[Progress in the application of artificial intelligence in gastric cancer imaging].

Gastric cancer is a common malignant tumor of digestive tract, and its prognosis varies greatly with different stages of the tumor. In recent years, more and more evidence shows that artificial intelligence (AI) technology has excellent performance in imaging diagnostic applications, with remarkable diagnostic effects and broad prospects. AI not only improves the accuracy of diagnosis and staging of gastric cancer, but also has great application value in pathological assessment, adjuvant therapy and prognosis prediction. This article systematically reviews domestic and foreign literature to explore the application progress of AI in gastric cancer imaging.

Differentially expressed proteins identified by TMT proteomics analysis in children with verrucous epidermal naevi.

BACKGROUND: Verrucous epidermal naevi (VEN) are benign skin tumours, considered keratinocytic epidermal naevi, that appear at birth or early childhood. VEN may display a range of appearances, depending on patient age. Although the number of studies regarding VEN is increasing, the exact mechanism of VEN is still unknown. OBJECTIVES: The aim of this study was to analyse the changes in the expression of protein factors in lesions of VEN children by TMT labelling-based quantitative proteomics. METHODS: A total of 8 children with VEN (5 for experiment and 3 for validation) and 8 healthy children (5 for experiment and 3 for validation) presented to the Department of Dermatology, the First Affiliated Hospital of Anhui Medical University, Boao Super Hospital, between January 2019 and November 2019. The lesions and lesion-adjacent tissues from children with VEN and naevus-adjacent normal skin tissues from children with pigmented naevi were defined as the VEN group, VENC group and C group, respectively. We performed a proteomics analysis to screen for differentially expressed proteins in the lesions of these individuals. We further performed Western blotting to validate the relative expression levels of nine targeted proteins in the validation group. RESULTS: According to the proteomics results, a total of 4970 proteins were identified, and 4770 proteins were quantified. Among these proteins, 586 proteins were up- or downregulated at least 1.3-fold with a P-value < 0.05 (upregulated: 399, downregulated: 187) in lesions between the VEN group and the C group. These proteins played important roles in multiple biological functions, such as cornification, epidermal cell differentiation and neutrophil activation, and formed a complicated protein-protein interaction network. Of the 586 up- or downregulated proteins, nine were selected for further validation. According to Western blotting analysis results, the relative expression levels of Involucrin, NDUFA4, Loricrin, Keratin type II cytoskeletal 6A (Cytokeratin 6A), BRAF, Filaggrin, S100A7 and Desmocollin-3 were significantly upregulated in VEN children and may be associated with skin barrier dysfunction, epidermal cell overgrowth and differentiation, inflammation and immune and oxidative phosphorylation, which are involved in the pathogenesis of VEN. CONCLUSIONS: According to TMT-based proteomics and Western blotting results, we identified eight noteworthy proteins, Involucrin, NDUFA4, Loricrin, Keratin type II cytoskeletal 6A, BRAF, Filaggrin, S100A7 and Desmocollin-3, that were upregulated in the lesions of VEN children and may be associated with the pathogenesis of VEN. Our findings provide new starting points for identifying precise pathogenic mechanisms or therapeutic targets for VEN.