ABSTRACT
BACKGROUND: Large to giant congenital melanocytic nevi (LGCMN) significantly decrease patients' quality of life, but the inaccuracy of current classification system makes their clinical management challenging. OBJECTIVES: To improve and extend the existing LGCMN 6B/7B classification systems by developing a novel LGCMN classification system based on a new phenotypic approach to clinical tool development. METHODS: Three hundred and sixty-one LGCMN cases were categorized into four subtypes based on anatomic site: bonce (25.48%), extremity (17.73%), shawl (19.67%) and trunks (37.12%) LGCMN. A 'BEST' classification system of LGCMN was established and validated by a support vector machine classifier combined with the 7B system. RESULTS: The most common LGCMN distributions were on bonce and trunks (bathing trunk), whereas breast/belly and body LGCMN were exceptionally rare. Sexual dimorphism characterized distribution, with females showing a wider range of lesions in the genital area. Nearly half of the patients with bathing trunk LGCMN exhibited a butterfly-like distribution. Approximately half of the LGCMN with chest involvement did not have nipple-areola complex involvement. Abdomen, back and buttock involvement was associated with the presence of satellite nevi (r = 0.558), and back and buttock involvement was associated with the presence of nodules (r = 0.364). CONCLUSIONS: The effective quantification of a standardized anatomical site provides data support for the accuracy of the 6B/7B classification systems. The simplified BEST classification system can help establish a LGCMN clinical database for exploration of LGCMN aetiology, disease management and prognosis prediction.
ABSTRACT
Giant congenital melanocytic nevi (GCMN) is a congenital cutaneous developmental deformity tumor that usually occurs at birth or in the first few weeks after birth, but its pathogenesis is still unclear. In this study, nuclear magnetic resonance-based metabolomics strategy was employed to evaluate the metabolic variations in serum and urine of the GCMN patients in order to understand its underlying biochemical mechanism and provide a potential intervention idea. Twenty-nine metabolites were observed to change significantly in serum and urine metabolomes, which are mainly involved in a variety of metabolic pathways including glyoxylate and dicarboxylate metabolism, TCA cycle and metabolisms of amino acids. The substantial cores of all the disturbed metabolic pathways are related to amino acid metabolism and carbohydrate metabolism and regulate the physiological state of the GCMN patients. Our results provide the physiological basis and physiological responses of GCMN and will be helpful for better understanding the molecular mechanisms of GCMN in future research.
