背景：去粘附死亡（Anoikis）在癌变过程中起着编程性细胞死亡的作用，以清除与基质隔离的未被发现的细胞。进一步基于去粘附死亡的风险分层有望提供对胃腺癌（STAD）癌变过程更深入的理解。 方法：我们从TCGA数据集中获取了STAD患者的信息。从基因组学数据库中获取了与去粘附死亡相关的基因，并进行皮尔逊相关性分析，确定了与去粘附死亡相关的长链非编码RNA（ARLs）。我们使用Univariate Cox回归和最小绝对收缩选择算法（Lasso）分析等机器学习算法对ARLs进行分析，建立了OS分数和OS签名。进一步进行临床亚组分析、肿瘤突变负荷（TMB）检测、药物敏感性分析、免疫浸润和通路富集分析，全面探索临床意义。 结果：我们建立了一个基于五个ARLs的STAD预测模型，并验证了其预测价值。生存分析显示，高风险分组患者的总体生存期明显短于低风险分组患者。柱状图显示了令人满意的区分度和校准性。校准曲线证实了线图预测与实际观察之间的良好协议。TIDE分析和药物敏感性分析显示不同风险组之间存在显著差异。 结论：我们发现的基于去粘附死亡相关的长链非编码RNA的新型预测模型可以用于胃腺癌的预后预测和精准治疗。版权所有 © 2023 Li，张，胡，张，赵和冯。

Background: Anoikis acts as a programmed cell death that is activated during carcinogenesis to remove undetected cells isolated from ECM. Further anoikis based risk stratification is expected to provide a deeper understanding of stomach adenocarcinoma (STAD) carcinogenesis. Methods: The information of STAD patients were acquired from TCGA dataset. Anoikis-related genes were obtained from the Molecular Signatures Database and Pearson correlation analysis was performed to identify the anoikis-related lncRNAs (ARLs). We performed machine learning algorithms, including Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ARLs to build the OS-score and OS-signature. Clinical subgroup analysis, tumor mutation burden (TMB) detection, drug susceptibility analysis, immune infiltration and pathway enrichment analysis were further performed to comprehensive explore the clinical significance. Results: We established a STAD prognostic model based on five ARLs and its prognostic value was verified. Survival analysis showed that the overall survival of high-risk score patients was significantly shorter than that of low-risk score patients. The column diagrams show satisfactory discrimination and calibration. The calibration curve verifies the good agreement between the prediction of the line graph and the actual observation. TIDE analysis and drug sensitivity analysis showed significant differences between different risk groups. Conclusion: The novel prognostic model based on anoikis-related lncRNAs we identified could be used for prognosis prediction and precise therapy in gastric adenocarcinoma.Copyright © 2023 Li, Zhang, Hu, Zhang, Zhao and Feng.