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BackgroundOsteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.MethodWe retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.ResultIn our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes—CLTCL1, EDIL3, and SQLE—resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.ConclusionThe five genes constituting the OS-PCDS model—CLTCL1, MTM1, MLH1, EDIL3, and SQLE—were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

The primary causes of satellite breakups are hypervelocity impact and explosion, the research on satellite breakup can be used not only to evaluate the influence of breakup event on the space environment, but also to trace whether the satellite has been deliberately attacked. It is of great significance in both civil and military aspects. The study of satellite breakup behaviors and model is reviewed to summarize the research progress and insufficiency in recent decades, including the satellite breakup experiment, measurement and characterization of fragments, distribution characteristics of breakup fragments, satellite breakup model, etc. The classical studies are introduced in detail, and the limitations of the current research are pointed out. According to the current research results, the contemporary challenges and future directions for satellite breakup study are presented. The research on satellite breakup is developing in two directions: the miniaturization of satellite size and the complexity of satellite component. The study on satellite breakup needs to be explored and deepened on improving the experimental launch speed, expanding the model application range and breakup revealing the results under combined effect of impact and explosion.