Identification of LTF as a Prognostic Biomarker for Osteosarcoma

Abstract

Osteosarcoma remains a major health problem in teenagers. However, its pathogenesis mechanism remains not fully elucidated. This study aims to identify the prognostic biomarkers for osteosarcoma. In this study, we selected genes with a median absolute deviation (MAD) value of the top 5000 in the GSE32981 dataset for subsequent analysis. Weighted correlation network analysis (WGCNA) was used to construct a coexpression network. WGCNA showed that the tan module and midnight blue module were highly correlated with origin and metastases of osteosarcoma, respectively. Enrichment analysis was conducted using genes in the tan module and midnight blue module. A gene coexpression network was constructed by calculating the Spearman correlation coefficients. Four key genes (LTF, C10orf107, HIST1H2AK, and NEXN) were identified to be correlated with the prognosis of osteosarcoma patients. LTF has the highest AUC value, and its effect on osteosarcoma cells was then evaluated. The effect of LTF overexpression on proliferation, migration, and invasion of MG63 and 143B cells was detected by the CCK-8 assay, transwell cell migration assay, and transwell invasion assay, respectively. The overexpression of LTF promoted the proliferation, migration, and invasion of MG63 and 143B cells. In conclusion, LTF may serve as a prognostic biomarker for osteosarcoma.