SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine?

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 273; p. 120992
• Main Authors: Iancu, Stefania D., Cozan, Ramona G., Stefancu, Andrei, David, Maria, Moisoiu, Tudor, Moroz-Dubenco, Cristiana, Bajcsi, Adel, Chira, Camelia, Andreica, Anca, Leopold, Loredana F., Eniu, Daniela, Staicu, Adelina, Goidescu, Iulian, Socaciu, Carmen, Eniu, Dan T., Diosan, Laura, Leopold, Nicolae
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15-05-2022
• Subjects: Machine learning; Metabolites; SERS liquid biopsy; Serum; Urine; Urine; Serum; SERS liquid biopsy; Metabolites; Machine learning
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2022.120992

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•A critical assignment of the SERS bands of serum and urine samples is provided.•A better classification of breast cancer patients and healthy controls was reached by SERS liquid biopsy of urine compared to serum.•Decision tree and linear discriminant analysis yielded the best classification accuracy for SERS liquid biopsy.
SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical assignment of the SERS bands, our work aims to provide a systematic analysis of the molecular information that can be achieved from the SERS analysis of serum and urine obtained from breast cancer patients and controls. Further, we compared the relative performance of five different machine learning algorithms for breast cancer and control samples classification based on the serum and urine SERS datasets, and found comparable classification accuracies in the range of 61–89%. This result is not surprising since both biofluids show striking similarities in their SERS spectra providing similar metabolic information, related to purine metabolites. Lastly, by carefully comparing the two datasets (i.e., serum and urine) we show that it is possible to link the misclassified samples to specific metabolic imbalances, such as carotenoid levels, or variations in the creatinine concentration.