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26 February 2020 : Clinical Research  

Identification and Validation of a Potent Multi-miRNA Signature for Prediction of Prognosis of Osteosarcoma Patients

Xinle Luo1AB, Jiuyang Tang1ABF*, Huabing Xuan1BE, Jianlin Liu1BEF, Xi Li1AE

DOI: 10.12659/MSM.919272

Med Sci Monit 2020; 26:e919272

Abstract

BACKGROUND: Osteosarcoma, the most common solid malignancy, has high incidence and mortality rates. We constructed a miRNA-based signature that can be used to assess the prognosis of osteosarcoma patients.

MATERIAL AND METHODS: The miRNA profile was derived from the Gene Expression Omnibus (GEO) website, with matched clinical records. The miRNA-based overall survival (OS)-predicting signature was established by LASSO Cox regression analysis. Receiver operating characteristic (ROC) curve and Kaplan-Meier (K-M) analyses were performed to examine the stability and discriminatory ability of the OS-predicting signatures. Pathway enrichment analyses were performed to uncover potential mechanisms.

RESULTS: Three miRNAs (miR-153, miR-212, and miR-591) independently related to the OS were extracted to build a risk score formula. The ROC curve and K-M analyses revealed good discrimination ability of the OS signature for osteosarcoma patients in both the training cohort (P=0.00015, AUC=0.962) and the validation cohort (P=0.0065, AUC=0.793). As shown in multivariate analysis, the classifier showed favorable predictive accuracy similar to the recurrence status to be an independent risk factor for osteosarcoma. Furthermore, the nomogram showed a synergistic effect by combining the clinicopathological features with our classifier. Also, the enrichment analyses of the target genes may contribute to improved treatment of osteosarcoma.

CONCLUSIONS: The 3-miRNA-based classifier serves as an effective prognosis-predicting signature for osteosarcoma patients.

Keywords: Osteosarcoma, Adolescent, Biomarkers, Tumor, Cohort Studies, Gene Expression Profiling, nomograms, ROC Curve

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750