Introduction: Skin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion. We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones.Methods: A deep neural network was developed with two encoders for extracting information from image data and metadata. A multimodal fusion module with intra-modality self-attention and inter-modality cross-attention was proposed to effectively combine image features and meta features. The model was trained on tested on a public dataset and compared with other state-of-the-art methods using five-fold cross-validation.Results: Including metadata is shown to significantly improve a model's performance. Our model outperformed other metadata fusion methods in terms of accuracy, balanced accuracy and area under the receiver-operating characteristic curve, with an averaged value of 0.768±0.022, 0.775±0.022 and 0.947±0.007.Conclusion: A deep learning model using smartphone collected images and metadata for skin lesion diagnosis was successfully developed. The proposed model showed promising performance and could be a potential tool for skin cancer screening.

Ferroptosis is a novel form of cell death that plays a key role in several diseases, including inflammation and tumours; however, the role of ferroptosis‐related genes in diabetic foot remains unclear. Herein, diabetic foot‐related genes were downloaded from the Gene Expression Omnibus and the ferroptosis database (FerrDb). The least absolute shrinkage and selection operator regression algorithm was used to construct a related risk model, and differentially expressed genes were analysed through immune infiltration. Finally, we identified relevant core genes through a protein–protein interaction network, subsequently verified using immunohistochemistry. Comprehensive analysis showed 198 genes that were differentially expressed during ferroptosis. Based on functional enrichment analysis, these genes were primarily involved in cell response, chemical stimulation, and autophagy. Using the CIBERSORT algorithm, we calculated the immune infiltration of 22 different types of immune cells in diabetic foot and normal tissues. The protein–protein interaction network identified the hub gene TP53, and according to immunohistochemistry, the expression of TP53 was high in diabetic foot tissues but low in normal tissues. Accordingly, we identified the ferroptosis‐related gene TP53 in the diabetic foot, which may play a key role in the pathogenesis of diabetic foot and could be used as a potential biomarker.

Objective: Providing protection against aggregation and guiding hydrophobic precursors through the mitochondria’s intermembrane space, this protein functions as a chaperone-like protein. SLC25A12 is imported by TIMM8 as a result of its interaction with TIMM13. In spite of this, it is still unknown how TIMM13 interacts with skin cutaneous melanoma (SKCM) and tumor-infiltrating lymphocytes (TILs).Methods: Aberrant expression of TIMM13 in SKCM and its clinical outcome was evaluated with the help of multiple databases, including the Xiantao tool (https://www.xiantao.love/), HPA, and UALCAN. TISIDB and Tumor Immune Estimation Resources (TIMER) databases were applied to explore the association between TIMM13 and tumor infiltration immune cells. OS nomogram was constructed, and model performance was examined. Finally, TIMM13 protein expression was validated by immunohistochemistry (IHC).Results: TIMM13 expression was higher in SKCM samples than in peritumor samples. TIMM13 was strongly associated with sample type, subgroup, cancer stage, lymph node stage, and worse survival. Further, upregulation of TIMM13 was significantly associated with immunoregulators, and chemokines, as well as T cells, B cells, monocytes, neutrophils, macrophages, and T-cell regulators. An analysis of bioinformatic data uncovered that TIMM13 expression was strongly associated with PD1 (T-cell exhaustion marker). The nomogram showed good predictive performance based on calibration plot. TIMM13 was highly expressed in melanoma tissue samples than in normal samples.Conclusion: In brief, TIMM13 may be a prognostic biomarker for SKCM. It might modulate the tumor immune microenvironment and lead to a poorer prognosis. In addition, it is necessary to study the targeted therapy of TIMM13.

Background: Utrophin(UTRN), as a tumor suppressor gene, is involved in various cancer progression. The function of UTRN in the melanoma process and the related molecular mechanisms are still unclear. Herein, we studied the function of UTRN in melanoma growth and the relevant molecular mechanisms.Methods: Using the GEO database and UCSC Xena project, we compared the expression of UTRN in non-cancerous and melanoma tissues. Immunohistochemistry (IHC) staining, qRT-PCR and Western Blot (WB) were performed to evaluate UTRN expression in clinical samples. A total of 447 cases with UTRN expression data, patient characteristics and survival data were extracted from TCGA database and analyzed. After stable transduction and single cell cloning, the proliferation ability of A375 human melanoma cells was analyzed by Cell Counting Kit‑8 (CCK) and 5‑ethynyl‑2′‑deoxyuridine (EdU) incorporation assays. GSEA was performed to predict the mechanism by which UTRN regulated melanoma growth. Then WB analysis was used to assess the protein expression levels of pathway signaling in overexpression (EXP) melanoma cells. Epac activator 8-pCPT-2′-O-Me-cAMP was then used to evaluate the proliferation ability by activation of p38 and JNK/c-Jun signaling pathways.Results: Data from GEO and UCSC Xena project indicated that UTRN expression was decreased in melanoma. Experiment on clinical samples further confirmed our finding. TCGA results showed that a reduced expression of UTRN in 447 melanoma samples was associated with advanced clinical characteristics (T stage, Clark level, ulceration), shorter survival time and poorer prognosis. In addition, up-regulated UTRN expression inhibited melanoma cell proliferation when compared to control group. MAPK signaling pathway was presented in both KEGG and BioCarta databases by using GSEA tool. WB results confirmed the down-regulated expression of p38, JNK1 and c-Jun in EXP group when compared to control group. Epac activator 8-pCPT-2′-O-Me-cAMP treatment could partially rescue proliferation of tumor cells.Conclusion: We have demonstrated that reduced UTRN predicted poorer prognosis and UTRN inhibited melanoma growth via p38 and JNK1/c-Jun pathways. Therefore, UTRN could serve as a tumor suppressor and novel prognostic biomarker for melanoma patients.