Liu Binfeng, He Shasha, Li Chenbei, Xiong Zijian, Li Zhaoqi, Feng Chengyao, Wang Hua, Tu Chao, Li Zhihong
Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.
Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
Discov Oncol. 2025 May 25;16(1):917. doi: 10.1007/s12672-025-02587-z.
Soft tissue sarcomas (STS) imposes a substantial healthcare burden on society. The progression of these tumors is significantly influenced by diverse modes of programmed cell death (PCD), which can serve as valuable indicators for assessing prognosis and immune therapeutic response in STS. Nonetheless, the precise role of multiple cell death patterns in STS is yet to be clarified. We employed 96 machine-learning algorithm combination frameworks to identify novel cell death-related signatures (CDSigs) with the highest mean c-index, indicating their excellence. The independence test and comparison with previously published models further confirmed the stability and quality of these signatures for survival prediction in STS. The nomogram, comprising the cell death score (CDS) and clinical features, exhibited excellent predictive performance. Additionally, the CDSigs revealed associations with immune checkpoint genes and the immune microenvironment in STS. Furthermore, the results demonstrated that patients with lower CDS had the potential for greater benefit from immune therapeutic responses compared to those with higher CDS. Moreover, STS patients with low-risk scores exhibited heightened sensitivity to doxorubicin, axitinib, cisplatin, and camptothecin. Finally, the RT-qPCR results underscored significant differences in expression levels of several CDSigs genes between STS and normal cells. Overall, we comprehensively analyzed the multiple PCD in STS and established a novel CDSig for STS patients. This novel CDSig holds great promise in deciphering the prognosis, immune, and immune therapeutic response of STS.
软组织肉瘤（STS）给社会带来了沉重的医疗负担。这些肿瘤的进展受到多种程序性细胞死亡（PCD）模式的显著影响，这些模式可作为评估STS预后和免疫治疗反应的有价值指标。然而，多种细胞死亡模式在STS中的确切作用尚待阐明。我们采用了96种机器学习算法组合框架来识别具有最高平均c指数的新型细胞死亡相关特征（CDSigs），表明它们的卓越性。独立性测试以及与先前发表模型的比较进一步证实了这些特征在STS生存预测中的稳定性和质量。包含细胞死亡评分（CDS）和临床特征的列线图表现出优异的预测性能。此外，CDSigs揭示了与STS中免疫检查点基因和免疫微环境的关联。此外，结果表明，与CDS较高的患者相比，CDS较低的患者可能从免疫治疗反应中获益更大。此外，低风险评分的STS患者对阿霉素、阿西替尼、顺铂和喜树碱表现出更高的敏感性。最后，RT-qPCR结果强调了STS与正常细胞之间几种CDSigs基因表达水平的显著差异。总体而言，我们全面分析了STS中的多种PCD，并为STS患者建立了一种新型CDSig。这种新型CDSig在解读STS的预后、免疫和免疫治疗反应方面具有很大的前景。
