## 4 Discussion
The assignment of SERS spectra in the case of biofluids such as urine or serum is difficult, given the enormous chemical complexity of these matrices [9]. In line with previous studies, we have assigned several bands to uric acid (SERS bands at 650, 809, 1017, 1135 and 1522 cm\({}^{-1}\)), xanthine (SERS bands at 1135, 1251 and 1318 cm\({}^{-1}\)) and hypoxanthine (SERS bands at 724, 1094, 1459 and 1595 cm\({}^{-1}\)) [18; 23; 24]. The perturbations in the pathways of the purine metabolism (also called quininosome) are known to play a significant role in the onset and progression of cancer [25]. For instance, an increase in the levels of uric acid is known to accompany an increase in the cellular turnover, as it is the case with a malignant lesion which spreads into the surrounding tissues [26]. However, the processes that regulate the purine metabolism are intricate and there are ongoing efforts to clarify the differential expression of purine metabolites that accompany different types of cancer [25]. The fact that some SERS bands remain unassigned underscores an important limitation of SERS strategy, which is the uncertainty regarding the analytes responsible for the SERS signal. This is particularly applicable in the case of biofluids such as urine or serum, which contain a myriad of metabolites that could be responsible for the SERS signal. Nonetheless, the shape of mean spectra difference showed that some of the bands are more intense in the case of urine samples from breast cancer whereas other SERS bands are more intense in the case of urine samples from controls. To inspect the degree of separation between samples corresponding to the breast cancer group and controls, we performed PCA. The results show a good separation between the two groups, especially when plotting PC 1 versus PC 3 (Figure 4). Given that PCA is an unsupervised method, it cannot be used for calculating figures of merit such as sensitivity and specificity values but only for inspecting the relation between the samples. To quantify the classification accuracy resulting from SERS spectra of urine, we performed PCA-LDA using the first 7 PC score values as input. Together, the first 7 PCs accounted for 82% of the variability in the data set (Figure S2). For each sample, LDA calculated a discriminant value corresponding to each group and the sample was assigned to the group having the highest discriminant value (Figure 5a). Thus, the points in Figure 5a that sit above the dashed line were assigned to the breast cancer group, while the points below the dashed line were assigned to the control group. The HotellingT\({}^{2}\) values corresponding to the control group and the breast cancer group are presented in Figure S3 and Figure S4, respectively. The confusion matrix corresponding to the PCA-LDA of SERS spectra from urine samples is presented in Figure 5b and it corresponds to a specificity of 95%, a sensitivity of 81% and an overall accuracy of 88%. For comparison, in the study by [PERSON] _et al._ on n = 20 urine samples from prostate cancer patients, the authors reported an overall accuracy of 95% [11], while in the preliminary Raman study on urine from rats with breast cancer the sensitivity and specificity reported by the authors was 80% and 72%, respectively, using unprocessed urine and 78% and 91%, respectively, using concentrated urine [10]. Thus, the results of our study are in line with previous reports on the use of Raman and SERS spectroscopy of urine for discriminating between cancer patients and controls, which reported similar figures of merit [10; 11]. Given that our dataset lacked urine from patients with stage I and stage IV breast cancer patients, we did not include the stage of the cancer in our analysis. Nonetheless, [PERSON] et al. showed that SERS spectra of filtered serum acquired from breast cancer patients enable the classification of samples according to cancer stage [14]. Whether the SERS spectra of urine also display features that are specific to cancer stage will require further studies.