Frontiers in Endocrinology (Oct 2023)

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

  • Lei Fu,
  • Manshi Li,
  • Junjie Lv,
  • Chengcheng Yang,
  • Zihan Zhang,
  • Shimei Qin,
  • Wan Li,
  • Xinyan Wang,
  • Lina Chen

DOI
https://doi.org/10.3389/fendo.2023.1270772
Journal volume & issue
Vol. 14

Abstract

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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.

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