MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality

TL;DR

This study uses a new computational model to predict key genes and drugs for treating non-small cell lung cancer.

Contribution

The study introduces MVGAE, a novel multi-view graph auto-encoder model for drug prediction based on synthetic lethality in NSCLC.

Findings

Predicted seven potential driver genes associated with NSCLC using the NIAPU model.

Identified nine genes with synthetic lethality interactions as candidate therapeutic targets.

Predicted corresponding targeted drugs using the MVGAE model, including PAZOPANIB.

Abstract

What are the main findings?
This study predicts nine key therapeutic target genes in non-small cell lung cancer (NSCLC) and identified their corresponding potential targeted drugs.

This study predicts nine key therapeutic target genes in non-small cell lung cancer (NSCLC) and identified their corresponding potential targeted drugs.

What are the implications of the main findings?
2.Provides experimentally testable target and drug candidates for NSCLC therapy.3.Establishes an extensible computational pipeline for multi-omics target discovery.

Provides experimentally testable target and drug candidates for NSCLC therapy.

Establishes an extensible computational pipeline for multi-omics target discovery.

Identifying therapeutic target genes and their corresponding targeted drugs is of significant importance for the treatment of non-small cell lung cancer (NSCLC). This study proposes a multi-view graph auto-encoder model (MVGAE), which, together with the network-informed adaptive positive-unlabeled (NIAPU) and synthetic lethality multi-view graph auto-encoder (SLMGAE) model, constitutes an integrated computational framework. The framework integrates multi-source biological network data, including protein–protein interaction networks, disease-gene association information, and gene-drug bipartite graphs, for data mining. Through systematic analysis and computational screening, we ultimately predicted seven potential driver genes associated with NSCLC using the NIAPU model. The SLMGAE model predicted nine genes with synthetic lethality (SL) interactions to these driver genes as candidate therapeutic targets. Based on these SL targets, the MVGAE model further predicted corresponding targeted drugs. Notably, among the prioritized targets, existing studies indicate that ATR and RAD51 exhibit conditional SL effects in the context of functional impairment. Furthermore, several of the predicted candidate drugs (such as PAZOPANIB) have been previously reported to play a positive role in NSCLC treatment. This study highlights MVGAE as a novel computational framework for drug repurposing and demonstrates how its integration with complementary models can effectively prioritize potential therapeutic targets and candidate drugs, providing a robust computational basis for precision treatment strategies.

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