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Published on: December 9, 2016
A bioinformatics approach to identify novel long, non-coding RNAs in breast cancer cell lines from an existing
Oza Zaheed1, Julia Samson1, Kellie Dean1
1School of Biochemistry and Cell Biology, Western Gateway Building, University College Cork, Cork, T12XF62, Ireland.
Abstract:
Breast cancer research has traditionally centred on genomic alterations, hormone receptor status and changes in cancer-related proteins to provide new avenues for targeted therapies. Due to advances in next generation sequencing technologies, there has been the emergence of long, non-coding RNAs (lncRNAs) as regulators of normal cellular events, with links to various disease states, including breast cancer. Here we describe our bioinformatic analyses of a previously published RNA sequencing (RNA-seq) dataset to identify lncRNAs with altered expression levels in a subset of breast cancer cell lines. Using a previously published RNA-seq dataset of 675 cancer cell lines, a subset of 18 cell lines was selected for our analyses that included 16 breast cancer lines, one ductal carcinoma in situ line and one normal-like breast epithelial cell line. Principal component analysis demonstrated correlation with well-established categorisation methods of breast cancer (i.e. luminal A/B, HER2 enriched and basal-like A/B). Through detailed comparison of differentially expressed lncRNAs in each breast cancer sub-type with normal-like breast epithelial cells, we identified 15 lncRNAs with consistently altered expression, including three uncharacterised lncRNAs. Utilising data from The Cancer Genome Atlas (TCGA) and The Genotype Tissue Expression (GETx) project via Gene Expression Profiling Interactive Analysis (GEPIA2), we assessed clinical relevance of several identified lncRNAs with invasive breast cancer. Lastly, we determined the relative expression level of six lncRNAs across a spectrum of breast cancer cell lines to experimentally confirm the findings of our bioinformatic analyses. Overall, we show that the use of existing RNA-seq datasets, if re-analysed with modern bioinformatic tools, can provide a valuable resource to identify lncRNAs that could have important biological roles in oncogenesis and tumour progression.
Insights
Researchers identified 15 long non-coding RNAs (lncRNAs) with altered expression in breast cancer cell lines. Re-analyzing existing RNA sequencing data offers a valuable resource for discovering novel lncRNAs in cancer progression.
Area of Science:
- * Molecular biology
- * Bioinformatics
- * Oncology
Background:
- * Traditional breast cancer research focuses on genomic alterations and protein changes for targeted therapies.
- * Long non-coding RNAs (lncRNAs) are emerging as key regulators in cellular events and disease, including breast cancer.
Purpose of the Study:
- * To identify lncRNAs with altered expression in breast cancer cell lines using bioinformatic analysis of existing RNA sequencing data.
- * To assess the clinical relevance of identified lncRNAs in invasive breast cancer.
- * To experimentally validate the expression levels of key lncRNAs.
Main Methods:
- * Bioinformatic analysis of a published RNA sequencing dataset from 18 cell lines (16 breast cancer, 1 ductal carcinoma in situ, 1 normal-like breast epithelial).
- * Principal component analysis for breast cancer sub-typing (luminal A/B, HER2, basal-like A/B).
- * Differential expression analysis comparing breast cancer subtypes to normal-like cells.
- * Validation using The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GETx) via GEPIA2.
- * Experimental confirmation of lncRNA expression levels in breast cancer cell lines.
Main Results:
- * Principal component analysis correlated with established breast cancer classifications.
- * 15 lncRNAs showed consistently altered expression across breast cancer subtypes compared to normal-like cells, including three uncharacterized lncRNAs.
- * Clinical relevance of several lncRNAs was assessed using TCGA and GETx data.
- * Relative expression of six lncRNAs was determined experimentally, confirming bioinformatic findings.
Conclusions:
- * Re-analysis of existing RNA sequencing datasets with modern bioinformatic tools is a valuable strategy for identifying novel lncRNAs in breast cancer.
- * Identified lncRNAs may play significant roles in breast cancer oncogenesis and tumor progression.
- * This study highlights the potential of lncRNAs as therapeutic targets or biomarkers in breast cancer.
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