Analysis of metastasis‑related risk factors and clinical relevance in adult soft‑tissue sarcoma.

Abstract

Metastasis occurs in nearly 50% of cases of adult soft-tissue sarcoma (ASTS), leading to a dismal prognosis, with a 2-year survival rate of ~30%. Consequently, a prognostic model that incorporates metastatic characteristics may be instrumental in predicting survival time and in crafting optimal personalized therapeutic strategies for patients with ASTS. In the present study, a prognostic prediction model for ASTS was developed by examining genes that are differentially expressed between non-metastatic and metastatic patients in the Gene Expression Omnibus dataset. The prognostic model, which includes five featured genes [actin γ2 (ACTG2), apolipoprotein D, coatomer protein complex subunit γ2 imprinted transcript 1, collagen type VI α6 chain and osteomodulin], was further validated in patients with ASTS from the Cancer Genome Atlas dataset. Based on these five-gene signatures, patients were categorized into high- and low-risk groups. Functional and pathway analyses revealed disparities in stemness, extracellular matrix and cell adhesion-related pathways between the two risk groups, particularly noting the activation of the PI3K-Akt pathway in high-risk cases. Analysis of immune infiltration also revealed variations in immune microenvironment changes between the two risk groups. Immunohistochemical staining substantiated the prognostic significance of these gene signatures in a specific sarcoma subtype. Additionally, wound-healing and Transwell assays demonstrated that inhibition of ACTG2 by shRNAs curbed cell migration and invasion in a sarcoma HOS cell line, underscoring its role in sarcoma metastasis. In conclusion, the present study successfully developed and validated a metastasis-based prognosis prediction model. This model not only reliably forecasts the survival of patients with ASTS, but also may pave the way for further investigation into the processes underlying sarcoma metastasis, ultimately aiding in the design of tailored therapeutic regimens.