Dual Effects of Non-Coding RNAs (ncRNAs) in Cancer Stem Cell Biology

Kyriazi, Athina A; Papiris, Efstathios; Kalyvianakis, Konstantinos Kitsos; Sakellaris, George; Baritaki, Stavroula

doi:10.3390/ijms21186658

“…CSCs in such a niche are able to maintain their stemness state [30]. On one hand, the niche with hypoxia and high vascular intensity can directly maintain CSCs plasticity and survival [31]. On the other hand, the maintenance of CSCs can be reinforced by the EMT process under such a favorable microenvironment.…”

Section: The Relationship Between Emt and Cscsmentioning

confidence: 99%

MiRNA-mediated EMT and CSCs in cancer chemoresistance

Dong

¹

,

Li

²

,

Zhu

³

et al. 2021

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Cancer stem cells (CSCs) are a small group of cancer cells, which contribute to tumorigenesis and cancer progression. Cancer cells undergoing epithelial-to-mesenchymal transition (EMT) acquire the chemoresistant ability, which is regarded as an important feature of CSCs. Thus, there emerges an opinion that the generation of CSCs is considered to be driven by EMT. In this complex process, microRNAs (miRNAs) are found to play a key role. In order to overcome the drug resistance, inhibiting EMT as well as CSCs phenotype seem feasible. Thereinto, regulating the EMT- or CSCs-associated miRNAs is a crucial approach. Herein, we conduct this review to elaborate on the complicated interplay between EMT and CSCs in cancer chemoresistance, which is modulated by miRNAs. In addition, we elucidate the therapeutic strategy to overcome drug resistance through targeting EMT and CSCs.

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“…Moreover, to test the accuracy of the workflow across diverse sets of data, we moved to a public dataset. Recent findings have indicated that the role of piRNAs may not be only limited to germ cells, but may be extended to the regulation of cancer, promoting a stem-like state of tumour cells 65 . Therefore, we selected a dataset (GSE68246) to compare the piRNA profiles of breast spheroid-enriched CSCs against parental MCF7 cells and also generated in this case files, statistics and plots with WIND that are all available on the GitHub repository.…”

Section: Resultsmentioning

confidence: 99%

WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data

Geles

¹

,

Palumbo

²

,

Sellitto

³

et al. 2021

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Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

show abstract

“…Moreover, to test the accuracy of the workflow across diverse sets of data, we moved to a public dataset. Recent findings have indicated that the role of piRNAs may not be only limited to germ cells, but may be extended to the regulation of cancer, promoting a stem-like state of tumour cells 61 . Therefore, we selected a dataset (GSE68246) to compare the piRNA profiles of breast spheroid-enriched CSCs against parental MCF7 cells and also generated in this case files, statistics and plots with WIND that are all available on the GitHub repository.…”

Section: Resultsmentioning

confidence: 99%

WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data

Geles

¹

,

Palumbo

²

,

Sellitto

³

et al. 2021

F1000Res

2

0

View full text Add to dashboard Cite

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

show abstract

Dual Effects of Non-Coding RNAs (ncRNAs) in Cancer Stem Cell Biology

Cited by 21 publications

References 237 publications

MiRNA-mediated EMT and CSCs in cancer chemoresistance

MiRNA-mediated EMT and CSCs in cancer chemoresistance

WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data

WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data

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