Department of Oncology, the Affiliated Hospital of Chengde Medical University, Chengde, China.
Chengde Academy of Agriculture and Forestry, Institute of Medicinal Animals and Plants, Chengde, China.
Medicine (Baltimore). 2024 Oct 4;103(40):e39745. doi: 10.1097/MD.0000000000039745.
Anoikis, a form of programmed cell death linked to cancer, has garnered significant research attention. Esophageal cancer (ESCA) ranks among the most prevalent malignant tumors and represents a major global health concern. To ascertain whether anoikis-related genes (ARGs) can accurately predict ESCA prognosis, we evaluated the predictive value and molecular mechanisms of ARGs in ESCA and constructed an optimal model for prognostic prediction. Using the Cancer Genome Atlas (TCGA)-ESCA database, we identified ARGs with differences in ESCA. ARG signatures were generated using Cox regression. A predictive nomogram model was developed to forecast ARG signatures and patient outcomes in ESCA. Gene set enrichment analysis (GSEA) was employed to uncover potential biological pathways associated with ARG signatures. Estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) and cell-type identification by estimating relative subsets of RNA transcripts analyses were used to assess differences in the immune microenvironment of the ARG signature model. Based on ARGs, the patients with ESCA were divided into high and low groups, and the sensitivity of patients to drugs in the database of genomics of drug sensitivity in cancer was analyzed. Finally, the correlation between drug sensitivity and risk score was then evaluated based on the ARG signatures. Prognostic relevance was significantly linked to the ARG profiles of 5 genes: MYB binding protein 1a (MYBBP1A), plasminogen activator, urokinase (PLAU), budding uninhibited by benzimidazoles 3, HOX transcript antisense RNA, and euchromatic histone-lysine methyltransferase 2 (EHMT2). Using the risk score as an independent prognostic factor combined with clinicopathological features, the nomogram accurately predicted the overall survival (OS) of individual patients with ESCA. Gene ontology (GO) enrichment analysis indicated that the primary molecular roles included histone methyltransferase function, binding to C2H2 zinc finger domains, and histone-lysine N-methyltransferase activity. GSEA revealed that the high-risk cohort was connected to cytokine-cytokine receptor interaction, graft-versus-host disease, and hematopoietic cell lineage, whereas the low-risk cohort was related to arachidonic acid metabolism, drug metabolism via cytochrome P450 and fatty acid metabolism. Drug sensitivity tests showed that 16 drugs were positively correlated, and 3 drugs were negatively correlated with ARG characteristic scores. Our study developed 5 ARG signatures as biomarkers for patients with ESCA, providing an important reference for the individualized treatment of this disease.
细胞凋亡，一种与癌症相关的程序性细胞死亡形式，已经引起了广泛的研究关注。食管癌（ESCA）是最常见的恶性肿瘤之一，也是一个全球性的健康关注问题。为了确定与细胞凋亡相关的基因（ARGs）是否可以准确预测 ESCA 的预后，我们评估了 ARGs 在 ESCA 中的预测价值和分子机制，并构建了一个用于预后预测的最佳模型。使用癌症基因组图谱（TCGA）-ESCA 数据库，我们确定了 ESCA 中存在差异的 ARGs。使用 Cox 回归生成 ARG 特征。开发了一个预测列线图模型，以预测 ARG 特征和 ESCA 患者的结局。使用基因集富集分析（GSEA）来揭示与 ARG 特征相关的潜在生物学途径。使用表达数据（ESTIMATE）估计恶性肿瘤组织中的基质和免疫细胞，并通过估计相对 RNA 转录物分析的细胞类型来评估 ARG 特征模型的免疫微环境差异。基于 ARGs，将 ESCA 患者分为高风险组和低风险组，并分析数据库中基因组药物敏感性分析中患者对药物的敏感性。最后，基于 ARG 特征评估药物敏感性与风险评分之间的相关性。预后相关性与 5 个基因的 ARG 图谱显著相关：MYB 结合蛋白 1a（MYBBP1A）、尿激酶型纤溶酶原激活物（PLAU）、无苯并咪唑抑制芽殖 3、HOX 转录反义 RNA 和 euchromatic histone-lysine methyltransferase 2（EHMT2）。使用风险评分作为独立的预后因素，结合临床病理特征，列线图可以准确预测个体 ESCA 患者的总生存率（OS）。基因本体论（GO）富集分析表明，主要的分子作用包括组蛋白甲基转移酶功能、与 C2H2 锌指结构域结合以及组蛋白赖氨酸 N-甲基转移酶活性。GSEA 表明，高危队列与细胞因子-细胞因子受体相互作用、移植物抗宿主病和造血细胞谱系有关，而低危队列与花生四烯酸代谢、细胞色素 P450 介导的药物代谢和脂肪酸代谢有关。药物敏感性测试表明，有 16 种药物呈正相关，有 3 种药物与 ARG 特征评分呈负相关。我们的研究开发了 5 个 ARG 特征作为 ESCA 患者的生物标志物，为该疾病的个体化治疗提供了重要参考。
