Feline mammary carcinoma (FMC) is a prevalent and aggressive cancer in female cats, known for its high potential for metastasis. Early detection is crucial to prevent the disease's local and distant spread, which can significantly enhance survival outcomes. The purpose of this study was to evaluate the effectiveness of partial least-squares discriminant analysis (PLS-DA) in rapidly distinguishing peptide clusters from the serum and saliva of cats with various classifications of feline mammary carcinoma (FMC), including different metastatic statuses, histological grades (HGs) and histological types (HTs). This was achieved using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).