肺癌是全世界疾病和死亡的主要原因。肺腺癌（LUAD）是其最常见的亚型。代谢物-mRNA 相互作用在癌症代谢中发挥着至关重要的作用。因此，代谢相关的 mRNA 是癌症治疗的潜在靶标。本研究使用四个数据库构建了代谢物-mRNA 相互作用 (MMI) 网络。我们从肿瘤基因组图谱 (TCGA)-LUAD 队列中检索了显示肿瘤和非肿瘤组织之间显着表达变化的 mRNA，并鉴定了 MMI 中与代谢相关的差异表达 (DE) mRNA。挖掘了对深度神经网络 (DNN) 模型有重大贡献的候选 mRNA。利用MMI和功能分析的结果，我们创建了一个包含候选mRNA和代谢物的子网络。最后，经过生存分析和验证，获得了10个生物标志物。它们在 LUAD 中的良好预后价值在独立数据集中得到了验证。通过与传统机器学习模型的比较，它们的有效性在 TCGA 和独立的临床蛋白质组学肿瘤分析联盟 (CPTAC) 数据集中得到证实。总而言之，确定了 10 个代谢相关的生物标志物，并通过 MMI 网络成功证实了它们的预后价值和 DNN 模型。我们的策略具有重要意义，可为研究其他癌症的代谢生物标志物铺平道路。版权所有 © 2023 Fu、Li、Lv、Yang、Zhang、Qin、Li、Wang 和 Chen。

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy.This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites.Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models.To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.Copyright © 2023 Fu, Li, Lv, Yang, Zhang, Qin, Li, Wang and Chen.