Implementation of Machine Learning-Based System for Early Diagnosis of Feline Mammary Carcinomas through Blood Metabolite Profiling

Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unsprayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration. The diagnosis through these methods is sometimes delayed and unreliable, leading to increased chances of mortality. The objective of this study was to identify the biomarkers, including blood metabolites and genes, related to feline mammary carcinoma, study their relationships, and develop a machine-learning (ML) model for the early diagnosis of the disease. We analyzed blood metabolites of felines with mammary carcinoma using the pathway analysis feature in the MetaboAnalyst software. The metabolic pathways that were elucidated to be associated with this disease include Alanine, aspartate and glutamate metabolism, Glutamine and glutamate metabolism, Arginine biosynthesis, and Glycerophospholipid metabolism. Furthermore, we also elucidated several genes that play a significant role in the development of FMC, such as ERBB2, PDGFA, EGFR, FLT4, ERBB3, FIGF, PDGFC, PDGFB through STRINGdb, a database of known and predicted protein–protein interactions, and MetaboAnalyst 5.0. We utilized ML methods to recognize FMC using blood metabolites of sick patients. The best-performing model was able to predict metabolite class with an accuracy of 85%. In conclusion, our findings demonstrate that the identification of the biomarkers associated with FMC and the affected metabolic pathways can aid in the early diagnosis of feline mammary carcinoma.