Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma

• Published in: Frontiers in endocrinology (Lausanne) Vol. 14; p. 1270772
• Main Authors: Fu, Lei, Li, Manshi, Lv, Junjie, Yang, Chengcheng, Zhang, Zihan, Qin, Shimei, Li, Wan, Wang, Xinyan, Chen, Lina
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 25.10.2023
• Subjects: biomarkers; deep neural network; Endocrinology; lung adenocarcinoma; metabolite-mRNA interactions network; risk model
• ISSN: 1664-2392; 1664-2392
• DOI: 10.3389/fendo.2023.1270772

Introduction 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. Methods 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. Results 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. Conclusion 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.