MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma

MSdb: An integrated expression atlas of human musculoskeletal system

The global prevalence and burden of musculoskeletal (MSK) disorders are immense. Advancements in next-generation sequencing (NGS) have generated vast amounts of data, accelerating the research of pathological mechanisms and the development of therapeutic approaches for MSK disorders. However, scattered datasets across various repositories complicate uniform analysis and comparison. Here, we introduce MSdb, a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis. Overall, systematic categorizing, standardized processing, and freely accessible knowledge features MSdb a valuable resource for MSK research community.

Regulation of the Epithelial to Mesenchymal Transition in Osteosarcoma

The epithelial to mesenchymal transition (EMT) is a cellular process that has been linked to the promotion of aggressive cellular features in many cancer types. It is characterized by the loss of the epithelial cell phenotype and a shift to a more mesenchymal phenotype and is accompanied by an associated change in cell markers. EMT is highly complex and regulated via multiple signaling pathways. While the importance of EMT is classically described for carcinomas-cancers of epithelial origin-it has also been clearly demonstrated in non-epithelial cancers, including osteosarcoma (OS), a primary bone cancer predominantly affecting children and young adults. Recent studies examining EMT in OS have highlighted regulatory roles for multiple proteins, non-coding nucleic acids, and components of the tumor micro-environment. This review serves to summarize these experimental findings, identify key families of regulatory molecules, and identify potential therapeutic targets specific to the EMT process in OS.

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

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.

Pediatric Sarcomas: The Next Generation of Molecular Studies

Simple Summary

There has been an incredible amount of discovery in pediatric sarcomas, but much remains to be accomplished. Clinical challenges include diagnostic heterogeneity and the poor outcome of patients with high risk, metastatic, and relapsed disease. The emergence of single cell sequencing has allowed the ability to document tumor cell heterogeneity in amazing detail, but it does not allow the ability to visualize spatial orientation. This problem has been solved by spatial multi-omics, which can be used to map tumors and visualize the distribution of critical transcripts, mutations, and proteins. However, these tools only offer observational data. High-throughput functional genomics provides a powerful way to highlight oncogenic drivers and potential therapy opportunities. Research has been hamstrung by a need for annotated specimens, particularly in post-therapy, relapsed, and metastatic disease, and initial biopsies offer only limited data opportunities. Data complexity, variability, and inconsistency present problems best approached with AI/machine learning. We stand on the threshold of a revolution in cancer cell biology that has the potential for translation into more effective and more directed therapies, particularly for previously recalcitrant diseases.

Abstract

Pediatric sarcomas constitute one of the largest groups of childhood cancers, following hematopoietic, neural, and renal lesions. Partly because of their diversity, they continue to offer challenges in diagnosis and treatment. In spite of the diagnostic, nosologic, and therapeutic gains made with genetic technology, newer means for investigation are needed. This article reviews emerging technology being used to study human neoplasia and how these methods might be applicable to pediatric sarcomas. Methods reviewed include single cell RNA sequencing (scRNAseq), spatial multi-omics, high-throughput functional genomics, and clustered regularly interspersed short palindromic sequence-Cas9 (CRISPR-Cas9) technology. In spite of these advances, the field continues to be challenged by a dearth of properly annotated materials, particularly from recurrences and metastases and pre- and post-treatment samples.

Genome-wide DNA methylation patterns reveal clinically relevant predictive and prognostic subtypes in human osteosarcoma

Aberrant methylation of genomic DNA has been reported in many cancers. Specific DNA methylation patterns have been shown to provide clinically useful prognostic information and define molecular disease subtypes with different response to therapy and long-term outcome. Osteosarcoma is an aggressive malignancy for which approximately half of tumors recur following standard combined surgical resection and chemotherapy. No accepted prognostic factor save tumor necrosis in response to adjuvant therapy currently exists, and traditional genomic studies have thus far failed to identify meaningful clinical associations. We studied the genome-wide methylation state of primary tumors and tested how they predict patient outcomes. We discovered relative genomic hypomethylation to be strongly predictive of response to standard chemotherapy. Recurrence and survival were also associated with genomic methylation, but through more site-specific patterns. Furthermore, the methylation patterns were reproducible in three small independent clinical datasets. Downstream transcriptional, in vitro, and pharmacogenomic analysis provides insight into the clinical translation of the methylation patterns. Our findings suggest the assessment of genomic methylation may represent a strategy for stratifying patients for the application of alternative therapies.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08346-6.

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

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.