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Identification of a metastasis-associated prognostic gene signature in osteosarcoma through integrated bioinformatics
Qingquan Xia1, Xiangchao Meng1, Xuhua Wu1
1Department of Orthopedics, Minhang Hospital, Fudan University, Shanghai, 201100, China.
Abstract:
Osteosarcoma is a highly aggressive bone malignancy with a strong tendency for metastasis and poor clinical outcomes. The molecular mechanisms driving osteosarcoma progression and metastasis remain incompletely understood, highlighting the need to identify robust biomarkers and therapeutic targets. Weighted gene co-expression network analysis (WGCNA) was performed using osteosarcoma datasets from the Gene Expression Omnibus to identify metastasis-associated gene modules and hub genes. Expression, methylation, mutation, immune infiltration, and drug sensitivity analyses were conducted using multiple public databases, including TCGA, GSCA, UALCAN, and cBioPortal. A prognostic model was developed using the TARGET osteosarcoma cohort and validated in an independent GEO dataset. Functional roles of hub genes were investigated through loss- and gain-of-function experiments in sarcoma cell lines using RT-qPCR, Western blotting, proliferation, colony formation, wound-healing, and luciferase reporter assays. Four hub genes, AURKB, CDC20, KIF11, and TOP2A, were identified as strongly associated with metastasis. These genes were significantly upregulated in sarcoma tissues and cell lines and demonstrated excellent diagnostic performance. Promoter hypomethylation and frequent genomic alterations contributed to their aberrant expression. High expression of the hub genes was associated with poor overall survival, and a four-gene prognostic model showed strong predictive performance in both training and validation cohorts. Functional assays confirmed that these genes promote sarcoma cell proliferation and migration, while miRNA-mediated regulation and drug resistance associations further highlighted their biological relevance. This integrated computational and experimental study identifies AURKB, CDC20, KIF11, and TOP2A as key oncogenic drivers in osteosarcoma, with significant diagnostic, prognostic, and therapeutic implications.
