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ORIGINAL RESEARCH article

Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1513938
This article is part of the Research Topic Deep Machine Learning and Big Data Resources for Transcriptional Regulation Analysis, Volume II View all articles

Integration of Gene Expression and DNA Methylation Data Using MLA-GNN for Liver Cancer Biomarker Mining

Provisionally accepted
Chun-Yu Lu Chun-Yu Lu 1Zi Liu Zi Liu 1Muhammad Arif Muhammad Arif 2Tanvir Alam Tanvir Alam 2Wang-Ren Qiu Wang-Ren Qiu 1*
  • 1 Jingdezhen Ceramic University, Jingdezhen, China
  • 2 Hamad bin Khalifa University, Doha, Qatar

The final, formatted version of the article will be published soon.

    The early symptoms of hepatocellular carcinoma patients are often subtle and easily overlooked. By the time patients exhibit noticeable symptoms, the disease has typically progressed to middle or late stages, missing optimal treatment opportunities. Therefore, discovering biomarkers is essential for elucidating their functions for the early diagnosis and prevention. In practical research, challenges such as high-dimensional features, low sample size, and the complexity of gene interactions impact the reliability of biomarker discovery and disease diagnosis when using single-omics approaches. To address these challenges, we thus propose, Multi-level attention graph neural network (MLA-GNN) model for analyzing integrated multi-omics data related to liver cancer. The proposed protocol are using feature selection strategy by removing the noise and redundant information from gene expression and DNA methylation data. Additionally, it employs the Cartesian product method to integrate multi-omics datasets. The study also analyzes gene interactions using WGCNA and identifies potential genes through the MLA-GNN model, offering innovative approaches to resolve these issues. Furthermore, this paper identifies FOXL2 as a promising liver cancer marker through gene ontology and survival analysis. Validation using box plots showed that the expression of the gene FOXL2 was higher in patients with hepatocellular carcinoma than in normal individuals. The drug sensitivity correlation and molecular docking results of FOXL2 with the liver cancer-targeting agent lenvatinib emphasized its potential role in hepatocellular carcinoma treatment and highlighted the importance of FOXL2 in hepatocellular carcinoma treatment.

    Keywords: Multi-omics data, Feature Selection, MLA-GNN, Cartesian product, WGCNA

    Received: 19 Oct 2024; Accepted: 27 Nov 2024.

    Copyright: © 2024 Lu, Liu, Arif, Alam and Qiu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Wang-Ren Qiu, Jingdezhen Ceramic University, Jingdezhen, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.