Abstract
Disulfidptosis is a novel form of programmed cell death discovered by Liu et al. It's initiated in cells highly expressing SLC7A11, especially in cancers. Our principal aim is to establish and validate a prognostic prediction model for osteosarcoma patients, potentially providing a fresh perspective on the characteristics of disulfidptosis in osteosarcoma and its treatment. Osteosarcoma cohorts obtained from the TARGET and GEO databases were classified into disulfidptosis-high/low-related groups to analyze the Differentially Expressed Genes (DEGs) using the ssGSEA method. DEGs were subsequently analyzed by the Weighted Gene Co-expression Network Analysis (WGCNA) method. Various machine learning algorithms, including the log-rank test, univariate Cox analysis, and LASSO algorithm, were employed, yielding 5 Disulfidptosis-Related Genes (DRGs). GSVA and ssGSEA, were also conducted to investigate the underlying mechanisms of disulfidptosis in osteosarcoma. We established a reliable disulfidptosis-related classification, aand our subsequent analysis has suggested intriguing disparities in the expression of the pentose phosphate pathway (PPP) and cytoskeleton regulation among the groups, indicating that the high-related group was more susceptible to disulfidptosis. 5 disulfidptosis-related genes were selected from the differentially expressed genes (DEGs) , and samples in the cohorts were divided into high-/low-risk groups based on the risk score. Functional analysis demonstrated significantly higher expression of the regulation of the cytoskeleton pathway in the high-risk group. Additionally, immune cell-associated pathways such as the T cell receptor signaling pathway and NOD/TOLL-like receptor signaling pathway showed significant decreases in the high-risk group. We then analyzed the infiltration of immune cells in the tumor microenvironment, revealing lower infiltration of almost every immune cell in the high-risk group. To gain insights into the clinical treatment of osteosarcoma patients, we also analyzed the differences in drug sensitivity between the risk groups, identifying 8 drugs that were more sensitive in the high-risk group.