ArticleJournal of hematology & oncology2017
An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets.
Article in Journal of hematology & oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- A dynamic microRNA profile that tracks a chemotherapy resistance phenotype in osteosarcoma. Implications for novel therapeutics.medRxiv : the preprint server for health sciences · 2024Article
- Unveiling research trends in the prognosis of osteosarcoma: A bibliometric analysis from 2000 to 2022.Heliyon · 2024Article
- Exosome‑delivered miR‑486‑3p inhibits the progression of osteosarcoma via sponging CircKEAP1/MARCH1 axis components.Oncology letters · 2024Article
- Heterogeneity in the gene regulatory landscape of leiomyosarcoma.NAR cancer · 2023Article
- YBX1-interacting small RNAs andJournal of bone oncology · 2023Article
- Navigating the genomic instability mine field of osteosarcoma to better understand implications of non-coding RNAs.Biocell : official journal of the Sociedades Latinoamericanas de Microscopia Electronica ... et. al · 2022Article
- Genome-wide DNA methylation patterns reveal clinically relevant predictive and prognostic subtypes in human osteosarcoma.Communications biology · 2022Article
- Integrated Analysis of Multi-Omics Data to Establish a Hypoxia-Related Prognostic Model in Osteosarcoma.Evolutionary bioinformatics online · 2022Article
- ClustMMRA v2: A Scalable Computational Pipeline for the Identification of MicroRNA Clusters Acting Cooperatively on Tumor Molecular Subgroups.Advances in experimental medicine and biology · 2022Article
- Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme.Cancer research · 2021Article
- Maternally expressed 3 inhibits the biological activity of oral squamous cell carcinoma SCC25 and CAL27 cell lines.Oncology letters · 2021Article
- Article
- PUMA: PANDA Using MicroRNA Associations.Bioinformatics (Oxford, England) · 2020Article
- Four genes predict the survival of osteosarcoma patients based on TARGET database.Journal of bioenergetics and biomembranes · 2020Article
- Epigenetic Regulation of Skeletal Tissue Integrity and Osteoporosis Development.International journal of molecular sciences · 2020Review
- MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma.Scientific reports · 2020Article
- DNA methylation of noncoding RNAs: new insights into osteogenesis and common bone diseases.Stem cell research & therapy · 2020Review
- Clinical and Molecular Analysis of Pathologic Fracture-associated Osteosarcoma: MicroRNA profile Is Different and Correlates with Prognosis.Clinical orthopaedics and related research · 2019Article
- Identification of microRNA clusters cooperatively acting on epithelial to mesenchymal transition in triple negative breast cancer.Nucleic acids research · 2019Article
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Authors and funding
19 authors at 9 institutions in 2 countries.
Funding
Abstract
backgroundA microRNA (miRNA) collection on the imprinted 14q32 MEG3 region has been associated with outcome in osteosarcoma. We assessed the clinical utility of this miRNA set and their association with methylation status.
methodsWe integrated coding and non-coding RNA data from three independent annotated clinical osteosarcoma cohorts (n = 65, n = 27, and n = 25) and miRNA and methylation data from one in vitro (19 cell lines) and one clinical (NCI Therapeutically Applicable Research to Generate Effective Treatments (TARGET) osteosarcoma dataset, n = 80) dataset. We used time-dependent receiver operating characteristic (tdROC) analysis to evaluate the clinical value of candidate miRNA profiles and machine learning approaches to compare the coding and non-coding transcriptional programs of high- and low-risk osteosarcoma tumors and high- versus low-aggressiveness cell lines. In the cell line and TARGET datasets, we also studied the methylation patterns of the MEG3 imprinting control region on 14q32 and their association with miRNA expression and tumor aggressiveness.
resultsIn the tdROC analysis, miRNA sets on 14q32 showed strong discriminatory power for recurrence and survival in the three clinical datasets. High- or low-risk tumor classification was robust to using different microRNA sets or classification methods. Machine learning approaches showed that genome-wide miRNA profiles and miRNA regulatory networks were quite different between the two outcome groups and mRNA profiles categorized the samples in a manner concordant with the miRNAs, suggesting potential molecular subtypes. Further, miRNA expression patterns were reproducible in comparing high-aggressiveness versus low-aggressiveness cell lines. Methylation patterns in the MEG3 differentially methylated region (DMR) also distinguished high-aggressiveness from low-aggressiveness cell lines and were associated with expression of several 14q32 miRNAs in both the cell lines and the large TARGET clinical dataset. Within the limits of available CpG array coverage, we observed a potential methylation-sensitive regulation of the non-coding RNA cluster by CTCF, a known enhancer-blocking factor.
conclusionsLoss of imprinting/methylation changes in the 14q32 non-coding region defines reproducible previously unrecognized osteosarcoma subtypes with distinct transcriptional programs and biologic and clinical behavior. Future studies will define the precise relationship between 14q32 imprinting, non-coding RNA expression, genomic enhancer binding, and tumor aggressiveness, with possible therapeutic implications for both early- and advanced-stage patients.
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