MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma

Lietz, Christopher B.; Garbutt, Cassandra; Barry, William T.; Chen, Yen‐Lin; Lozano-Calderón, Santiago; Wang, Yao‐Yu; Lawney, Brian; Ebb, David H.; Coté, Gregory M.; Duan, Zhenfeng; Hornicek, Francis J.; Choy, Edwin; Nielsen, G. Petur; Haibe‐Kains, Benjamin; Quackenbush, John; Spentzos, Dimitrios

doi:10.1038/s41598-020-61236-3

Cited by 11 publications

(11 citation statements)

References 55 publications

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“…An assessment of the clinical utility of miRNA sets and their association with methylation status ( Hill et al, 2017 ) found that most prognostic miRNAs affecting gene expression via DNA methylation, cluster in 14q32 -a region which was also previously reported to encode more than 40 miRNAs including imprinted genes important in osteogenic differentiation and inhibiting cancer ( Thayanithy et al, 2012 ). This report suggests that miRNAs and modulation in methylation patterns may offer prognostic and therapeutic strategies in osteosarcoma treatment ( Lietz et al, 2020 ). The hypothesis that miRNA may regulate gene expression epigenetically was reinforced by the relationship of certain miRNAs and DNA methyltransferases, for example-miR-485-3p, MiR-370, miR-142, miR-7, miR-129-5p ( Ding et al, 2015 ; Du et al, 2018 ; Fabbri et al, 2007 ; Khraiwesh et al, 2010 ; Wu et al, 2010 ; Zhang and Peng, 2017 ; Zhang et al, 2017a ; Zhang et al, 2019 ) ( Fig.…”

Section: Short Non-coding Rnas– Micro Rnasmentioning

confidence: 91%

Navigating the genomic instability mine field of osteosarcoma to better understand implications of non-coding RNAs

Fatema¹,

Larson²,

Barrott³

2022

Biocell

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Osteosarcoma is one of the most genomically complex cancers and as result, it has been difficult to assign genomic aberrations that contribute to disease progression and patient outcome consistently across samples. One potential source for correlating osteosarcoma and genomic biomarkers is within the non-coding regions of RNA that are differentially expressed. However, it is unsurprising that a cancer classification that is fraught with genomic instability is likely to have numerous studies correlating non-coding RNA expression and function have been published on the subject. This review undertakes the formidable task of evaluating the published literature of noncoding RNAs in osteosarcoma. This is not the first review on this topic and will certainly not be the last. The review is organized with an introduction into osteosarcoma and the epigenetic control of gene expression before reviewing the molecular function and expression of long non-coding RNAs, circular RNAs, and short non-coding RNAs such as microRNAs, piwi RNAs, and short-interfering RNAs. The review concludes with a review of the literature and how the biology of non-coding RNAs can be used therapeutically to treat cancers, especially osteosarcoma. We conclude that non-coding RNA expression and function in osteosarcoma is equally complex to understanding the expression differences and function of coding RNA and proteins; however, with the added lens of both coding and non-coding genomic sequence, researchers can begin to identify the patterns that consistently associate with aggressive osteosarcoma.

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Section: Short Non-coding Rnas– Micro Rnasmentioning

confidence: 91%

Navigating the genomic instability mine field of osteosarcoma to better understand implications of non-coding RNAs

Fatema¹,

Larson²,

Barrott³

2022

Biocell

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“…Hierarchical clustering as well as a supervised analysis using the “signed average expression” survival prediction method were used by Lietz et al (2020) to test the validity of a set of prognostic signatures ( 5-miRNA and 22-miRNA profiles) in two osteosarcoma (ORPHA:668) cohorts. Furthermore, the authors observed that sets of experimentally validated gene (mRNA) targets of the prognostic miRNAs presented robust outcome predictive function, suggesting a possibly active miRNA/mRNA network.…”

Section: Epigenetics and Ai In Rds: Existing Literaturementioning

confidence: 99%

“…Furthermore, the authors observed that sets of experimentally validated gene (mRNA) targets of the prognostic miRNAs presented robust outcome predictive function, suggesting a possibly active miRNA/mRNA network. A composite model integrating information from pathologic necrosis combined with miRNA biomarkers was proposed, allowing improved and refined stratification into three relevant prognostic groups (very favorable, very unfavorable, and intermediate) ( Lietz et al, 2020 ).…”

Section: Epigenetics and Ai In Rds: Existing Literaturementioning

confidence: 99%

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

Brasil¹,

Neves²,

Rijoff³

et al. 2021

Front. Mol. Biosci.

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More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.

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“…miRNAs are frequently associated with CGIs and are themselves also found silenced through epigenetic mechanisms [ 21 ]. Dysregulated miRNA expression may result in aberrant expression of genes that play critical roles in osteosarcoma tumorigenesis and progression [ 22 – 25 ]. We have previously identified miR-486-5p to be among the most downregulated miRNAs in osteosarcoma cell lines [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

miR-486-5p expression is regulated by DNA methylation in osteosarcoma

et al. 2022

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Background Osteosarcoma is the most common primary malignant tumour of bone occurring in children and young adolescents and is characterised by complex genetic and epigenetic changes. The miRNA miR-486-5p has been shown to be downregulated in osteosarcoma and in cancer in general. Results To investigate if the mir-486 locus is epigenetically regulated, we integrated DNA methylation and miR-486-5p expression data using cohorts of osteosarcoma cell lines and patient samples. A CpG island in the promoter of the ANK1 host gene of mir-486 was shown to be highly methylated in osteosarcoma cell lines as determined by methylation-specific PCR and direct bisulfite sequencing. High methylation levels were seen for osteosarcoma patient samples, xenografts and cell lines based on quantitative methylation-specific PCR. 5-Aza-2′-deoxycytidine treatment of osteosarcoma cell lines caused induction of miR-486-5p and ANK1, indicating common epigenetic regulation in osteosarcoma cell lines. When overexpressed, miR-486-5p affected cell morphology. Conclusions miR-486-5p represents a highly cancer relevant, epigenetically regulated miRNA in osteosarcoma, and this knowledge contributes to the understanding of osteosarcoma biology.

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MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma

Cited by 11 publications

References 55 publications

Navigating the genomic instability mine field of osteosarcoma to better understand implications of non-coding RNAs

Navigating the genomic instability mine field of osteosarcoma to better understand implications of non-coding RNAs

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

miR-486-5p expression is regulated by DNA methylation in osteosarcoma

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