Objective:To explore the changes in serum metabolic profile in patients with oral squamous cell carcinoma (OSCC) and to identify the diagnostic biomarkers in order to provide new ideas for the early diagnosis of OSCC.Methods:In total, 76 OSCC patients who were diagnosed at the Department of Oral and Maxillofacial Surgery and 70 healthy individuals who at the Department of Medical Center of The First Affiliated Hospital of Zhengzhou University from August 2019 to January 2020 were recruited in The study. According to the random number table method, all subjects were divided into a test group ( n=96) and a verification group ( n=50). Subjects in the test group consisted of 51 OSCC patients and 45 healthy subjects and subjects in the verification group included 25 OSCC patients and 25 healthy individuals. Serum samples and clinical data of each of the subjects were collected. The serum samples were analyzed by ultra-high-performance liquid chromatography quadrupole-Orbitrap high resolution accurate mass spectrometry. Principal component analysis, orthogonal partial least square discrimination analysis and t-test were used to profile the differential metabolites in the test group. Pathway analysis of differential metabolites was performed. In addition, binary logistic regression analysis and receiver operating characteristic analysis were used in order to establish the potential diagnostic panel. Results:Twenty-one endogenous differential metabolites were identified showing significant association with OSCC. Results of pathway analysis suggested that OSCC associated with lipid metabolism and amino acid metabolism ( P<0.05). A novel diagnostic panel consisting of lysophosphatidylcholine (LysoPC) (16∶0/0∶0), LysoPC[18∶1(9z)/0∶0], taurine and D-glutamic acid was defined. The panel performed a high area under the receiver operating characteristic curve (0.998, 95% CI: 0.994-0.999, P<0.05). Conclusions:There were obvious lipid and amino acid metabolism disorders in OSCC patients. It was an effective method to establish a diagnostic model by metabolomics.