Abstract：
Gastric cancer (GC) is highly heterogeneous and influenced by aging-related factors. This study aimed to improve individualized prognostic assessment of GC by identifying aging-related genes and subtypes. Immune scores of GC samples from GEO and TCGA databases were calculated using ESTIMATE and scored as high immune (IS_high) and low immune (IS_low). ssGSEA was used to analyze immune cell infiltration. Univariate Cox regression was employed to identify prognosis-related genes. LASSO regression analysis was used to construct a prognostic model. GSVA enrichment analysis was applied to determine pathways. CCK-8, wound healing, and Transwell assays tested the proliferation, migration, and invasion of the GC cell line (AGS). Cell cycle and aging were examined using flow cytometry, β-galactosidase staining, and Western blotting. Two aging-related GC subtypes were identified. Subtype 2 was characterized as lower survival probability and higher risk, along with a more immune-responsive tumor microenvironment. Three genes (IGFBP5, BCL11B, and AKR1B1) screened from aging-related genes were used to establish a prognosis model. The AUC values of the model were greater than 0.669, exhibiting strong prognostic value. In vitro, IGFBP5 overexpression in AGS cells was found to decrease viability, migration, and invasion, alter the cell cycle, and increase aging biomarkers (SA-β-galactosidase, p53, and p21). This analysis uncovered the immune characteristics of two subtypes and aging-related prognosis genes in GC. The prognostic model established for three aging-related genes (IGFBP5, BCL11B, and AKR1B1) demonstrated good prognosis performance, providing a foundation for personalized treatment strategies aimed at GC.
摘要：
胃癌（GC）是高度异质性的，并受衰老相关因素的影响。本研究旨在通过识别与衰老相关的基因和亚型来改善GC的个体化预后评估。使用ESTIMATE计算来自GEO和TCGA数据库的GC样品的免疫评分,并评分为高免疫(IS_高)和低免疫(IS_低)。ssGSEA用于分析免疫细胞浸润。单变量Cox回归用于鉴定预后相关基因。LASSO回归分析用于构建预后模型。应用GSVA富集分析来确定途径。CCK-8，伤口愈合，Transwell分析测试了增殖，迁移,和GC细胞系(AGS)的侵袭。使用流式细胞术检查细胞周期和衰老，β-半乳糖苷酶染色,和西方印迹。鉴定了两种衰老相关的GC亚型。亚型2的特点是生存概率较低，风险较高。以及更具免疫反应性的肿瘤微环境。三个基因（IGFBP5，BCL11B，和AKR1B1)从衰老相关基因中筛选,建立预后模型。模型的AUC值大于0.669,表现出较强的预后价值。体外，IGFBP5在AGS细胞中的过表达被发现降低活力,迁移,和入侵,改变细胞周期，并增加衰老生物标志物(SA-β-半乳糖苷酶，p53和p21)。该分析揭示了GC中两种亚型和衰老相关预后基因的免疫特征。针对三个衰老相关基因（IGFBP5，BCL11B，和AKR1B1)表现出良好的预后表现，为针对GC的个性化治疗策略提供基础。
