Non-coding RNAs (ncRNAs) are regulators of intracellular and intercellular signaling in breast cancer. ncRNAs modulate intracellular signaling to control diverse cellular processes, including levels and activity of estrogen receptor α (ERα), proliferation, invasion, migration, apoptosis, and stemness. In addition, ncRNAs can be packaged into exosomes to provide intercellular communication by the transmission of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) to cells locally or systemically. This review provides an overview of the biogenesis and roles of ncRNAs: small nucleolar RNA (snRNA), circular RNAs (circRNAs), PIWI-interacting RNAs (piRNAs), miRNAs, and lncRNAs in breast cancer. Since more is known about the miRNAs and lncRNAs that are expressed in breast tumors, their established targets as oncogenic drivers and tumor suppressors will be reviewed. The focus is on miRNAs and lncRNAs identified in breast tumors, since a number of ncRNAs identified in breast cancer cells are not dysregulated in breast tumors. The identity and putative function of selected lncRNAs increased: nuclear paraspeckle assembly transcript 1 (NEAT1), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), steroid receptor RNA activator 1 (SRA1), colon cancer associated transcript 2 (CCAT2), colorectal neoplasia differentially expressed (CRNDE), myocardial infarction associated transcript (MIAT), and long intergenic non-protein coding RNA, Regulator of Reprogramming (LINC-ROR); and decreased levels of maternally-expressed 3 (MEG3) in breast tumors have been observed as well. miRNAs and lncRNAs are considered targets of therapeutic intervention in breast cancer, but further work is needed to bring the promise of regulating their activities to clinical use.

Non-Coding RNAs in Breast Cancer: Intracellular and Intercellular Communication.

In recent years, technological advances in transcriptome profiling revealed that the repertoire of human RNA molecules is more diverse and extended than originally thought. This diversity and complexity mainly derive from a large ensemble of noncoding RNAs. Because of their key roles in cellular processes important for normal development and physiology, disruption of noncoding RNA expression is intrinsically linked to human disease, including cancer. Therefore, studying the noncoding portion of the transcriptome offers the prospect of identifying novel therapeutic and diagnostic targets. Although evidence of the relevance of noncoding RNAs in cancer is accumulating, we still face many challenges when it comes to accurately profiling their expression levels. Some of these challenges are inherent to the technologies employed, whereas others are associated with characteristics of the noncoding RNAs themselves. In this review, we discuss the challenges related to long noncoding RNA expression profiling, highlight how cancer long noncoding RNAs provide new opportunities for cancer diagnosis and treatment, and reflect on future developments.

Long noncoding RNA expression profiling in cancer: Challenges and opportunities.

RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.

/pdf/systematic-comparison-and-assessment-of-rna-seq-procedures-3x6mk1jsnh.pdf

Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis.

Dozens of normalization methods for correcting experimental variation and bias in high-throughput expression data have been developed during the last two decades. Up to 23 methods among them consider the skewness of expression data between sample states, which are even more than the conventional methods, such as loess and quantile. From the perspective of reference selection, we classified the normalization methods for skewed expression data into three categories, data-driven reference, foreign reference, and entire gene set. We separately introduced and summarized these normalization methods designed for gene expression data with global shift between compared conditions, including both microarray and RNA-seq, based on the reference selection strategies. To our best knowledge, this is the most comprehensive review of available preprocessing algorithms for the unbalanced transcriptome data. The anatomy and summarization of these methods shed light on the understanding and appropriate application of preprocessing methods.

/pdf/normalization-methods-for-the-analysis-of-unbalanced-2be9df6kt6.pdf

Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review.

In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.

/pdf/on-the-utility-of-rna-sample-pooling-to-optimize-cost-and-wveo1lj1h0.pdf

On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

Long non-coding RNAs (lncRNAs) are typically expressed at low levels and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 25 pipelines for testing DE in RNA-seq data is comprehensively evaluated, with a particular focus on lncRNAs and low-abundance mRNAs. Fifteen performance metrics are used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets. Gene expression data are simulated using non-parametric procedures in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, results for mRNA and lncRNA were tracked separately. All the pipelines exhibit inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and benchmark RNA-seq datasets. The substandard performance of DE tools for lncRNAs applies also to low-abundance mRNAs. No single tool uniformly outperformed the others. Variability, number of samples, and fraction of DE genes markedly influenced DE tool performance. Overall, linear modeling with empirical Bayes moderation (limma) and a non-parametric approach (SAMSeq) showed good control of the false discovery rate and reasonable sensitivity. Of note, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in realistic settings such as in clinical cancer research. About half of the methods showed a substantial excess of false discoveries, making these methods unreliable for DE analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, giving guidance on selection of the optimal DE tool (
 http://statapps.ugent.be/tools/AppDGE/
 ).

https://biblio.ugent.be/publication/8599289/file/8599322.pdf

Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data.

SUMMARY SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios, including different sample sizes, biological signals (differential expression) and confounding batch effects. AVAILABILITY AND IMPLEMENTATION The R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data.

An amendment to this paper has been published and can be accessed via the original article.

/pdf/correction-to-on-the-utility-of-rna-sample-pooling-to-82deo9iuud.pdf

Correction to: On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments.

Summary SPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts. Availability and implementation The R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq. Supplementary information Supplementary data are available at bioRχiv online.

https://www.biorxiv.org/content/biorxiv/early/2019/06/21/677740.full.pdf

Alemu Takele Assefa

Papers

On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data.

SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data.

Correction to: On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments.

SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data