SummaryT helper type 9 (Th9) cells are a novel identified subset of CD4 + T helper cells, which could partly contribute to allergic inflammation, while the precise contribution of Th9 cells in atopic dermatitis (AD) remains unknown. We aimed to explore the possible role of Th9 cells in AD pathogenesis. The Th9 cell percentage, transcription factor PU.1 and cytokine interleukin (IL)-9 mRNA levels, as well as IL-9 serum concentration in peripheral circulation, were measured in AD patients, psoriasis patients and healthy controls. The Th9 cell percentage, PU.1 and IL-9 expression levels of AD patients were all increased significantly compared with the other two control groups (P < 0·01), and correlated positively with SCORing Atopic Dermatitis index, serum immunoglobulin (Ig)E and thymus-and activationregulated chemokine (TARC) levels (P < 0·05). In simple AD patients and AD patients complicated by allergic rhinitis or asthma, there were no significant differences in the Th9 cell percentage, PU.1 and IL-9 expression levels between them. At the same time, IL-9 and vascular endothelial growth factor (VEGF) mRNA levels were detected in AD lesions and normal skin samples, which were both distinctly elevated in AD lesions, and there was a positive association between them (P < 0·01). Keratinocytes were cultured with IL-9 stimulation and the secretion of VEGF was detected. IL-9 can promote the secretion of VEGF by keratinocytes in a time-and dose-dependent manner. In conclusion, the expansion of the Th9 cell subset, up-regulation of the PU.1 transcription factor and increased secretion of the IL-9 cytokine may contribute to the pathogenesis of AD, which may be supported by the increased release of VEGF by keratinocyes after IL-9 stimulation.

In AD, there exists an immune imbalance in Th17 and Treg cells, which may contribute to its pathogenesis and development.

Melanocyte loss in vitiligo vulgaris is believed to be an autoimmune process. Macrophage migration inhibitory factor (MIF) is involved in many autoimmune skin diseases. We determined the possible role of MIF in the pathogenesis of vitiligo vulgaris, and describe the relationship between MIF expressions and disease severity and activity. Serum MIF concentrations and mRNA levels in PBMCs were measured in 44 vitiligo vulgaris patients and 32 normal controls, using ELISA and real-time RT-PCR. Skin biopsies from 15 patients and 6 controls were analyzed by real-time RT-PCR. Values are reported as median (25th-75th percentile). Serum MIF concentrations were significantly increased in patients [35.81 (10.98-43.66) ng/mL] compared to controls [7.69 (6.01-9.03) ng/mL]. MIF mRNA levels were significantly higher in PBMCs from patients [7.17 (3.59-8.87)] than controls [1.67 (1.23-2.42)]. There was also a significant difference in MIF mRNA levels in PBMCs between progressive and stable patients [7.86 (5.85-9.13) vs 4.33 (2.23-8.39)] and in serum MIF concentrations [40.47 (27.71-46.79) vs 26.80 (10.55-36.07) ng/mL]. In addition, the vitiligo area severity index scores of patients correlated positively with changes of both serum MIF concentrations (r = 0.488) and MIF mRNA levels in PBMCs (r = 0.426). MIF mRNA levels were significantly higher in lesional than in normal skin [2.43 (2.13-7.59) vs 1.18 (0.94-1.83)] and in patients in the progressive stage than in the stable stage [7.52 (2.43-8.84) vs 2.13 (1.98-2.64)]. These correlations suggest that MIF participates in the pathogenesis of vitiligo vulgaris and may be useful as an index of disease severity and activity.

Background. Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis. Method. The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis. Results. We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients. Conclusions. We constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients.