SERS-based simultaneous multibiomarkers sensing for precision diagnosis of lung cancer

Accurate and early diagnosis of lung cancer is critical for effective treatment and improved patient outcomes. Traditional diagnostic methods face challenges in sensitivity and specificity, particularly for multi-biomarker detection in complex biological samples. This study introduces a novel platform combining Surface-Enhanced Raman Spectroscopy (SERS) with machine learning techniques for simultaneous multi-biomarker detection and quantification. The developed SERSIA platform leverages gold nanoparticle-based substrates and advanced classification algorithms (t-SNE, SVM) to achieve high sensitivity and specificity. Validation studies on human serum samples revealed that the platform could accurately detect and quantify four key lung cancer biomarkers—CYFRA21-1, CEA, SCC-ag, and GCC2—achieving 92% diagnostic accuracy. Moreover, the method enabled precise differentiation of cancer subtypes and stages with over 82% accuracy. This study underscores the transformative potential of integrating SERS and machine learning in advancing precision diagnostics, paving the way for broader clinical applications in early cancer detection and personalized medicine.