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Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets

BACKGROUND: Multiple myeloma is a cancer which has a high occurrence rate and causes great injury to people worldwide. In recent years, many studies reported the effects of miRNA on the appearance of multiple myeloma. However, due to the differences of samples and sequencing platforms, a large numbe...

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Autores principales: Xiang, Tian, Hu, Ai-Xin, Sun, Peng, Liu, Gao, Liu, Gang, Xiao, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5289111/
https://www.ncbi.nlm.nih.gov/pubmed/28168095
http://dx.doi.org/10.7717/peerj.2831
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author Xiang, Tian
Hu, Ai-Xin
Sun, Peng
Liu, Gao
Liu, Gang
Xiao, Yan
author_facet Xiang, Tian
Hu, Ai-Xin
Sun, Peng
Liu, Gao
Liu, Gang
Xiao, Yan
author_sort Xiang, Tian
collection PubMed
description BACKGROUND: Multiple myeloma is a cancer which has a high occurrence rate and causes great injury to people worldwide. In recent years, many studies reported the effects of miRNA on the appearance of multiple myeloma. However, due to the differences of samples and sequencing platforms, a large number of inconsistent results have been generated among these studies, which limited the cure of multiple myeloma at the miRNA level. METHODS: We performed meta-analyses to identify the key miRNA biomarkers which could be applied on the treatment of multiple myeloma. The key miRNAs were determined by overlap comparisons of seven datasets in multiple myeloma. Then, the target genes for key miRNAs were predicted by the software TargetScan. Additionally, functional enrichments and binding TFs were investigated by DAVID database and Tfacts database, respectively. RESULTS: Firstly, comparing the normal tissues, 13 miRNAs were differently expressed miRNAs (DEMs) for at least three datasets. They were considered as key miRNAs, with 12 up-regulated (hsa-miR-106b, hsa-miR-125b, hsa-miR-130b, hsa-miR-138, hsa-miR-15b, hsa-miR-181a, hsa-miR-183, hsa-miR-191, hsa-miR-19a, hsa-miR-20a, hsa-miR-221 and hsa-miR-25) and one down-regulated (hsa-miR-223). Secondly, functional enrichment analyses indicated that target genes of the upregulated miRNAs were mainly transcript factors and enriched in transcription regulation. Besides, these genes were enriched in multiple pathways: the cancer signal pathway, insulin signal metabolic pathway, cell binding molecules, melanin generation, long-term regression and P53 signaling pathway. However, no significant enrichment was found for target genes of the down-regulated genes. Due to the distinct regulation function, four miRNAs (hsa-miR-19a has-miR-221 has-miR25 and has-miR223) were ascertained as the potential prognostic and diagnostic markers in MM. Thirdly, transcript factors analysis unveiled that there were 148 TFs and 60 TFs which bind target genes of the up-regulated miRNAs and target genes of the down-regulated miRNAs, respectively. They respectively generated 652 and 139 reactions of TFs and target genes. Additionally, 50 (31.6%) TFs were shared, while higher specificity was found in TFs of target genes for the upregulated miRNAs. DISCUSSIONS: Together, our findings provided the key miRNAs which affected occurrence of multiple myeloma and regulation function of these miRNAs. It is valuable for the prognosis and diagnosis of multiple myeloma.
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spelling pubmed-52891112017-02-06 Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets Xiang, Tian Hu, Ai-Xin Sun, Peng Liu, Gao Liu, Gang Xiao, Yan PeerJ Genomics BACKGROUND: Multiple myeloma is a cancer which has a high occurrence rate and causes great injury to people worldwide. In recent years, many studies reported the effects of miRNA on the appearance of multiple myeloma. However, due to the differences of samples and sequencing platforms, a large number of inconsistent results have been generated among these studies, which limited the cure of multiple myeloma at the miRNA level. METHODS: We performed meta-analyses to identify the key miRNA biomarkers which could be applied on the treatment of multiple myeloma. The key miRNAs were determined by overlap comparisons of seven datasets in multiple myeloma. Then, the target genes for key miRNAs were predicted by the software TargetScan. Additionally, functional enrichments and binding TFs were investigated by DAVID database and Tfacts database, respectively. RESULTS: Firstly, comparing the normal tissues, 13 miRNAs were differently expressed miRNAs (DEMs) for at least three datasets. They were considered as key miRNAs, with 12 up-regulated (hsa-miR-106b, hsa-miR-125b, hsa-miR-130b, hsa-miR-138, hsa-miR-15b, hsa-miR-181a, hsa-miR-183, hsa-miR-191, hsa-miR-19a, hsa-miR-20a, hsa-miR-221 and hsa-miR-25) and one down-regulated (hsa-miR-223). Secondly, functional enrichment analyses indicated that target genes of the upregulated miRNAs were mainly transcript factors and enriched in transcription regulation. Besides, these genes were enriched in multiple pathways: the cancer signal pathway, insulin signal metabolic pathway, cell binding molecules, melanin generation, long-term regression and P53 signaling pathway. However, no significant enrichment was found for target genes of the down-regulated genes. Due to the distinct regulation function, four miRNAs (hsa-miR-19a has-miR-221 has-miR25 and has-miR223) were ascertained as the potential prognostic and diagnostic markers in MM. Thirdly, transcript factors analysis unveiled that there were 148 TFs and 60 TFs which bind target genes of the up-regulated miRNAs and target genes of the down-regulated miRNAs, respectively. They respectively generated 652 and 139 reactions of TFs and target genes. Additionally, 50 (31.6%) TFs were shared, while higher specificity was found in TFs of target genes for the upregulated miRNAs. DISCUSSIONS: Together, our findings provided the key miRNAs which affected occurrence of multiple myeloma and regulation function of these miRNAs. It is valuable for the prognosis and diagnosis of multiple myeloma. PeerJ Inc. 2017-01-31 /pmc/articles/PMC5289111/ /pubmed/28168095 http://dx.doi.org/10.7717/peerj.2831 Text en ©2017 Xiang et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Genomics
Xiang, Tian
Hu, Ai-Xin
Sun, Peng
Liu, Gao
Liu, Gang
Xiao, Yan
Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets
title Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets
title_full Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets
title_fullStr Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets
title_full_unstemmed Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets
title_short Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets
title_sort identification of four potential predicting mirna biomarkers for multiple myeloma from published datasets
topic Genomics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5289111/
https://www.ncbi.nlm.nih.gov/pubmed/28168095
http://dx.doi.org/10.7717/peerj.2831
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