Cervical cancer biomarker screening based on Raman spectroscopy and multivariate statistical analysis

Area of Science:

• Biomedical Spectroscopy
• Chemical Analysis
• Cancer Diagnostics

Background:

• Cervical cancer (CC) is a significant global health concern for women.
• Accurate diagnosis and identification of biomarkers are crucial for effective CC management.
• Serum tumor markers play a vital role in CC diagnosis and treatment monitoring.

Purpose of the Study:

• To investigate the potential of Surface-Enhanced Raman Spectroscopy (SERS) combined with Multivariate Statistical Analysis (MSA) for cervical cancer diagnosis.
• To identify novel serum biomarkers for distinguishing CC patients from healthy individuals.
• To evaluate the diagnostic performance of identified biomarkers.

Main Methods:

• Serum samples from 95 CC patients and 81 healthy subjects were analyzed using SERS.
• Multivariate Statistical Analysis (MSA), including Partial Least Squares Discriminant Analysis (PLS-DA) and Random Forest (RF), was employed.
• Characteristic spectral peaks and their associated biomolecules were identified and correlated with CC presence.

Main Results:

• The PLS-DA model achieved a high diagnostic accuracy of 97.73%, with 100% sensitivity and 95.83% specificity.
• Key spectral features linked to DNA, tyrosine, adenine, valine, D-mannose, and amide I were identified.
• D-mannose demonstrated excellent performance as a potential biomarker, with an Area Under the Curve (AUC) of 0.979.

Conclusions:

• SERS combined with MSA offers a powerful approach for accurate cervical cancer diagnosis.
• Specific serum components, particularly D-mannose, show significant potential as diagnostic biomarkers for CC.
• This method facilitates the identification of novel biomarkers, improving early detection and management strategies for cervical cancer.