背景：涉及氨基酸的代谢过程与癌症的发生和进展密切相关。长链非编码RNA（LncRNA）在调节代谢过程和促进肿瘤发展中起着不可或缺的作用。然而，尚未对与氨基酸代谢相关的LncRNA（AMML）在预测胃腺癌（STAD）预后中可能发挥的作用进行研究。因此，本研究旨在设计一个AMML模型来预测STAD相关的预后，并阐明它们的免疫特性和分子机制。 方法：在TCGA-STAD数据集中，将STAD RNA-seq数据按1：1的比例随机分为训练组和验证组，并分别构建和验证模型。在分子签名数据库中，本研究筛选了涉及氨基酸代谢的基因。通过Pearson相关分析获得AMML并使用最小绝对收缩和选择算子（LASSO）回归、单变量Cox分析和多元Cox分析建立预测风险特征。随后，检查高风险和低风险患者的免疫和分子特征以及药物的受益情况。 结果：使用11个AMML（LINC01697、LINC00460、LINC00592、MIR548XHG、LINC02728、RBAKDN、LINCOG、LINC00449、LINC01819和UBE2R2-AS1）开发了一个预后模型。此外，验证组和综合组中高风险个体的总体生存率（OS）均低于低风险患者。高风险评分与癌症转移，血管生成途径和肿瘤相关成纤维细胞、Treg细胞和M2巨噬细胞的高浸润、免疫抑制反应以及更具侵略性的表型有关。 结论：本研究确定了与11个AMML相关的风险信号，并建立了STAD的OS预测图。这些发现将帮助我们个性化治疗胃癌患者。版权所有 © 2023 Jin、Ou、Li、Liu和Zhao。

Background: The metabolic processes involving amino acids are intimately linked to the onset and progression of cancer. Long non-coding RNAs (LncRNAs) perform an indispensable function in the modulation of metabolic processes as well as the advancement of tumors. Non-etheless, research into the role that amino acid metabolism-related LncRNAs (AMMLs) might play in predicting the prognosis of stomach adenocarcinoma (STAD) has not been done. Therefore, This study sought to design a model for AMMLs to predict STAD-related prognosis and elucidate their immune properties and molecular mechanisms. Methods: The STAD RNA-seq data in the TCGA-STAD dataset were randomized into the training and validation groups in a 1:1 ratio, and models were constructed and validated respectively. In the molecular signature database, This study screened for genes involved in amino acid metabolism. AMMLs were obtained by Pearson's correlation analysis, and predictive risk characteristics were established using least absolute shrinkage and selection operator (LASSO) regression, univariate Cox analysis, and multivariate Cox analysis. Subsequently, the immune and molecular profiles of high- and low-risk patients and the benefit of the drug were examined. Results: Eleven AMMLs (LINC01697, LINC00460, LINC00592, MIR548XHG, LINC02728, RBAKDN, LINCOG, LINC00449, LINC01819, and UBE2R2-AS1) were used to develop a prognostic model. Moreover, high-risk individuals had worse overall survival (OS) than low-risk patients in the validation and comprehensive groups. A high-risk score was associated with cancer metastasis as well as angiogenic pathways and high infiltration of tumor-associated fibroblasts, Treg cells, and M2 macrophages; suppressed immune responses; and a more aggressive phenotype. Conclusion: This study identified a risk signal associated with 11 AMMLs and established predictive nomograms for OS in STAD. These findings will help us personalize treatment for gastric cancer patients.Copyright © 2023 Jin, Ou, Li, Liu and Zhao.