Biomarker significance of plasma and tumor miR-21, miR-221, and miR-106a in osteosarcoma
Bibliographic record
Abstract
// Manjula Nakka 1, 2 , Wendy Allen-Rhoades 1, 2, 4 , Yiting Li 1, 2 , Aaron J. Kelly 2, 3 , Jianhe Shen 1, 2 , Aaron M. Taylor 2, 3 , Donald A. Barkauskas 5, 6 , Jason T. Yustein 1, 2, 4 , Irene L. Andrulis 7, 8 , Jay S. Wunder 9 , Richard Gorlick 6 , Paul S. Meltzer 10 , Ching C. Lau 1, 2, 3, 4 and Tsz-Kwong Man 1, 2, 3, 4 , the TARGET osteosarcoma consortium * 1 Texas Children’s Cancer and Hematology Centers, Texas Children’s Hospital, Houston, TX, USA 2 Department of Pediatrics, and Baylor College of Medicine, Houston, TX, USA 3 Program of Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX, USA 4 Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA 5 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA 6 Children’s Oncology Group, Monrovia, CA, USA 7 Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada 8 Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada 9 Department of Surgery, University of Toronto, Toronto, ON, Canada 10 Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA * Ching Lau, Paul Meltzer, Sean David, Josh Waterfall, Sven Bilke, Malcolm Smith, Daniela Gerhard, Jaime Guidary Auvil, Tanja Davidsen, Leandro Hermida, Patee Gesuwan, Richard Gorlick, Don Barkauskas, Mark Krailo, Chand Khanna, Neyssa Marina, Lisa Teot, Julie Gastier-Foster, Nicole Ross, Yvonne Moyer, Laura Monovich, Mary McNulty, Irene Andrulis, Nalan Gokgoz, Shintaro Iwata, Miki Ohira, Silvia Caminada De Toledo, Sergio Petrilli, Jiayi Sun, Aaron Taylor, Jianhe Shen and Tsz-Kwong Man Correspondence to: Tsz-Kwong Man, email: ctman@txch.org Keywords: miRNA, osteosarcoma, biomarker, plasma, prognosis Received: December 09, 2016 Accepted: May 15, 2017 Published: May 27, 2017 ABSTRACT Osteosarcoma is the most common malignant bone tumor in children and young adults. Despite the use of surgery and multi-agent chemotherapy, osteosarcoma patients who have a poor response to chemotherapy or develop relapses have a dismal outcome. Identification of biomarkers for active disease may help to monitor tumor burden, detect early relapses, and predict prognosis in these patients. In this study, we examined whether circulating miRNAs can be used as biomarkers in osteosarcoma patients. We performed genome-wide miRNA profiling on a discovery cohort of osteosarcoma and control plasma samples. A total of 56 miRNAs were upregulated and 164 miRNAs were downregulated in osteosarcoma samples when compared to control plasma samples. miR-21, miR-221 and miR-106a were selected for further validation based on their known biological importance. We showed that all three circulating miRNAs were expressed significantly higher in osteosarcoma samples than normal samples in an independent cohort obtained from the Children’s Oncology Group. Furthermore, we demonstrated that miR-21 was expressed significantly higher in osteosarcoma tumors compared with normal bone controls. More importantly, lower expressions of miR-21 and miR-221, but not miR-106a, significantly correlated with a poor outcome. In conclusion, our results indicate that miR-21, miR-221 and miR-106a were elevated in the circulation of osteosarcoma patients, whereas tumor expressions of miR-21 and miR-221 are prognostically significant. Further investigation of these miRNAs may lead to a better prognostic method and potential miRNA therapeutics for osteosarcoma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".