Advancing Breast Cancer Diagnosis: Optimization of Raman Spectroscopy for Urine-Based Early Detection

Area of Science:

• Biochemistry
• Analytical Chemistry
• Oncology

Background:

• Surface-enhanced Raman spectroscopy (SERS) shows promise for urine-based cancer detection via liquid biopsy.
• Clinical translation is limited by physiological variations (pH, dilution) and unclear molecular origins of SERS signals.
• Understanding SERS spectral variations is crucial for accurate machine learning classification in cancer diagnostics.

Purpose of the Study:

• To investigate the impact of pH on SERS spectra of urine.
• To elucidate the molecular basis of SERS signals in urine.
• To develop methods for improving the accuracy and interpretability of SERS analysis for cancer detection.

Main Methods:

• Analyzed urine samples from breast cancer patients (n=18) and controls (n=10) at pH 5, 7, and 9.
• Studied simulated urine mixtures (uric acid, hypoxanthine, xanthine, creatinine) to understand pH-dependent spectral variations.
• Normalized SERS signals to the creatinine band (1420 cm⁻¹) to correct for urine dilution effects.

Main Results:

• Urine at pH 9 produced the most detailed SERS spectral features.
• Alkaline pH (9) enhanced contributions from hypoxanthine, uric acid, and creatinine, while reducing xanthine signals.
• Normalization to creatinine effectively mitigated dilution-induced signal fluctuations.

Conclusions:

• Adjusting urine pH to 9 and normalizing to creatinine significantly improves SERS analysis interpretability and accuracy.
• These findings advance the development of SERS-based liquid biopsy tools for cancer detection.
• Optimized SERS protocols offer practical solutions for clinical translation in cancer diagnostics.