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MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma.

ABSTRACT: There is a lack of well validated prognostic biomarkers in osteosarcoma, a rare, recalcitrant disease for which treatment standards have not changed in over 20 years. We performed microRNA sequencing in 74 frozen osteosarcoma biopsy samples, constituting the largest single center translationally analyzed osteosarcoma cohort to date, and we separately analyzed a multi-omic dataset from a large NCI supported national cooperative group cohort. We validated the prognostic value of candidate microRNA signatures and contextualized them in relevant transcriptomic and epigenomic networks. Our results reveal the existence of molecularly defined phenotypes associated with outcome independent of clinicopathologic features. Through machine learning based integrative pharmacogenomic analysis, the microRNA biomarkers identify novel therapeutics for stratified application in osteosarcoma. The previously unrecognized osteosarcoma subtypes with distinct clinical courses and response to therapy could be translatable for discerning patients appropriate for more intensified, less intensified, or alternate therapeutic regimens.

SUBMITTER: Lietz CE

PROVIDER: S-EPMC7064533 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma.

Lietz Christopher E CE Garbutt Cassandra C Barry William T WT Deshpande Vikram V Chen Yen-Lin YL Lozano-Calderon Santiago A SA Wang Yaoyu Y Lawney Brian B Ebb David D Cote Gregory M GM Duan Zhenfeng Z Hornicek Francis J FJ Choy Edwin E Petur Nielsen G G Haibe-Kains Benjamin B Quackenbush John J Spentzos Dimitrios D

Scientific reports 20200310 1

There is a lack of well validated prognostic biomarkers in osteosarcoma, a rare, recalcitrant disease for which treatment standards have not changed in over 20 years. We performed microRNA sequencing in 74 frozen osteosarcoma biopsy samples, constituting the largest single center translationally analyzed osteosarcoma cohort to date, and we separately analyzed a multi-omic dataset from a large NCI supported national cooperative group cohort. We validated the prognostic value of candidate microRNA ...[more]

PMID: 32157112

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Project description:Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA-mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA-mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA-mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV1) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA-mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.

Project description:BackgroundTo investigate the role of microRNA-376b-3p (miR-376b-3p) and regulator of G protein signaling 1 (RGS1) in the proliferation, metastasis, and apoptosis of osteosarcoma.MethodsDifferentially expressed genes (DEGs) between tumor and normal tissues from GSE14359 and GSE33382 in the cancer genome atlas (TCGA) dataset were analyzed with GEO2R online. Similarly, differentially expressed miRNAs from GSE70367 were also analyzed with GEO2R. The interaction between the differentially expressed miRNAs and the shared distal metastasis-related DEGs from the two datasets were analyzed using miRWalk and Cytoscape. RGS1 and miR-376-3p were chosen to verify the prediction. RGS1 stably expressing and silencing cells were established based on the MG63 and U2OS cell lines. The targeting of RGS1 with miR-376b-3p was confirmed with Starbase prediction and luciferase reporter assay. Cell proliferation, metastasis, and apoptosis were characterized in vitro and in xenograft mice.ResultsA total of 10 up-regulated and 8 down-regulated DEGs were characterized as shared metastasis-related DEGs for GSE14359 and GSE33382. Among these DEGs, RGS1 was targeted with miR-376b-3p, a predicted down-regulated miRNA in GSE70367. High expression of RGS1 predicted proliferation, invasion, metastases, and poor prognosis in osteosarcoma. Overexpression of RGS1 promoted proliferation, invasion, mobility, and stemness in MG63 and U2OS cells, while silencing of RGS1 had the opposite effect in both cell lines. High expression of RGS1 promoted tumor growth in xenograft nude mice. RGS1 was targeted with miR-376b-3p; the addition of miR-376b-3p down-regulated RGS1, and suppressed cell proliferation, invasion, and metastasis. Meanwhile, sponging of miR-376b-3p had the opposite effect. The suppressive effects of miR-376b-3p could be abolished with RGS1, as cell proliferation, stemness, metastasis, and invasion were all promoted with RGS1 co-transfection in both cell lines.ConclusionsOur study indicated that RGS1 is a tumor-promoting gene in osteosarcoma, which could be inhibited with miR-376b-3p.

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MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma.

Publications

MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma.

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