Wei Gang, Wang Yan, Liu Ru, Liu Lei
Emergency Department, The XIJING 986 Hospital of Air Force Medical University, Xi'an, Shaanxi, China.
Department of Clinical Laboratory, The Second Affiliated Hospital of Air Force Medical University, Xi'an, Shaanxi, China.
Biochem Biophys Rep. 2024 Dec 4;41:101891. doi: 10.1016/j.bbrep.2024.101891. eCollection 2025 Mar.
Gastric cancer (GC), a prevalent and deadly malignancy, demonstrates poor survival outcomes. Evidence has emerged indicating that disruptions in endoplasmic reticulum homeostasis are significantly implicated in the onset and progression of various oncological conditions. This study was designed to construct a prognostic model based on genes related to endoplasmic reticulum stress(ERS) to predict survival outcomes in patients with GC.
Expression profiling data for GC samples were extracted and analyzed from TCGA-STAD, revealing 214 genes related to endoplasmic reticulum stress that show differential expression when compared with normal gastric tissue. Building on these insights, a prognostic model was formulated using data from TCGA-STAD and validated through subsequent analyses of GEO datasets. The tumor immune dysfunction and exclusion(TIDE) algorithm was applied to determine the susceptibility of individuals in high- and low-risk categories to immunotherapy. The presence of immune and stromal cells within the tumor microenvironment was assessed with the aid of the ESTIMATE algorithm. Sensitivity variations to prevalent anticancer drugs between the risk groups were evaluated using the Genomics of Drug Sensitivity in Cancer(GDSC) database, and prospective therapeutic agents were confirmed through molecular docking techniques.
Thirty-one endoplasmic reticulum stress (ERS)-related differentially expressed genes (DEGs) crucial for prognosis in GC were pinpointed. These DEGs were then used to construct a prognostic model and were considered as independent prognostic factors for GC patients. This risk model proved to have a good predictive performance for estimating the overall survival of these patients. The patients placed into the high-risk group showed worse results and lower sensitivity to immunotherapy. Moreover, five specific targeted therapy drugs, namely BMS-754807, Dasatinib, JQ1, AZD8055 and SB505124, produced better results in the treatment of the high-risk group of patients.
A new molecular prognostic model associated with ERS was established and validated for GC and showed relatively good discriminative and predictive ability. This model greatly expands the collection of weapons in the armoury of prognostic analysis in GC.
胃癌（GC）是一种常见且致命的恶性肿瘤，其生存结果较差。有证据表明，内质网稳态的破坏与各种肿瘤疾病的发生和发展密切相关。本研究旨在构建基于内质网应激（ERS）相关基因的预后模型，以预测GC患者的生存结果。
从TCGA-STAD中提取并分析GC样本的表达谱数据，发现214个与内质网应激相关的基因，与正常胃组织相比显示出差异表达。基于这些见解，使用来自TCGA-STAD的数据制定了预后模型，并通过对GEO数据集的后续分析进行验证。应用肿瘤免疫功能障碍和排除（TIDE）算法来确定高风险和低风险类别个体对免疫治疗的易感性。借助ESTIMATE算法评估肿瘤微环境中免疫和基质细胞的存在。使用癌症药物敏感性基因组学（GDSC）数据库评估风险组之间对常见抗癌药物的敏感性差异，并通过分子对接技术确认潜在的治疗药物。
确定了31个与内质网应激（ERS）相关的差异表达基因（DEG），这些基因对GC的预后至关重要。然后使用这些DEG构建预后模型，并将其视为GC患者的独立预后因素。该风险模型被证明在估计这些患者的总生存期方面具有良好的预测性能。处于高风险组的患者结果较差，对免疫治疗的敏感性较低。此外，五种特定的靶向治疗药物，即BMS-754807、达沙替尼、JQ1、AZD8055和SB505124，在治疗高风险组患者方面产生了更好的效果。
建立并验证了一种与ERS相关的新的GC分子预后模型，该模型具有较好的判别和预测能力。该模型极大地扩展了GC预后分析的武器库。
