanding of the possible mechanisms. Side population (SP) cells are a group of enriched progenitor cells showing stem-like phenotypes with a distinct low-staining pattern with Hoechst 33342. Compared to main population (MP) cells, the underlying molecular characteristics of SP cells remain largely unclear. This bioinformatics analysis aimed to identify key genes and pathways in myeloma SP cells to provide novel biomarkers, predict MM prognosis and advance potential therapeutic targets. Methods: The gene expression profile GSE109651 was obtained from Gene Expression Omnibus database, and then differentially expressed genes (DEGs) with P-value <0.05 and |log2 fold-change (FC)| > 2 were selected by the comparison of myeloma light-chain (LC) restricted SP (LC/SP) cells and MP CD138+ cells. Subsequently, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) network analysis were performed to identify the functional enrichment analysis of the DEGs and screen hub genes. Cox proportional hazards regression was used to select the potential prognostic DEGs in training dataset (GSE2658). The prognostic value of the potential prognostic genes was evaluated by Kaplan-Meier curve and validated in another external dataset (MMRF-CoMMpass cohort from TCGA). Results: Altogether, 403 up-regulated and 393 down-regulated DEGs were identified. GO analysis showed that the up-regulated DEGs were significantly enriched in innate immune response, inflammatory response, plasma membrane and integral component of membrane, while the down-regulated DEGs were mainly involved in protoporphyrinogen IX and heme biosynthetic process, hemoglobin complex and erythrocyte differentiation. KEGG pathway analysis suggested that the DEGs were significantly enriched in osteoclast differentiation, porphyrin and chlorophyll metabolism and cytokine-cytokine receptor interaction. The top 10 hub genes, identified by the plug-in cytoHubba of the Cytoscape software using maximal clique centrality (MCC) algorithm, were ITGAM, MMP9, ITGB2, FPR2, C3AR1, CXCL1, CYBB, LILRB2, HP and FCER1G. Modules and corresponding GO enrichment analysis indicated that myeloma LC/SP cells were significantly associated with immune system, immune response and cell cycle. The predictive value of the prognostic model including TFF3, EPDR1, MACROD1, ARHGEF12, AMMECR1, NFATC2, HES6, PLEK2 and SNCA was identified, and validated in another external dataset (MMRF-CoMMpass cohort from TCGA). Conclusions: In conclusion, this study provides reliable molecular biomarkers for screening, prognosis, as well as novel therapeutic targets for myeloma LC/SP cells.
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