Quantum AI for Cancer Diagnostic Biomarker Discovery

2026-04-17

AI Summary

This study addresses the challenge of subtype-specific diagnosis in non-small cell lung cancer (NSCLC) by proposing a two-stage approach: first, integrating differential expression and methylation analyses to identify adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC)-specific biomarkers, and then constructing a quantum machine learning classifier to accurately distinguish among LUAD, LUSC, and normal samples. This work represents the first application of quantum computing to multi-omics analysis in lung cancer, uncovering key genes—including NGFR, NTRK2, and NTF3—and implicating neurotrophin, MAPK, Ras, and PI3K-Akt signaling pathways in tumorigenesis, thereby highlighting the role of neural signaling in cancer development. The quantum classifier demonstrates superior performance and scalability on biomedical big data, with the Sample3 gene set achieving optimal results across all evaluation metrics.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In this study, we apply QML to identify subtype specific biomarkers for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two predominant forms of non-small cell lung cancer. Our methodology involves a two-phase process: in Phase 1, differential expression analysis and methylation analysis between tumor and normal samples allows us to identify LUAD-specific and LUSC-specific genes, revealing potential prognostic biomarkers for cancer subtypes. Phase 2 focuses on developing a quantum classifier capable of distinguishing between LUAD and LUSC tumors, as well as between tumor and normal samples. This classifier not only enhances diagnostic precision but also demonstrates the quantum advantage in processing large-scale multiomic datasets. Our results consistently demonstrated that Sample3, representing the combined gene set, achieved the highest overall predictive performance in all metrics. These results demonstrate that QML provides an effective and scalable approach for biomarker discovery and subtype specific cancer classification. GO enrichment analysis highlighted the significant involvement of genes in synaptic signaling, ion channel regulation, and neuronal development. In the quantum phase, KEGG analysis further identified enrichment in cancer-associated pathways, including neurotrophin, MAPK, Ras, and PI3KAkt signaling, with key genes such as NGFR, NTRK2, and NTF3 suggesting a central role in neurotrophinmediated oncogenic processes. Our findings highlight the growing potential of quantum computing to advance precision oncology and next-generation biomedical analytics.

Problem

• Quantum AI
• biomarker discovery
• lung cancer subtypes
• cancer diagnostics
• precision oncology

Innovation

• Quantum Machine Learning
• Biomarker Discovery
• Cancer Subtype Classification
• Multiomic Data Analysis
• Quantum Advantage