Abstract
Osteosarcoma is a very aggressive bone tumor mainly affecting teens and young adults. Disulfidptosis is a metabolic-related form of regulated cell death. However, the interconnection between disulfidptosis and osteosarcoma has not been explored. In the present study, disulfidptosis-related clusters were identified in osteosarcoma using the nonnegative matrix factorization clustering method. PABPC3 was identified as a hazardous gene in osteosarcoma using machine learning algorithms, CoxBoost, and Random Survival Forest. The prognostic value, pathway annotation, immune characteristics, and drug prediction of PABPC3 were systematically explored. MTT (i.e., 3-(4, 5-dimethyl thiazol-2-yl)-2,5-diphenytetrazolium bromide), EdU (ie. 5-ethyny-2'-deoxvuridine), and Transwell assays were used for in vitro validation of PABPC3. The disulfidptosis-related clusters could distinguish survival outcomes of osteosarcoma patients. PABPC3 could predict survival outcomes, immune activity, and drug response in osteosarcoma patients. Besides, PABPC3 was proven to facilitate the proliferation and migration of osteosarcoma. The present study is expected to establish the bridge between disulfidptosis and osteosarcoma. PABPC3 is expected to be further explored as a therapeutic target in osteosarcoma.
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