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Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis

BACKGROUND: Multiple 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. METHODS: First,...

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Autores principales: Peng, Yue, Wu, Dong, Li, Fangmei, Zhang, Peihua, Feng, Yuandong, He, Aili
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309988/
https://www.ncbi.nlm.nih.gov/pubmed/32581652
http://dx.doi.org/10.1186/s12935-020-01355-z
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author Peng, Yue
Wu, Dong
Li, Fangmei
Zhang, Peihua
Feng, Yuandong
He, Aili
author_facet Peng, Yue
Wu, Dong
Li, Fangmei
Zhang, Peihua
Feng, Yuandong
He, Aili
author_sort Peng, Yue
collection PubMed
description BACKGROUND: Multiple 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. METHODS: First, 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. RESULTS: First 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. CONCLUSION: In conclusion, ITGA9 and LAMB1 were identified as potent biomarkers associated with cell adhesion in MM.
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spelling pubmed-73099882020-06-23 Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis Peng, Yue Wu, Dong Li, Fangmei Zhang, Peihua Feng, Yuandong He, Aili Cancer Cell Int Primary Research BACKGROUND: Multiple 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. METHODS: First, 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. RESULTS: First 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. CONCLUSION: In conclusion, ITGA9 and LAMB1 were identified as potent biomarkers associated with cell adhesion in MM. BioMed Central 2020-06-22 /pmc/articles/PMC7309988/ /pubmed/32581652 http://dx.doi.org/10.1186/s12935-020-01355-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Primary Research
Peng, Yue
Wu, Dong
Li, Fangmei
Zhang, Peihua
Feng, Yuandong
He, Aili
Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis
title Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis
title_full Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis
title_fullStr Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis
title_full_unstemmed Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis
title_short Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis
title_sort identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis
topic Primary Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309988/
https://www.ncbi.nlm.nih.gov/pubmed/32581652
http://dx.doi.org/10.1186/s12935-020-01355-z
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