鉴定和验证骨肉瘤的新型 PANoptosis 相关基因签名作为预后模型

Xianglin Peng, Wanchun Wang
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引用次数: 0

摘要

研究目的构建并验证骨肉瘤预后模型,以及 PANoptosis 相关基因的治疗潜力:观察性研究。研究地点和时间研究地点和时间:中国湖南长沙中南大学湘雅二医院骨科,2021年8月至2024年1月:方法:利用GEO和TARGET数据库中的转录组数据构建并验证骨肉瘤预后模型。分析中使用了LASSO Cox回归法和Glmnet R软件包,以确定关键的PAN凋亡相关基因。使用Limma R软件包进行了差异基因表达分析,并使用Kaplan-Meier生存分析和随时间变化的ROC曲线对模型进行了验证:结果:该模型由五个关键的 PANoptosis 相关基因衍生而来,在训练和验证队列中对患者的生存率具有显著的预测能力。进一步的分析证实了该模型的有效性,并确定转移分期和风险评分是可靠的独立预后指标:结论:该预后模型为骨肉瘤预后提供了一种新工具,并强调了靶向PAN凋亡相关通路的治疗潜力:PAN凋亡相关基因 骨肉瘤 预后 生物信息学 肿瘤微环境
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Identification and Validation of a Novel PANoptosis-related Gene Signatured for Osteosarcoma as Prognostic Model.

Objective: To construct and validate a prognostic model for osteosarcoma prognostication and therapeutic potential of PANoptosis- related genes.

Study design: Observational study. Place and Duration of the Study: Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, from August 2021 to January 2024.

Methodology: Transcriptomic data from the GEO and TARGET databases were utilised to construct and validate a prognostic model for osteosarcoma. The analysis involved the use of the LASSO Cox-regression method with the Glmnet R package to identify key PANoptosis-related genes. Differential gene expression analysis was conducted using the Limma R package, and model validation was performed using Kaplan-Meier survival analysis and time-dependent ROC curves.

Results: This model, derived from five key PANoptosis-related genes, demonstrated significant predictive capability for patient survival across training and validation cohorts. Further analysis confirmed the model's effectiveness and identified metastasis stage and risk scores as the robust independent prognostic indicators.

Conclusion: The prognostic model offers a novel tool for osteosarcoma prognostication and underscores the therapeutic potential of targeting PANoptosis-related pathways.

Key words: PANoptosis-related genes, Osteosarcoma, Prognosis, Bioinformatics, Tumour micro-environment.

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