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Different miRNA expression profiles between human breast cancer turmors and serum

ABSTRACT: We identified total 174 significantly differentially expressed miRNAs between tumors and the normal tissues, and 109 miRNAs between serum from patients and serum from healthy individuals. There are only 10 common miRNAs. This suggests that only a small portion of tumor miRNAs are released into serum selectively. Interestingly, the expression change pattern of 28 miRNAs is opposite between breast cancer tumors and serum. Functional analysis shows that the differentially expressed miRNAs and their target genes form a complex interaction network affecting many biological processes and involving in cancer-related pathways such as prostate, basal cell carcinoma, acute myeloid leukymia, and more. A bunch of miRNAs have been demonstrated to be aberrantly expressed in cancer tumor tissue and serum. The miRNA signatures identified from the serum samples could serve as potential noninvasive diagnostic markers for breast cancer. The roles of the miRNAs in cancerigenesis is unclear. In this study, we generated the expression profiles of miRNAs from the paired breast cancer tumors, normal, tissue, and serum samples from eight patients using small RNA-sequencing. Serum samples from eight healthy individuals were used as normal controls.

ORGANISM(S): Homo sapiens

SUBMITTER: Li ZhaoYun

PROVIDER: S-ECPF-GEOD-56614 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BackgroundMicroRNAs (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.ResultsWe 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.ConclusionsThe 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.

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Different miRNA expression profiles between human breast cancer turmors and serum

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