Biomarker Discovery & AI in Oncology
Identification of diagnostic and prognostic biomarkers in lung adenocarcinoma through integrated bioinformatics analysis and real time PCR validation
Author: Rasoul Hossein Zadeh et al.
Publication Date: 30 January 2026
Unlocking Early Lung Cancer Detection with AI-Powered Biomarker Discovery
This study leverages advanced deep learning and bioinformatics to identify novel diagnostic and prognostic biomarkers for lung adenocarcinoma, validating key findings with real-time PCR. Our approach significantly enhances early detection accuracy, offering a robust tool for personalized treatment strategies and improved patient outcomes.
98.44%
Predictive Accuracy
1.0
AUC Score
3,513
Differentially Expressed Genes
4
Validated Biomarkers
Deep Analysis & Enterprise Applications
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98.44%
AI Model Predictive Accuracy for Lung Adenocarcinoma Diagnosis
Enterprise Process Flow
RNA-Seq Data Collection (522 Samples)
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Data Preprocessing & DEG Identification
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Deep Learning Algorithm Application
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Functional & Pathway Enrichment Analysis
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Correlation with Clinical Data
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Prognostic Marker Identification
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ROC Curve Analysis
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External Validation (GDAC, GEO)
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RT-PCR Validation (30 Patients, 30 Controls)
| Feature | Deep Learning Model | Traditional Models (Logistic Regression, SVM) |
|---|---|---|
| Predictive Accuracy | 98.44% | Lower |
| AUC Score | 1.0 | Lower |
| Handling Nonlinear Relationships | Highly Effective | Limited |
| Integration with Multi-omics | Strong Potential | Complex |
| Biomarker Robustness | Enhanced | Standard |
Key Diagnostic & Prognostic Biomarkers Identified
The study identified and validated several key genes with significant diagnostic and prognostic potential in lung adenocarcinoma.
- CYP2C9: Role: Diagnostic & Prognostic, 4.1x higher in cancer. Expression: Upregulated.
- A2M: Role: Diagnostic, 0.5x in cancer (anti-tumor role). Expression: Downregulated.
- KRT14: Role: Diagnostic & Prognostic, 8.1x higher in cancer, linked to metastasis. Expression: Upregulated.
- PECAM1: Role: Diagnostic, 2.2x higher in cancer, role in angiogenesis. Expression: Upregulated.
- LOC730668: Role: Prognostic, pseudogene with epigenetic regulation. Expression: Identified as prognostic.
Clinical Translation Potential
The identified biomarkers, particularly the combination of A2M, CYP2C9, KCNV1, KRT24, and SIRPD, offer a robust tool for early detection and personalized treatment strategies. This approach moves beyond traditional analysis by integrating deep learning with experimental validation, providing highly accurate and clinically actionable insights. The focus on minimally invasive testing using peripheral blood samples further enhances its translational potential.
- Enhanced accuracy of early detection and diagnosis.
- Potential for personalized treatment plans.
- Minimally invasive diagnostic approach.
- Integration of computational modeling with RT-PCR validation.
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Estimated Annual Savings
$55,000
Annual Hours Reclaimed
1,100
Your AI Implementation Roadmap
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Phase 1: Discovery & Strategy (2-4 Weeks)
In-depth analysis of current workflows, identification of AI opportunities, and development of a customized implementation strategy aligned with business objectives.
Phase 2: Solution Design & Development (6-12 Weeks)
Building and customizing AI models, integrating with existing systems, and developing necessary infrastructure for seamless deployment.
Phase 3: Pilot & Iteration (4-8 Weeks)
Deployment of AI solution in a controlled environment, rigorous testing, performance monitoring, and iterative refinement based on feedback.
Phase 4: Full-Scale Deployment & Optimization (Ongoing)
Gradual rollout across the enterprise, comprehensive training for users, continuous monitoring, and optimization for sustained performance and ROI.
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