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Open AccessArticle
Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network
by
Murtada K. Elbashir
Murtada K. Elbashir
Murtada K.
Elbashir received a B.Sc. degree (Hons.) in computer/statistics from the University
of [...]
Murtada K.
Elbashir received a B.Sc. degree (Hons.) in computer/statistics from the University
of Gezira, Wad Madani, Sudan, in 2000, an M.Sc. degree (Hons.) in computer
information systems from the University of the Free State, Bloemfontein, South
Africa, in 2003, and a Ph.D. degree in computer science and technology from
Central South University, Changsha, China, in 2013. In 2016, he was promoted to
Associate Professor at the University of Gezira. He is currently an Associate
Professor at the College of Computer and Information Sciences, Jouf University,
Sakaka, Saudi Arabia. His current research interests include machine learning
and bioinformatics.
1,*,
Abdullah Almotilag
Abdullah Almotilag 1,
Mahmood A. Mahmood
Mahmood A. Mahmood 1 and
Mohanad Mohammed
Mohanad Mohammed 2
1
Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi Arabia
2
School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(19), 2178; https://doi.org/10.3390/diagnostics14192178 (registering DOI)
Submission received: 26 August 2024
/
Revised: 24 September 2024
/
Accepted: 27 September 2024
/
Published: 29 September 2024
Abstract
Background: Cancer survival prediction is vital in improving patients’ prospects and recommending therapies. Understanding the molecular behavior of cancer can be enhanced through the integration of multi-omics data, including mRNA, miRNA, and DNA methylation data. In light of these multi-omics data, we proposed a graph attention network (GAT) model in this study to predict the survival of non-small cell lung cancer (NSCLC). Methods: The different omics data were obtained from The Cancer Genome Atlas (TCGA) and preprocessed and combined into a single dataset using the sample ID. We used the chi-square test to select the most significant features to be used in our model. We used the synthetic minority oversampling technique (SMOTE) to balance the dataset and the concordance index (C-index) to measure the performance of our model on different combinations of omics data. Results: Our model demonstrated superior performance, with the highest value of the C-index obtained when we used both mRNA and miRNA data. This demonstrates that the multi-omics approach could be effective in predicting survival. Further pathway analysis conducted with KEGG showed that our GAT model provided high weights to the features that are associated with the viral entry pathways, such as the Epstein–Barr virus and Influenza A pathways, which are involved in lung cancer development. From our findings, it can be observed that the proposed GAT model leads to a significantly improved prediction of survival by exploiting the strengths of multiple omics datasets and the findings from the enriched pathways. Our GAT model outperforms other state-of-the-art methods that are used for NSCLC prediction. Conclusions: In this study, we developed a new model for the survival prediction of NSCLC using the GAT based on multi-omics data. Our model showed outstanding predictive values, and the KEGG analysis of the selected significant features showed that they were implicated in pivotal biological processes underlying pathways such as Influenza A and the Epstein–Barr virus infection, which are linked to lung cancer progression.
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MDPI and ACS Style
Elbashir, M.K.; Almotilag, A.; Mahmood, M.A.; Mohammed, M.
Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network. Diagnostics 2024, 14, 2178.
https://doi.org/10.3390/diagnostics14192178
AMA Style
Elbashir MK, Almotilag A, Mahmood MA, Mohammed M.
Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network. Diagnostics. 2024; 14(19):2178.
https://doi.org/10.3390/diagnostics14192178
Chicago/Turabian Style
Elbashir, Murtada K., Abdullah Almotilag, Mahmood A. Mahmood, and Mohanad Mohammed.
2024. "Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network" Diagnostics 14, no. 19: 2178.
https://doi.org/10.3390/diagnostics14192178
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