[Identification of diagnostic biomarkers for metastatic lymph nodes in oral squamous cell carcinoma using spatial metabolomics].

Objective: To uncover alterations in the metabolic microenvironment of lymph node metasasis, LNM in oral squamous cell carcinoma (OSCC) and identify potential metabolic biomarkers for the early diagnosis of LNM using desorption electrospray ionization mass spectrometry imaging (DESI-MSI) spatial metabolomics. Methods: Six OSCC patients with LNM, who underwent neck dissection surgery at the Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University between October 2020 and October 2022, were enrolled. Matched metastatically involved (positive) and benign (negative) lymph node tissue samples were collected and analyzed using DESI-MSI. Univariate and multivariate statistical analyses were employed to identify differentially abundant metabolites. The diagnostic efficacy of these metabolites was evaluated using receiver operating characteristic (ROC) curve analysis. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed to determine the implicated metabolic pathways. Results: A total of 62 and 29 differentially abundant metabolites were identified in the metastatically involved lymph nodes compared to benign lymph nodes under positive-ion mode and negative-ion mode, respectively. These metabolites were predominantly amino acids and lipids. Four metabolites common to both ionization modes were selected for ROC curve analysis: phenylalanine, phosphoethanolamine, histidine, and taurine. The area under the curve values were 0.861, 0.802, 0.729, and 0.722, respectively, indicating promising diagnostic performance. Metabolic pathway analysis revealed significantly heightened activity in Amino acid metabolism (P=0.469) and Glycerophospholipid metabolism (P=0.006) within the LNM microenvironment. Conclusions: This DESI-MSI-based study identified disruptions in amino acid and glycerophospholipid metabolism within OSCC metastatic lymph node tissues. The associated differentially abundant metabolites represent potential candidate molecules for diagnosing OSCC LNM.