BACKGROUND: MicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA-seq) is a Next Generation Sequencing (NGS) method for the analysis of differential gene expression. Using machine learning algorithms, it is possible to improve the functional significance interpretation of miRNA in the analysis and interpretation of data from RNA-seq. Furthermore, we tried to identify some patterns of deregulated miRNA in human breast cancer (BC), in order to give a contribution in the understanding of this type of cancer at the molecular level. RESULTS: We adopted a biclustering approach, using the Iterative Signature Algorithm (ISA) algorithm, in order to evaluate miRNA deregulation in the context of miRNA abundance and tissue heterogeneity. These are important elements to identify miRNAs that would be useful as prognostic and diagnostic markers. Considering a real word breast cancer dataset, the evaluation of miRNA differential expressions in tumours versus healthy tissues evidenced 12 different miRNA clusters, associated to specific groups of patients. The identified miRNAs were deregulated in breast tumours compared to healthy controls. Our approach has shown the association between specific sub-class of tumour samples having the same immuno-histo-chemical and/or histological features. Biclusters have been validated by means of two online repositories, MetaMirClust database and UCSC Genome Browser, and using another biclustering algorithm. CONCLUSIONS: The obtained results with biclustering algorithm aimed first of all to give a contribute in the differential expression analysis in a cohort of BC patients and secondly to support the potential role that these non-coding RNA molecules could play in the clinical practice, in terms of prognosis, evolution of tumour and treatment response.
BACKGROUND: MicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA-seq) is a Next Generation Sequencing (NGS) method for the analysis of differential gene expression. Using machine learning algorithms, it is possible to improve the functional significance interpretation of miRNA in the analysis and interpretation of data from RNA-seq. Furthermore, we tried to identify some patterns of deregulated miRNA in humanbreast cancer (BC), in order to give a contribution in the understanding of this type of cancer at the molecular level. RESULTS: We adopted a biclustering approach, using the Iterative Signature Algorithm (ISA) algorithm, in order to evaluate miRNA deregulation in the context of miRNA abundance and tissue heterogeneity. These are important elements to identify miRNAs that would be useful as prognostic and diagnostic markers. Considering a real word breast cancer dataset, the evaluation of miRNA differential expressions in tumours versus healthy tissues evidenced 12 different miRNA clusters, associated to specific groups of patients. The identified miRNAs were deregulated in breast tumours compared to healthy controls. Our approach has shown the association between specific sub-class of tumour samples having the same immuno-histo-chemical and/or histological features. Biclusters have been validated by means of two online repositories, MetaMirClust database and UCSC Genome Browser, and using another biclustering algorithm. CONCLUSIONS: The obtained results with biclustering algorithm aimed first of all to give a contribute in the differential expression analysis in a cohort of BC patients and secondly to support the potential role that these non-coding RNA molecules could play in the clinical practice, in terms of prognosis, evolution of tumour and treatment response.
Authors: Aaron J Schetter; Suet Yi Leung; Jane J Sohn; Krista A Zanetti; Elise D Bowman; Nozomu Yanaihara; Siu Tsan Yuen; Tsun Leung Chan; Dora L W Kwong; Gordon K H Au; Chang-Gong Liu; George A Calin; Carlo M Croce; Curtis C Harris Journal: JAMA Date: 2008-01-30 Impact factor: 56.272
Authors: Pauline A Fujita; Brooke Rhead; Ann S Zweig; Angie S Hinrichs; Donna Karolchik; Melissa S Cline; Mary Goldman; Galt P Barber; Hiram Clawson; Antonio Coelho; Mark Diekhans; Timothy R Dreszer; Belinda M Giardine; Rachel A Harte; Jennifer Hillman-Jackson; Fan Hsu; Vanessa Kirkup; Robert M Kuhn; Katrina Learned; Chin H Li; Laurence R Meyer; Andy Pohl; Brian J Raney; Kate R Rosenbloom; Kayla E Smith; David Haussler; W James Kent Journal: Nucleic Acids Res Date: 2010-10-18 Impact factor: 16.971
Authors: Yi Luo; Daniel McShan; Dipankar Ray; Martha Matuszak; Shruti Jolly; Theodore Lawrence; Feng Ming Kong; Randall Ten Haken; Issam El Naqa Journal: IEEE Trans Radiat Plasma Med Sci Date: 2018-05-02
Authors: Sipko van Dam; Urmo Võsa; Adriaan van der Graaf; Lude Franke; João Pedro de Magalhães Journal: Brief Bioinform Date: 2018-07-20 Impact factor: 11.622