sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses

Abstract MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating ge...

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Autores principales:	Chun-Long Zhang, Yan-Jun Xu, Hai-Xiu Yang, Ying-Qi Xu, De-Si Shang, Tan Wu, Yun-Peng Zhang, Xia Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c4363fc33e9f47c5a9db99a437a4038e
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spelling	oai:doaj.org-article:c4363fc33e9f47c5a9db99a437a4038e2021-12-02T15:06:03ZsPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses10.1038/s41598-017-15631-y2045-2322https://doaj.org/article/c4363fc33e9f47c5a9db99a437a4038e2017-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-15631-yhttps://doaj.org/toc/2045-2322Abstract MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients’ prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.Chun-Long ZhangYan-Jun XuHai-Xiu YangYing-Qi XuDe-Si ShangTan WuYun-Peng ZhangXia LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chun-Long Zhang Yan-Jun Xu Hai-Xiu Yang Ying-Qi Xu De-Si Shang Tan Wu Yun-Peng Zhang Xia Li sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses
description	Abstract MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients’ prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.
format	article
author	Chun-Long Zhang Yan-Jun Xu Hai-Xiu Yang Ying-Qi Xu De-Si Shang Tan Wu Yun-Peng Zhang Xia Li
author_facet	Chun-Long Zhang Yan-Jun Xu Hai-Xiu Yang Ying-Qi Xu De-Si Shang Tan Wu Yun-Peng Zhang Xia Li
author_sort	Chun-Long Zhang
title	sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses
title_short	sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses
title_full	sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses
title_fullStr	sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses
title_full_unstemmed	sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses
title_sort	spagm: inferring subpathway activity by integrating gene and mirna expression-robust functional signature identification for melanoma prognoses
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/c4363fc33e9f47c5a9db99a437a4038e
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sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses

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