长链非编码RNA（LncRNAs）可能通过调节自噬参与胃癌（GC）的发生、发展和药物耐药。本研究旨在建立一个与自噬相关的LncRNA（ARL）签名（ARLSig），并探究其在GC患者中的免疫基因组意义。从The Cancer Genome Atlas数据库提取了GC患者的RNA测序和临床数据，并从人类自噬数据库提取了自噬基因。通过共表达和Cox回归分析建立了预后性的ARLSig。进一步，利用多个算法探索了危险组和低风险组之间的临床病理学差异、免疫微环境、免疫功能和免疫治疗反应。建立了由11个ARL组成的预后风险模型。ARLSig与全面、T和N分期之间的临床相关性分析表明，ARLSig与这些因素相关（所有P<0.05）。此外，包含ARLSig和临床因素的刻度图表明，它对生存具有强大的预测价值，对1年、3年和5年的生存预测效果高于其他临床病理因素。最后，两个风险组之间的免疫相关分析显示，高风险组中自然杀伤细胞休眠、单核细胞、M2型巨噬细胞和树突状细胞休眠的浸润比例显著高于低风险组，并且表达了25个免疫检查点基因。此外，通过插入/缺失追踪分解算法进行的免疫治疗反应预测显示，低风险组对免疫检查点抑制剂治疗更敏感。GC中由11个ARL组成的ARLSig对GC患者的生存具有高效的预测价值，并可能为个体化免疫治疗提供新的靶点。

Long noncoding RNAs (LncRNAs) may be involved in the occurrence, development, and drug resistance of gastric cancer (GC) by regulating autophagy. This study aims to establish an autophagy-related LncRNA (ARL) signature (ARLSig) and explore its immunogenomic implications in patients with GC. The RNA sequencing and clinical data of patients with GC from The Cancer Genome Atlas database, and autophagy genes from the Human Autophagy Database were extracted. The co-expression and Cox regression analyses were performed to establish a prognostic ARLSig. Further, the differences in clinicopathology, immune microenvironment, immune function, and response to immunotherapy between the risk groups were explored by several algorithms. A prognostic risk model consisting of 11 ARLs was constructed. The clinical correlation analysis between the ARLSig and clinicopathological factors indicated that the ARLSig was correlated with the comprehensive, T, and N stages (all P<0.05). Further, a nomogram including the ARLSig and clinical factors suggested it had a powerful predictive value for survival, with a higher prediction efficiency for 1-, 3-, and 5-year survival than other clinicopathological factors. Finally, the immune-related analysis between the two risk groups showed that the high-risk group had significantly higher infiltration proportions of natural killer cells resting, monocytes, M2 macrophages, and dendritic cells resting, as well as higher expression of 25 immune checkpoint genes. In addition, the immunotherapy response prediction by the tracking of indels by decomposition algorithm showed the low-risk group was more sensitive to immune checkpoint inhibitor therapy. The ARLSig consisting of 11 ARLs in GC showed highly efficient predictive value for survival of patients with GC and might provide novel targets for their individualized immunotherapy.