Meng Fanmao, Sun Xin, Guo Wei, Shi Yong, Cheng Wenhui, Zhao Liang
Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, PR China.
Department of Medical Management, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, PR China.
Heliyon. 2023 Nov 17;9(12):e22434. doi: 10.1016/j.heliyon.2023.e22434. eCollection 2023 Dec.
Cell death is a key regulatory process in organisms and its study has become increasingly important in the field of cancer. While prior research has primarily centered on the individual pathways of cell death in cancer, there has been a lack of comprehensive investigation into the synergistic effects of multiple cell death pathways.
Genes related to autophagy, apoptosis, necroptosis, pyroptosis, and cuproptosis was selected, and patients' data was collected from The Cancer Genome Atlas (TCGA)project. Cell death features were identified using principal component analysis and combined to create a composite score. A scalable prediction model was then created using LASSO regression after a thorough assessment of the composite scores. The model was subsequently validated across multiple external datasets to establish its robustness and reliability.
The cell death features effectively represented the gene expression patterns in the samples. The composite score well predicted prognosis, clinical stage, mutation, tumor microenvironment, and immunotherapy effectiveness. The model built on composite scores accurately predicted prognosis and immunotherapy effectiveness across multiple datasets. was identified as a potential biomarker.
Models based on multiple cell death pathways have significant predictive power for prognosis and immunotherapy effectiveness in lung adenocarcinoma. This highlights the synergistic role of multiple cell death pathways in cancer development and offers a new perspective for cancer research.
细胞死亡是生物体中的关键调节过程，其研究在癌症领域变得越来越重要。虽然先前的研究主要集中在癌症中细胞死亡的各个途径，但对多种细胞死亡途径的协同作用缺乏全面的研究。
选择与自噬、凋亡、坏死性凋亡、焦亡和铜死亡相关的基因，并从癌症基因组图谱（TCGA）项目中收集患者数据。使用主成分分析识别细胞死亡特征，并将其组合以创建一个综合评分。在对综合评分进行全面评估后，使用套索回归创建一个可扩展的预测模型。随后在多个外部数据集中对该模型进行验证，以确定其稳健性和可靠性。
细胞死亡特征有效地代表了样本中的基因表达模式。综合评分能很好地预测预后、临床分期、突变、肿瘤微环境和免疫治疗效果。基于综合评分构建的模型在多个数据集中准确预测了预后和免疫治疗效果。被确定为潜在的生物标志物。
基于多种细胞死亡途径的模型对肺腺癌的预后和免疫治疗效果具有显著的预测能力。这突出了多种细胞死亡途径在癌症发展中的协同作用，并为癌症研究提供了新的视角。
