DataSheet_1_Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation.pdf

BackgroundDisulfidptosis is a newly identified variant of cell death characterized by disulfide accumulation, which is independent of ATP depletion. Accordingly, the latent influence of disulfidptosis on the prognosis of lung adenocarcinoma (LUAD) patients and the progression of tumors remains poorly understood.
MethodsWe conducted a multifaceted analysis of the transcriptional and genetic modifications in disulfidptosis regulators (DRs) specific to LUAD, followed by an evaluation of their expression configurations to define DR clusters. Harnessing the differentially expressed genes (DEGs) identified from these clusters, we formulated an optimal predictive model by amalgamating 10 distinct machine learning algorithms across 101 unique combinations to compute the disulfidptosis score (DS). Patients were subsequently stratified into high and low DS cohorts based on median DS values. We then performed an exhaustive comparison between these cohorts, focusing on somatic mutations, clinical attributes, tumor microenvironment, and treatment responsiveness. Finally, we empirically validated the biological implications of a critical gene, KYNU, through assays in LUAD cell lines.
ResultsWe identified two DR clusters and there were great differences in overall survival (OS) and tumor microenvironment. We selected the "Least Absolute Shrinkage and Selection Operator (LASSO) + Random Survival Forest (RFS)" algorithm to develop a DS based on the average C-index across different cohorts. Our model effectively stratified LUAD patients into high- and low-DS subgroups, with this latter demonstrating superior OS, a reduced mutational landscape, enhanced immune status, and increased sensitivity to immunotherapy. Notably, the predictive accuracy of DS outperformed the published LUAD signature and clinical features. Finally, we validated the DS expression using clinical samples and found that inhibiting KYNU suppressed LUAD cells proliferation, invasiveness, and migration in vitro.
ConclusionsThe DR-based scoring system that we developed enabled accurate prognostic stratification of LUAD patients and provides important insights into the molecular mechanisms and treatment strategies for LUAD.

背景 双硫死亡（disulfidptosis）是一种新近发现的细胞死亡亚型，以二硫键堆积为特征，且不依赖ATP耗竭。目前，双硫死亡对肺腺癌（lung adenocarcinoma, LUAD）患者预后及肿瘤进展的潜在影响仍知之甚少。
方法 本研究针对肺腺癌患者的双硫死亡调控因子（disulfidptosis regulators, DRs）的转录组与遗传修饰开展多维度分析，随后通过分析其表达模式定义DR聚类亚型。基于上述聚类得到的差异表达基因（differentially expressed genes, DEGs），本研究整合10种不同机器学习算法的101种独特组合，构建最优预测模型以计算双硫死亡评分（disulfidptosis score, DS）。根据DS的中位数将患者分为高DS组与低DS组。随后对两组患者进行全面比较，分析内容涵盖体细胞突变、临床特征、肿瘤微环境及治疗响应性。最后，本研究通过肺腺癌细胞系实验，对关键基因KYNU的生物学功能进行了实验验证。
结果 本研究成功鉴定出两种DR聚类亚型，二者在总生存期（overall survival, OS）及肿瘤微环境方面存在显著差异。基于不同队列的平均一致性指数（C-index），我们选取“最小绝对收缩与选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）+随机生存森林（Random Survival Forest, RFS）”算法构建双硫死亡评分模型。该模型可有效将肺腺癌患者分为高、低DS亚组，其中低DS亚组患者总生存期更优、突变负荷更低、免疫状态更强，且对免疫治疗的敏感性更高。值得注意的是，本评分模型的预测准确性优于已发表的肺腺癌预后特征及临床特征。最后，我们通过临床样本验证了双硫死亡评分的表达水平，并发现抑制KYNU可在体外抑制肺腺癌细胞的增殖、侵袭与迁移能力。
结论 本研究构建的基于双硫死亡调控因子的评分系统可实现肺腺癌患者的精准预后分层，为肺腺癌的分子机制研究及治疗策略开发提供了重要参考。