Li Qinglin, Zhang Huangjie, Hu Jinguo, Zhang Lizhuo, Zhao Aiguang, Feng He
Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
Front Pharmacol. 2023 Feb 28;14:1124262. doi: 10.3389/fphar.2023.1124262. eCollection 2023.
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. 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. 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. The novel prognostic model based on anoikis-related lncRNAs we identified could be used for prognosis prediction and precise therapy in gastric adenocarcinoma.
失巢凋亡是一种程序性细胞死亡，在致癌过程中被激活，以清除从细胞外基质分离的未被检测到的细胞。进一步基于失巢凋亡的风险分层有望为胃腺癌（STAD）的致癌作用提供更深入的理解。STAD患者的信息从TCGA数据集中获取。从分子特征数据库中获得失巢凋亡相关基因，并进行Pearson相关性分析以鉴定失巢凋亡相关lncRNA（ARL）。我们对ARL进行了机器学习算法，包括单变量Cox回归和最小绝对收缩和选择算子（Lasso）分析，以构建OS评分和OS特征。进一步进行临床亚组分析、肿瘤突变负担（TMB）检测、药物敏感性分析、免疫浸润和通路富集分析，以全面探索其临床意义。我们基于五个ARL建立了一个STAD预后模型，并验证了其预后价值。生存分析表明，高危评分患者的总生存期明显短于低危评分患者。柱状图显示出令人满意的区分度和校准度。校准曲线验证了折线图预测与实际观察之间的良好一致性。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。我们鉴定的基于失巢凋亡相关lncRNA的新型预后模型可用于胃腺癌的预后预测和精准治疗。
