Dataset Information

A bioinformatics approach to identify novel long, non-coding RNAs in breast cancer cell lines from an existing RNA-sequencing dataset.

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.

SUBMITTER: Zaheed O

PROVIDER: S-EPMC7078458 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A bioinformatics approach to identify novel long, non-coding RNAs in breast cancer cell lines from an existing RNA-sequencing dataset.

Zaheed Oza O Samson Julia J Dean Kellie K

Non-coding RNA research 20200224 2

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 ...[more]

PMID: 32206740

Similar Datasets

Project description:Melanoma, which is usually induced by ultraviolet light exposure and the following DNA damage, is the most dangerous skin cancer. The purpose of the present study was to screen key molecules involved in melanoma.Microarray data of E-MTAB-1862 were downloaded from the ArrayExpress database, which included 21 primary melanoma samples and 11 benign nevus samples. In addition, the RNASeq version 2 and microRNA (miRNA) sequencing data of cutaneous melanoma were downloaded from The Cancer Genome Atlas database. After identifying the differentially expressed genes (DEGs) using Limma package, enrichment analysis and protein-protein interaction (PPI) network analysis were performed separately for them using DAVID software and Cytoscape software. In addition, survival analysis and regulatory network analysis were further performed by log-rank test and Cytoscape software, respectively. Moreover, real-time reverse transcription polymerase chain reaction (RT-PCR) was performed to further verify the expression patterns of several selected DEGs.A total of 382 DEGs were identified in primary melanoma samples, including 206 upregulated genes and 176 downregulated genes. Functional enrichment analysis showed that COL17A1 was enriched in epidermis development. In the PPI network, CXCL8 (degree?=?29) and STAT1 (degree?=?28) had higher degrees and could interact with each other. Survival analysis showed that 21 DEGs, 55 long noncoding RNAs (lncRNAs) and 32 miRNAs were found to be associated with prognosis. Furthermore, several regulatory relationships were found in the lncRNA-gene regulatory network (such as RP11-361L15.4 targeting COL17A1) and the miRNA-gene regulatory network (such as hsa-miR-375 targeting CCL27 and hsa-miR-375 targeting insulin-like growth factor 1 receptor [IGF1R]). Real-time RT-PCR results showed that the overall direction of differential expression was consistent except COL17A1.CXCL8 interacted with STAT1, CCL27, and IGF1R targeted by hsa-miR-375, and COL17A1 targeted by RP11-361L15.4 might function in the development and progression of melanoma, which should be verified by more detailed experiments.

Project description:Thyroid cancer (TC) is the most common endocrine cancer, accounting for 1.7% of all cancer cases. It has been reported that the existing approach to diagnosing TC is problematic. Therefore, it is essential to develop molecular biomarkers to improve the accuracy of the diagnosis. This study aimed to screen hub lncRNAs in the ceRNA network (ceRNET) connected to TC formation and progression based on the overall survival rate. In this study, first, RNA-seq data from the GDC database were collected. A package called edgeR in R programming language was then used to obtain differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) in TC patients' samples compared to normal samples. Second, DEmRNAs were analyzed for their functional enrichment. Third, to identify RNAs associated with overall survival, the overall survival of these RNAs was analyzed using the Kaplan-Meier plotter database to create a survival associated with the ceRNA network (survival-related ceRNET). Next, the GeneMANIA plugin was used to construct a PPI network to better understand survival-related DEmRNA interactions. The survival ceRNET was then visualized with the Cytoscape software, and hub genes, including hub lncRNAs and hub mRNAs, were identified using the CytoHubba plugin. We found 45 DElncRNAs, 28 DEmiRNAs, and 723 DEmRNAs among thyroid tumor tissue and non-tumor tissue samples. According to KEGG, GO and DO analyses, 723 DEmRNAs were mainly enriched in cancer-related pathways. Importantly, the results found that ten DElncRNAs, four DEmiRNAs, and 68 DEmRNAs are associated with overall survival. In this account, the PPI network was constructed for 68 survival-related DEmRNAs, and ADAMTS9, DTX4, and CLDN10 were identified as hub genes. The ceRNET was created by combining six lncRNAs, 109 miRNAs, and 22 mRNAs related to survival using Cytoscape. in this network, ten hub RNAs were identified by the CytoHubba plugin, including mRNAs (CTXND1, XKRX, IGFBP2, ENTPD1, GALNT7, ADAMTS9) and lncRNAs (AC090673.1, AL162511.1, LINC02454, AL365259.1). This study suggests that three lncRNAs, including AL162511.1, AC090673.1, and AL365259.1, could be reliable diagnostic biomarkers for TC. The findings of this study provide a basis for future studies on the therapeutic potential of these lncRNAs.

Project description:BackgroundGene profiling of diffuse large B cell lymphoma (DLBCL) has revealed broad gene expression deregulation compared to normal B cells. While many studies have interrogated well known and annotated genes in DLBCL, none have yet performed a systematic analysis to uncover novel unannotated long non-coding RNAs (lncRNA) in DLBCL. In this study we sought to uncover these lncRNAs by examining RNA-seq data from primary DLBCL tumors and performed supporting analysis to identify potential role of these lncRNAs in DLBCL.MethodsWe performed a systematic analysis of novel lncRNAs from the poly-adenylated transcriptome of 116 primary DLBCL samples. RNA-seq data were processed using de novo transcript assembly pipeline to discover novel lncRNAs in DLBCL. Systematic functional, mutational, cross-species, and co-expression analyses using numerous bioinformatics tools and statistical analysis were performed to characterize these novel lncRNAs.ResultsWe identified 2,632 novel, multi-exonic lncRNAs expressed in more than one tumor, two-thirds of which are not expressed in normal B cells. Long read single molecule sequencing supports the splicing structure of many of these lncRNAs. More than one-third of novel lncRNAs are differentially expressed between the two major DLBCL subtypes, ABC and GCB. Novel lncRNAs are enriched at DLBCL super-enhancers, with a fraction of them conserved between human and dog lymphomas. We see transposable elements (TE) overlap in the exonic regions; particularly significant in the last exon of the novel lncRNAs suggest potential usage of cryptic TE polyadenylation signals. We identified highly co-expressed protein coding genes for at least 88 % of the novel lncRNAs. Functional enrichment analysis of co-expressed genes predicts a potential function for about half of novel lncRNAs. Finally, systematic structural analysis of candidate point mutations (SNVs) suggests that such mutations frequently stabilize lncRNA structures instead of destabilizing them.ConclusionsDiscovery of these 2,632 novel lncRNAs in DLBCL significantly expands the lymphoma transcriptome and our analysis identifies potential roles of these lncRNAs in lymphomagenesis and/or tumor maintenance. For further studies, these novel lncRNAs also provide an abundant source of new targets for antisense oligonucleotide pharmacology, including shared targets between human and dog lymphomas.

Dataset Information

A bioinformatics approach to identify novel long, non-coding RNAs in breast cancer cell lines from an existing RNA-sequencing dataset.

Publications

A bioinformatics approach to identify novel long, non-coding RNAs in breast cancer cell lines from an existing RNA-sequencing dataset.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets