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

Frontiers in Endocrinology

Endocrinology

Junjie Lv¹  Wan Li¹  Lina Chen¹  Zihan Zhang¹  Lei Fu¹  Shimei Qin¹  Manshi Li²  Xinyan Wang³  Chengcheng Yang³ 

[1] College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China;Department of Radiation Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China;Department of Respiratory, Second Affiliated Hospital of Harbin Medical University, Harbin, China;

关键词: lung adenocarcinoma;    deep neural network;    metabolite-mRNA interactions network;    biomarkers;    risk model;   

DOI  :  10.3389/fendo.2023.1270772

 received in 2023-08-01, accepted in 2023-10-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionLung 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. MethodsThis 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. ResultsFinally, 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. ConclusionTo 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.

【 授权许可】

Unknown   
Copyright © 2023 Fu, Li, Lv, Yang, Zhang, Qin, Li, Wang and Chen