Analysis of miRNA expression profiles in breast cancer using biclustering
Open accessAbstract
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.
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Differential co-expression analysis identifies genes with varying co-expression partners under different conditions.
“An increasingly used method that goes beyond traditional co-expression networks is differential co-expression analysis [4–7]. This approach identifies genes with varying co-expression partners under different conditions, such as disease states [4, 8–10], tissue types [11] and developmental stages [12], because these genes are more likely to be regulators that underlie phenotypic differences.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [7]
The Background (page unavailable) says biclustering can place a gene in different co-regulated groups under different conditions, supporting the idea of changing partners. The paper describes biclustering of miRNA expression, however, rather than identifying its approach as differential co-expression analysis. (LLM, conf=0.82)
Experimental — accuracy not yet measuredBiclustering is more effective than other co-expression analysis methods for disentangling mechanisms when predefining biologically relevant sample groups is difficult.
“For this purpose, biclustering is more effective than other co-expression analysis methods [7].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [7]
The Background and Methods explain why biclustering can reveal co-regulated features and sample subsets that traditional clustering, which considers all conditions or features, may miss. The Experimental results also describe obtaining an association in one step rather than through multiple hierarchical-clustering steps. The paper does not establish that biclustering is more effective than other co-expression analysis methods generally, or specifically assess cases where biologically relevant sample groups are difficult to predefine; no page numbers are available in our copy. (LLM, conf=0.84)
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Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
- Authors
- Fiannaca, A., La Rosa, M., La Paglia, L., Rizzo, R., & Urso, A.
- Affiliations
- National Research Council (IT)
- Corresponding author
- Fiannaca, Antonino
- Venue
- BMC Bioinformatics
- Published
- 2015-02-23
- Volume
- 16
- Issue
- S4
- Pages
- S7-S7
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC4347621
- OpenAlex
- W1528850234
- Cited by
- 45
- References
- 70
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2022: 1 · 2021: 5 · 2020: 4 · 2019: 15 · 2018: 6 · 2017: 2 · 2016: 5 · 2015: 1
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- microRNA expression, breast cancer, biclustering, miRNA differential expression, tissue heterogeneity, prognostic factors, diagnostic biomarkers, non-coding RNA, tumor subtypes
- MeSH terms
- Algorithms, Breast Neoplasms, Female, Humans, Genome, Human, Gene Expression Regulation, Neoplastic, Gene Expression Profiling, MicroRNAs, High-Throughput Nucleotide Sequencing
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