Construction and investigation of breast‐cancer‐specific ceRNA network based on the mRNA and miRNA expression data

Abstract BACKGROUND Different technologies, such as quantitative real-time PCR or microarrays, have been developed to measure microRNA (miRNA) expression levels. Quantification of miRNA transcripts implicates data normalization using endogenous and exogenous reference genes for data correction. However, there is no consensus about an optimal normalization strategy. The choice of a reference gene remains problematic and can have a serious impact on the actual available transcript levels and, consequently, on the biological interpretation of data. CONTENT In this review article we discuss the reliability of the use of small RNAs, commonly reported in the literature as miRNA expression normalizers, and compare different strategies used for data normalization. SUMMARY A workflow strategy is proposed for normalization of miRNA expression data in an attempt to provide a basis for the establishment of a global standard procedure that will allow comparison across studies.

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Decision tree‑based classifiers for lung cancer diagnosis and subtyping using TCGA miRNA expression data

Oncology Letters ◽

10.3892/ol.2019.10462 ◽

2019 ◽

Cited By ~ 3

Author(s):

Masih Sherafatian ◽

Fateme Arjmand

Keyword(s):

Lung Cancer ◽

Decision Tree ◽

Cancer Diagnosis ◽

Mirna Expression ◽

Expression Data ◽

Lung Cancer Diagnosis ◽

Mirna Expression Data

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miRNA Expression Analysis: Cell Lines HCC1500 and HCC1937 as Models for Breast Cancer in Young Women and the miR-23a as a Poor Prognostic Biomarker

Breast Cancer Basic and Clinical Research ◽

10.1177/1178223420977845 ◽

2020 ◽

Vol 14 ◽

pp. 117822342097784

Author(s):

Sara S Oltra ◽

Maria Peña-Chilet ◽

Maria T Martinez ◽

Eduardo Tormo ◽

Juan Miguel Cejalvo ◽

...

Keyword(s):

Breast Cancer ◽

Cell Lines ◽

Young Women ◽

Mirna Expression ◽

Young Patients ◽

Breast Cancer Patients ◽

Old Patients ◽

Cellular Models ◽

Hcc1937 Cell ◽

Mirna Expression Data

Purpose: The study of breast cancer nearly always involves patients close to menopause or older. Therefore, young patients are mostly underrepresented. Our aim in this study was to demonstrate biological differences in breast cancer of young people using as a model available cell lines derived from people with breast cancer younger than 35 years. Methods: Global miRNA expression was analyzed in breast cancer cells from young (HCC1500, HCC1937) and old patients (MCF-7, MDA-MB-231, HCC1806, and MDA-MB-468). In addition, it was compared with same type of results from patients. Results: We observed a differential profile for 155 miRNAs between young and older cell lines. We identified a set of 24 miRNA associated with aggressiveness that were regulating pluripotency of stem cell-related pathways. Combining the miRNA expression data from cell lines and breast cancer patients, 132 miRNAs were differently expressed between young and old samples, most of them previously found in cell lines. MiR-23a-downregulation was also associated with poor survival in young patients. Conclusions: Our results suggest that HCC1500 and HCC1937 cell lines could be suitable cellular models for breast cancer affecting young women. The miR-23a-downregulation could have a potential role as a poor prognosis biomarker in this age group.

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mirEX: a platform for comparative exploration of plant pri-miRNA expression data

Nucleic Acids Research ◽

10.1093/nar/gkr878 ◽

2011 ◽

Vol 40 (D1) ◽

pp. D191-D197 ◽

Cited By ~ 31

Author(s):

Dawid Bielewicz ◽

Jakub Dolata ◽

Andrzej Zielezinski ◽

Sylwia Alaba ◽

Bogna Szarzynska ◽

...

Keyword(s):

Mirna Expression ◽

Expression Data ◽

Mirna Expression Data

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Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

BMC Bioinformatics ◽

10.1186/s12859-021-04215-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Xiaomei Li ◽

Buu Truong ◽

Taosheng Xu ◽

Lin Liu ◽

Jiuyong Li ◽

...

Keyword(s):

Breast Cancer ◽

R Package ◽

Breast Cancer Prognosis ◽

Cancer Prognosis ◽

Breast Cancer Subtypes ◽

Expression Data ◽

Mrna Expression Data ◽

Cancer Subtypes ◽

Lncrna Expression ◽

Mirna Expression Data

Abstract Background Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice. Results In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation. Conclusions The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

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