# A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer
## 4 Discussion
Alterations in galactose, starch, and sucrose metabolism appeared linked to early-stage, while changes in glycine, serine, threonine, arginine, proline, glycerophospholipid, and galactose metabolism seemed to be present in late-stage BC patients. Using these findings, they developed a machine-learning classification model, able to predict metabolite class with an accuracy of 82.54% and an area under precision-recall curve (PRC) of 0.84 on the training set. A similar approach was performed by [PERSON] et al. [49], who selected six putative bladder cancer markers by comparing the urinary metabolomic profiles of affected patients versus controls using UPLC-TOF-MS analysis. Basing on metabolomic profiles and the six marker candidates, a machine learning model, decision trees, was built, obtaining an accuracy of 76.60%, a sensitivity of 71.88%, and a specificity of 86.67% from an independent test. The increased concentrations of phenol, diethylene and propylene glycol here reported in BC patients could be due to a higher exposure to these molecules or the metabolism of related pollutants. Additionally, the smoking habits of our enrolled cohort could, at least partially, explain this result. Furthermore, the involvement of small chain fatty acids such as butyrate and propionate could be due to microbiome differences between BC and healthy controls [50]. Microbiomes play a key role in cancer development and promotion [51]. Specifically, the effects of the microbiome on the BC metabolome has been reported [52; 53; 54]. [PERSON] et al. [52] reported a significant difference in urine microbiota of patients with bladder cancer compared to healthy controls highlighting a dysbiosis involvement in tumor transformation. Moreover, they reported that BCL displayed a reduction in the abundance of _Sphingobacteriaceae_, _Bifidobacteriaceae_, and _Enterobacteriaceae_, while BCH showed decreased _Bifidobacterium_ and _Ruminococcus_, which are known to protect from inflammation, and increased _Corynebacterium_, a potential opportunistic bacteria highlighting dysbiosis association with aggressive tumors. Moreover, norvaline which is a microbial metabolite, produced especially by _E. coli_ in low oxygen environmental [55], a common condition in rapid growth cancer, was found to be elevated in BCH patients. To date, there are no standard screening evaluations for BC and only symptomatic subjects undergo further investigation, including urine cytology, pelvic ultrasonography, office endoscopic and biopsy [56]. Urine cytology shows high sensitivity (greater than 90%) for BCH, although negative findings do not exclude malignancy. Moreover, renal calculi and urinary tract infections can lead to false-positive results [57]. The reported ensemble classification model for the differentiation of symptomatic (with hematuria) but otherwise healthy patients from BC patients has shown good diagnostic performance. Moreover, the serum metabolic trajectory of investigated BC patients supports a consistent Warburg effect on these cells, aiding researchers investigating glycolysis inhibiting drugs [31]. Ensemble approaches to machine learning in cancer diagnosis were also investigated by Oman [58]. Six ensemble methods were studied (Bagging, Dagging, Ada Boost, Multi Boost, Decorate, and Random Subspace) within 14 ML algorithms for automatic detection of breast cancer using features computed from digitized images of fine needle aspirate (FNA) of a breast mass. Results illustrate that ensemble learning improved the global predictive performance compared to the single classifiers. Due to the pilot nature of our study, the classification performances of the single and EML models cannot be considered definitive. Both a higher number of enrolled patients and a blind evaluation of BC negative patients are two pivotal steps to fully validate the proposed metabolomics signature and bioinformatic method.