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

Xinle Luo, Jiuyang Tang, Huabing Xuan, Jianlin Liu, Xi Li

(Department of Trauma and Joint Orthopedics, The People’s Hospital of Longhua, Shenzhen, Guangdong, China (mainland))

Med Sci Monit 2020; 26:e919272

DOI: 10.12659/MSM.919272

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.

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