Noncoding RNA: Current Deep Sequencing Data Analysis Approaches and Challenges

Veneziano, Dario; Bella, Sebastiano Di; Nigita, Giovanni; Laganà, Antonio Simone; Ferro, Afredo; Croce, Carlo M.

doi:10.1002/humu.23066

Cited by 75 publications

(56 citation statements)

References 192 publications

(231 reference statements)

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“…RNA-sequencing technologies have enabled the discovery of novel categories of non-coding RNA (ncRNAs) (1). Among ncRNAs, microRNAs (miRNAs) are the best studied to date (2–9), having been linked to a wide range of processes (10–17) as well as conditions and diseases (18–20), including cancer (21,22).…”

Section: Introductionmentioning

confidence: 99%

Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types

Telonis

Magee

Loher

et al. 2017

Nucleic Acids Research

175

184

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Isoforms of human miRNAs (isomiRs) are constitutively expressed with tissue- and disease-subtype-dependencies. We studied 10 271 tumor datasets from The Cancer Genome Atlas (TCGA) to evaluate whether isomiRs can distinguish amongst 32 TCGA cancers. Unlike previous approaches, we built a classifier that relied solely on ‘binarized’ isomiR profiles: each isomiR is simply labeled as ‘present’ or ‘absent’. The resulting classifier successfully labeled tumor datasets with an average sensitivity of 90% and a false discovery rate (FDR) of 3%, surpassing the performance of expression-based classification. The classifier maintained its power even after a 15× reduction in the number of isomiRs that were used for training. Notably, the classifier could correctly predict the cancer type in non-TCGA datasets from diverse platforms. Our analysis revealed that the most discriminatory isomiRs happen to also be differentially expressed between normal tissue and cancer. Even so, we find that these highly discriminating isomiRs have not been attracting the most research attention in the literature. Given their ability to successfully classify datasets from 32 cancers, isomiRs and our resulting ‘Pan-cancer Atlas’ of isomiR expression could serve as a suitable framework to explore novel cancer biomarkers.

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Section: Introductionmentioning

confidence: 99%

Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types

Telonis

Magee

Loher

et al. 2017

Nucleic Acids Research

175

184

View full text Add to dashboard Cite

show abstract

“…Throughout this era, the field of non-coding RNAs (ncRNAs) enjoyed very significant progress. In fact, RNA-sequencing technologies helped uncovered many novel categories of short and long RNA transcripts (2). In the process, they also revealed multiple layers of regulatory processes.…”

Section: Introductionmentioning

confidence: 99%

The presence or absence alone of miRNA isoforms (isomiRs) successfully discriminate amongst the 32 TCGA cancer types

Telonis

Magee

Loher

et al. 2016

Preprint

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Previously, we demonstrated that miRNA isoforms (isomiRs) are constitutive and their expression profiles depend on tissue, tissue state, and disease subtype. We have now extended our isomiR studies to The Cancer Genome Atlas (TCGA) repository. Specifically, we studied whether isomiR profiles can distinguish amongst the 32 cancers. We analyzed 10,271 datasets from 32 cancers and found 7,466 isomiRs from 807 miRNA hairpin-arms to be expressed above threshold.Using the top 20% most abundant isomiRs, we built a classifier that relied on "binary" isomiR profiles: isomiRs were simply represented as 'present' or 'absent' and, unlike previous methods, all knowledge about their expression levels was ignored. The classifier could label tumor samples with an average sensitivity of 93% and a False Discovery Rate of 3%. Notably, its ability to classify well persisted even when we reduced the set of used features (=isomiRs) by a factor of 10. A counterintuitive finding of our analysis is that the isomiRs and miRNA loci with the highest ability to classify tumors are not the ones that have been attracting the most research attention in the miRNA field. Our results provide a framework in which to study cancer-type-specific isomiRs and explore their potential uses as cancer biomarkers

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“…More recently, next-generation sequencing allows us to discover new non-coding RNA species expressed in eukaryotic cells and some computational algorithms can predict circular RNAs (circRNAs), which are most commonly found at back-splicing junctions [94,95]. circRNA is a type of single-stranded RNA generally formed by alternative splicing of pre-mRNA where the 5 upstream splice acceptor is joined to a 3 downstream splice donor in a process called 'backsplicing'.…”

Section: Mutant P53 and Circular Rnasmentioning

confidence: 99%

The Impact of Mutant p53 in the Non-Coding RNA World

Agostino

2020

Biomolecules

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Long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), micro RNAs (miRNAs), and extracellular RNAs (exRNAs) are new groups of RNAs with regulation activities that have low or no protein-coding ability. Emerging evidence suggests that deregulated expression of these non-coding RNAs is associated with the induction and progression of diverse tumors throughout epigenetic, transcriptional, and post-transcriptional modifications. A consistent number of non-coding RNAs (ncRNAs) has been shown to be regulated by p53, the most important tumor suppressor of the cells frequently mutated in human cancer. It has been shown that some mutant p53 proteins are associated with the loss of tumor suppressor activity and the acquisition of new oncogenic functions named gain-of-function activities. In this review, we highlight recent lines of evidence suggesting that mutant p53 is involved in the expression of specific ncRNAs to gain oncogenic functions through the creation of a complex network of pathways that influence each other.Biomolecules 2020, 10, 472 2 of 15 heterotetrameric mut-p53/wt-p53 complex can inhibit the function of the remaining wt-p53 in tumor suppression [8,9]. Most of the missense mutations occur in the p53 DNA-binding region and can be classified as either contact mutations (as p53R248 and p53R273 interfere directly with DNA binding) or conformational mutations (as p53R175 induces local or global conformational distortions) [5,9].Six hotspot mutations are the most represented in the cancers. These include R175, G245, R248, R249, R273, and R282, which make up about 30% of all mutations in TP53 covering all human cancer types [8][9][10]. However, due to cancer genome sequencing tools, many other different TP53 mutations have been discovered. mut-p53 GOF has been demonstrated by numerous cell-based experiments such as by ectopic expression of mut-p53 proteins in p53-null human tumor cells or knockdown of endogenous mut-p53 in cells containing only one allele of mutant p53, as well as in mutant p53 knock-in mouse models [5,[8][9][10]. Genome sequencencing has highlighted that more than 91% of TP53-mutant cancers exhibit loss of the second allele (LOH) by mutation or DNA deletion [11].Many mut-p53 GOF activities have been identified as tumor cell proliferation, survival, migration and invasion, enhancing chemoresistance, disrupting proper tissue architecture, inducing cancer metabolism (Warburg effect and lipid metabolism), and increasing genomic instability and mitochondrial dysfunction [5,[10][11][12][13][14][15] (Figure 1).

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Noncoding RNA: Current Deep Sequencing Data Analysis Approaches and Challenges

Cited by 75 publications

References 192 publications

Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types

Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types

The presence or absence alone of miRNA isoforms (isomiRs) successfully discriminate amongst the 32 TCGA cancer types

The Impact of Mutant p53 in the Non-Coding RNA World

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