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Abstract：
UNASSIGNED: Osteosarcoma 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.
UNASSIGNED: We 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.
UNASSIGNED: In 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.
UNASSIGNED: The 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.
UNASSIGNED: We 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.
UNASSIGNED: In 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.
UNASSIGNED: The 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.
摘要：
■骨肉瘤主要影响儿童和青少年，目前的临床治疗往往导致预后不良。越来越多的证据表明程序性细胞死亡（PCD）与肿瘤的发生和发展有关。本研究旨在通过识别PCD相关的预后风险基因来提高OS预后评估的准确性。构建基于PCD的OS预后风险模型，并表征该模型中基因的功能。
■我们从TARGET和GEO数据库检索了骨肉瘤患者样本，并手动整理文献，总结15种形式的程序性细胞死亡。我们从文献来源以及KEGG和GSEA等数据库中整理了1621个PCD基因。为了构建我们的模型，我们集成了包括Enet在内的十种机器学习方法，里奇,RSF,CoxBoost,plsRcox,survivvalSVM，拉索,SuperPC,StepCox,GBM。根据平均C指数选择最优模型，并命名为骨肉瘤程序性细胞死亡评分(OS-PCDS)。为了验证我们的模型在不同数据集上的预测性能，我们采用了三个独立的GEO验证集。此外，我们评估了模型中包含的基因的mRNA和蛋白质表达水平，并调查了它们对扩散的影响，迁移,和骨肉瘤细胞凋亡的基因敲除实验。
■在我们广泛的分析中，我们在骨肉瘤(OS)中鉴定出30个与程序性细胞死亡(PCD)相关的预后风险基因.为了评估这些基因的预测能力，我们计算了各种组合的C指数。采用随机生存森林(RSF)算法的模型表现出优越的预测性能，显著优于传统方法。该最佳模型包括5个关键基因:MTM1、MLH1、CLTCL1、EDIL3和SQLE。为了验证这些基因的相关性，我们分析了它们的mRNA和蛋白质表达水平，揭示了骨肉瘤细胞和正常组织细胞之间的显着差异。具体来说,这些基因的表达水平在OS细胞中显著改变，表明它们在肿瘤进展中的关键作用。在U2OS细胞中通过基因敲低实验进行进一步的功能验证。敲除这些基因中的三个-CLTCL1，EDIL3和SQLE-导致增殖率发生实质性变化，迁移能力，骨肉瘤细胞凋亡率。这些发现强调了这些基因在骨肉瘤病理生理学中的关键作用，并强调了它们作为治疗靶标的潜力。
■发现构成OS-PCDS模型CLTCL1、MTM1、MLH1、EDIL3和SQLE的5个基因显著影响增殖，迁移,骨肉瘤细胞的凋亡，强调它们作为关键预后标志物和治疗靶点的潜力。OS-PCDS可以准确评估骨肉瘤患者的预后。
■我们从TARGET和GEO数据库检索了骨肉瘤患者样本，并手动整理文献，总结15种形式的程序性细胞死亡。我们从文献来源以及KEGG和GSEA等数据库中整理了1621个PCD基因。为了构建我们的模型，我们集成了包括Enet在内的十种机器学习方法，里奇,RSF,CoxBoost,plsRcox,survivvalSVM，拉索,SuperPC,StepCox,GBM。根据平均C指数选择最优模型，并命名为骨肉瘤程序性细胞死亡评分(OS-PCDS)。为了验证我们的模型在不同数据集上的预测性能，我们采用了三个独立的GEO验证集。此外，我们评估了模型中包含的基因的mRNA和蛋白质表达水平，并调查了它们对扩散的影响，迁移,和骨肉瘤细胞凋亡的基因敲除实验。
■在我们广泛的分析中，我们在骨肉瘤(OS)中鉴定出30个与程序性细胞死亡(PCD)相关的预后风险基因.为了评估这些基因的预测能力，我们计算了各种组合的C指数。采用随机生存森林(RSF)算法的模型表现出优越的预测性能，显著优于传统方法。该最佳模型包括5个关键基因:MTM1、MLH1、CLTCL1、EDIL3和SQLE。为了验证这些基因的相关性，我们分析了它们的mRNA和蛋白质表达水平，揭示了骨肉瘤细胞和正常组织细胞之间的显着差异。具体来说,这些基因的表达水平在OS细胞中显著改变，表明它们在肿瘤进展中的关键作用。在U2OS细胞中通过基因敲低实验进行进一步的功能验证。敲除这些基因中的三个-CLTCL1，EDIL3和SQLE-导致增殖率发生实质性变化，迁移能力，骨肉瘤细胞凋亡率。这些发现强调了这些基因在骨肉瘤病理生理学中的关键作用，并强调了它们作为治疗靶标的潜力。
■发现构成OS-PCDS模型CLTCL1、MTM1、MLH1、EDIL3和SQLE的5个基因显著影响增殖，迁移,骨肉瘤细胞的凋亡，强调它们作为关键预后标志物和治疗靶点的潜力。OS-PCDS可以准确评估骨肉瘤患者的预后。
