A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer

Abstract The aberrant alterations of biological functions are well known in tumorigenesis and cancer development. Hence, with advances in high-throughput sequencing technologies, capturing and quantifying the functional alterations in cancers based on expression profiles to explore cancer malignant...

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Autores principales:	Tianci Song, Sha Cao, Sheng Tao, Sen Liang, Wei Du, Yanchun Liang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e2ec18a198cd43adb7b12ece8aa8dc60
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spelling	oai:doaj.org-article:e2ec18a198cd43adb7b12ece8aa8dc602021-12-02T16:06:33ZA Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer10.1038/s41598-017-04961-62045-2322https://doaj.org/article/e2ec18a198cd43adb7b12ece8aa8dc602017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04961-6https://doaj.org/toc/2045-2322Abstract The aberrant alterations of biological functions are well known in tumorigenesis and cancer development. Hence, with advances in high-throughput sequencing technologies, capturing and quantifying the functional alterations in cancers based on expression profiles to explore cancer malignant process is highlighted as one of the important topics among cancer researches. In this article, we propose an algorithm for quantifying biological processes by using gene expression profiles over a sample population, which involves the idea of constructing principal curves to condense information of each biological process by a novel scoring scheme on an individualized manner. After applying our method on several large-scale breast cancer datasets in survival analysis, a subset of these biological processes extracted from corresponding survival model is then found to have significant associations with clinical outcomes. Further analyses of these biological processes enable the study of the interplays between biological processes and cancer phenotypes of interest, provide us valuable insights into cancer biology in biological process level and guide the precision treatment for cancer patients. And notably, prognosis predictions based on our method are consistently superior to the existing state of art methods with the same intention.Tianci SongSha CaoSheng TaoSen LiangWei DuYanchun LiangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tianci Song Sha Cao Sheng Tao Sen Liang Wei Du Yanchun Liang A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer
description	Abstract The aberrant alterations of biological functions are well known in tumorigenesis and cancer development. Hence, with advances in high-throughput sequencing technologies, capturing and quantifying the functional alterations in cancers based on expression profiles to explore cancer malignant process is highlighted as one of the important topics among cancer researches. In this article, we propose an algorithm for quantifying biological processes by using gene expression profiles over a sample population, which involves the idea of constructing principal curves to condense information of each biological process by a novel scoring scheme on an individualized manner. After applying our method on several large-scale breast cancer datasets in survival analysis, a subset of these biological processes extracted from corresponding survival model is then found to have significant associations with clinical outcomes. Further analyses of these biological processes enable the study of the interplays between biological processes and cancer phenotypes of interest, provide us valuable insights into cancer biology in biological process level and guide the precision treatment for cancer patients. And notably, prognosis predictions based on our method are consistently superior to the existing state of art methods with the same intention.
format	article
author	Tianci Song Sha Cao Sheng Tao Sen Liang Wei Du Yanchun Liang
author_facet	Tianci Song Sha Cao Sheng Tao Sen Liang Wei Du Yanchun Liang
author_sort	Tianci Song
title	A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer
title_short	A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer
title_full	A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer
title_fullStr	A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer
title_full_unstemmed	A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer
title_sort	novel unsupervised algorithm for biological process-based analysis on cancer
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e2ec18a198cd43adb7b12ece8aa8dc60
work_keys_str_mv	AT tiancisong anovelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT shacao anovelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT shengtao anovelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT senliang anovelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT weidu anovelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT yanchunliang anovelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT tiancisong novelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT shacao novelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT shengtao novelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT senliang novelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT weidu novelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer AT yanchunliang novelunsupervisedalgorithmforbiologicalprocessbasedanalysisoncancer
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A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer

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