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A novel serum miRNA-pair classifier for diagnosis of sarcoma
Soft tissue sarcomas (STS) is a set of rare malignant tumor originated from mesoderm. For the prognosis of sarcoma, early diagnosis is important, however, currently no mature and non-invasive method for diagnosis exists. MicroRNAs (miRNAs) are a class of noncoding RNAs and their expression varies gr...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365454/ https://www.ncbi.nlm.nih.gov/pubmed/32673360 http://dx.doi.org/10.1371/journal.pone.0236097 |
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author | Jin, Zheng Liu, Shanshan Zhu, Pei Tang, Mengyan Wang, Yuanxin Tian, Yuan Li, Dong Zhu, Xun Yan, Dongmei Zhu, Zhenhua |
author_facet | Jin, Zheng Liu, Shanshan Zhu, Pei Tang, Mengyan Wang, Yuanxin Tian, Yuan Li, Dong Zhu, Xun Yan, Dongmei Zhu, Zhenhua |
author_sort | Jin, Zheng |
collection | PubMed |
description | Soft tissue sarcomas (STS) is a set of rare malignant tumor originated from mesoderm. For the prognosis of sarcoma, early diagnosis is important, however, currently no mature and non-invasive method for diagnosis exists. MicroRNAs (miRNAs) are a class of noncoding RNAs and their expression varies greatly, especially during tumor activity. The purpose of this study was to construct a predictive model for the diagnosis of sarcomas based on the relative expression level of miRNA in serum. miRNA array expression data of 677 samples including 402 malignant sarcoma samples and 275 healthy samples was used to construct the prediction model. Based on 6 gene pairs, random generalized linear model (RGLM) was constructed, with an accuracy of 100% in the internal test dataset and of 74.3% in the merged external dataset in prediction whether a serum sample was obtained from a sarcoma patient, with a specificity of 100% in the internal test dataset and 90.5% in the external dataset. In conclusion, our serum miRNA-pair classifier has the potential to be used for the screening of sarcoma with high accuracy and specificity. |
format | Online Article Text |
id | pubmed-7365454 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73654542020-08-05 A novel serum miRNA-pair classifier for diagnosis of sarcoma Jin, Zheng Liu, Shanshan Zhu, Pei Tang, Mengyan Wang, Yuanxin Tian, Yuan Li, Dong Zhu, Xun Yan, Dongmei Zhu, Zhenhua PLoS One Research Article Soft tissue sarcomas (STS) is a set of rare malignant tumor originated from mesoderm. For the prognosis of sarcoma, early diagnosis is important, however, currently no mature and non-invasive method for diagnosis exists. MicroRNAs (miRNAs) are a class of noncoding RNAs and their expression varies greatly, especially during tumor activity. The purpose of this study was to construct a predictive model for the diagnosis of sarcomas based on the relative expression level of miRNA in serum. miRNA array expression data of 677 samples including 402 malignant sarcoma samples and 275 healthy samples was used to construct the prediction model. Based on 6 gene pairs, random generalized linear model (RGLM) was constructed, with an accuracy of 100% in the internal test dataset and of 74.3% in the merged external dataset in prediction whether a serum sample was obtained from a sarcoma patient, with a specificity of 100% in the internal test dataset and 90.5% in the external dataset. In conclusion, our serum miRNA-pair classifier has the potential to be used for the screening of sarcoma with high accuracy and specificity. Public Library of Science 2020-07-16 /pmc/articles/PMC7365454/ /pubmed/32673360 http://dx.doi.org/10.1371/journal.pone.0236097 Text en © 2020 Jin 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, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Jin, Zheng Liu, Shanshan Zhu, Pei Tang, Mengyan Wang, Yuanxin Tian, Yuan Li, Dong Zhu, Xun Yan, Dongmei Zhu, Zhenhua A novel serum miRNA-pair classifier for diagnosis of sarcoma |
title | A novel serum miRNA-pair classifier for diagnosis of sarcoma |
title_full | A novel serum miRNA-pair classifier for diagnosis of sarcoma |
title_fullStr | A novel serum miRNA-pair classifier for diagnosis of sarcoma |
title_full_unstemmed | A novel serum miRNA-pair classifier for diagnosis of sarcoma |
title_short | A novel serum miRNA-pair classifier for diagnosis of sarcoma |
title_sort | novel serum mirna-pair classifier for diagnosis of sarcoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365454/ https://www.ncbi.nlm.nih.gov/pubmed/32673360 http://dx.doi.org/10.1371/journal.pone.0236097 |
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