Salivary gland mucosa-associated lymphoid tissue lymphoma (SGML) is a subvariety of marginal zone B-cells that occurs outside of mucosal lymph nodes. The onset of SGML is closely related to immunity, chronic infections, and genetic factors, such as lymphoepithelial sialadenitis (LESA) and Sjogren’s syndrome (SS), as well as Helicobacter pylori, hepatitis C virus, Epstein-Barr virus, and human T-lymphocytic virus. The most common site of SGML is the parotid gland, followed by the submandibular gland, small salivary gland, and sublingual gland. SGML is more common in middle-aged and elderly women, and patients often have autoimmune diseases, such as Sjogren’s syndrome or rheumatoid arthritis. SGML can be diagnosed through clinical manifestations, imaging, and histopathology, but histopathological biopsy remains the main method for confirming SGML. Traditional treatment methods include anti-infective therapy and surgery combined with radiation or chemotherapy. In recent years, some new treatment methods, such as Bruton tyrosine kinase (BTK) inhibitors and programmed cell death protein-1 (PD-1) inhibitors, have been effective against recurrent or refractory SGML, but more clinical trial data are needed to support them. At present, the optimal treatment for SGML is not yet clear. Individualized treatment plans should be developed based on the location, staging, clinical characteristics, and overall health status of the patient. SGML progresses slowly and has a relatively good overall prognosis; however, the disease is recurrent, the treatment cycle is long, the recurrence rate is higher than that of other mucosa-associated lymphoid tissue lymphomas, and SGML may also cause other serious complications. Therefore, regular observation and follow-up are very important for its prognosis. This article reviews the etiology, diagnosis, treatment, and prognosis of SGML, with the aim of providing a reference for clinical diagnosis and treatment, and thus improve the survival rate of patients with SGML.

Objective:To uncover alterations in the metabolic microenvironment of lymph node metastasis (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.

Objective:To uncover alterations in the metabolic microenvironment of lymph node metastasis (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.