Related Experiment Video
Updated: Jul 11, 2025
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma
Lei Fu1, Manshi Li2, Junjie Lv1
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
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.
Insights
Ten metabolism-related biomarkers were identified for lung adenocarcinoma (LUAD) using a metabolite-mRNA interaction network and deep neural network model. These biomarkers show significant prognostic value, offering potential new therapeutic targets for LUAD.
Area of Science:
- Oncology
- Bioinformatics
- Metabolomics
Background:
- Lung cancer, particularly lung adenocarcinoma (LUAD), is a leading global cause of mortality.
- Metabolite-mRNA interactions are critical in cancer metabolism, highlighting metabolism-related mRNAs as potential therapeutic targets.
Purpose of the Study:
- To construct a metabolite-mRNA interaction (MMI) network to identify novel prognostic biomarkers for LUAD.
- To validate the prognostic value of identified biomarkers using deep neural network (DNN) models and independent datasets.
Main Methods:
- Construction of an MMI network using four databases.
- Identification of differentially expressed metabolism-related mRNAs in LUAD from TCGA data.
- Mining candidate mRNAs contributing to a DNN model and creating a subnetwork of candidate mRNAs and metabolites.
Main Results:
- Ten metabolism-related biomarkers were identified with significant prognostic value in LUAD.
- Validation in independent datasets (TCGA and CPTAC) confirmed the effectiveness of the biomarkers.
- Comparison with traditional machine learning models demonstrated the superiority of the proposed DNN approach.
Conclusions:
- The study successfully identified 10 metabolism-related biomarkers for LUAD using an MMI network and DNN model.
- The identified biomarkers demonstrated robust prognostic value, confirmed across multiple datasets.
- This strategy provides a framework for discovering metabolic biomarkers in other cancer types.
More Related Videos
11:39Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry
Published on: June 9, 2019
07:15Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020