Identification of EMT-related subtype and a 9 genes signature predicts the prognosis in osteosarcoma.

Objective: Osteosarcoma, mainly arising from mesenchymal cells, is the most common bone tumor in children and adolescents, with high malignancy and a tendency for metastasis and recurrence. Epithelial cells undergoing epithelial-mesenchymal transition (EMT) often signal the start of tumor metastasis, as they gain mesenchymal characteristics that enhance their migration and invasion capabilities.

Methods: Osteosarcoma patient gene expression and clinical data were retrieved from the TARGET database. EMT-related molecular subtypes were identified through consensus clustering. Immune microenvironment assessment was performed using the ESTIMATE algorithm. Using WGCNA, a co-expression network was developed to find modules linked to subtypes. Univariate Cox regression analysis identified prognosis-related genes. The development of a 9-gene prognostic risk model involved Lasso-Cox regression, and its accuracy was verified.

Results: Two molecular subtypes (C1 and C2) with distinct clinical outcomes were identified. The C1 group showed significantly higher immune and ESTIMATE scores compared to C2. Through WGCNA, the PINK module was identified as significantly associated with the subtypes. Cox regression analysis revealed 19 prognosis-related genes. A 9-gene risk model (EPHB3, GADD45GIP1, RAD23A, NGDN, SYCE2, SCD, AP1M1, POLR3D, FADS2) was constructed, demonstrating high predictive accuracy. Multivariate Cox analysis indicated GADD45GIP1, NGDN, AP1M1, and POLR3D as independent prognostic factors for osteosarcoma.

Conclusion: Two EMT-related subtypes with distinct immune features were identified, aiding clinical decision-making. A model comprising 9 genes offers a dependable means of predicting osteosarcoma prognosis.