High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that MYCN amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of MYC/MYCN targets in this subtype. In summary, prognostic subtypes identified by deep learning-based multi-omics integration could not only improve our understanding of molecular mechanism, but also help the clinicians make decisions.

Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression–methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer. To this end, we constructed a user-friendly database named DNA Methylation Interactive Visualization Database (DNMIVD), which comprehensively provides the following important resources: (i) diagnostic and prognostic models based on DNA methylation for multiple cancer types of The Cancer Genome Atlas (TCGA); (ii) meQTL, emQTL and pathway-meQTL for diverse cancers; (iii) Functional Epigenetic Modules (FEM) constructed from Protein-Protein Interactions (PPI) and Co-Occurrence and Mutual Exclusive (COME) network by integrating DNA methylation and gene expression data of TCGA cancers; (iv) differentially variable and differentially methylated CpGs and differentially methylated genes as well as related enhancer information; (v) correlations between methylation of gene promoter and corresponding gene expression and (vi) patient survival-associated CpGs and genes with different endpoints. DNMIVD is freely available at http://www.unimd.org/dnmivd/. We believe that DNMIVD can facilitate research of diverse cancers.

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Wound healing is a common occurrence. However, delayed healing and aberrant scarring result in pathological wound healing. Accordingly, a scarless wound healing remains a significant clinical challenge. In this study, we constructed hyaluronic acid (HA)-modified and verteporfin (VP)-loaded polylactic acid (PLA) nanogels (HA/VP-PLA) to promote scarless wound healing by accelerating wound re-epithelialization and controlling scar formation. Owing to the unique structure of HA incorporating and coating in VP-loaded PLA nanoparticles, HA/VP-PLA could be topically applied on wound to achieve targeted delivery to fibroblasts. Then, HA/VP-PLA released HA and lactic acid (LA) to stimulate the proliferation and migration of fibroblasts, as well as VP to inhibit Yes-associated protein (YAP) expression and nuclear localization to suppress fibrosis. In vitro (skin fibroblasts) and in vivo (rat and rabbit models) experiments strongly suggested that HA/VP-PLA promoted scarless wound healing by accelerating wound re-epithelialization and controlling scar formation. Therefore, our work provides a feasible strategy for scarless wound healing, and the sophisticated HA/VP-PLA exhibit a great potential for clinical applications.

Neuroblastoma (NB) is the most common malignant tumor originating from the extracranial sympathetic nervous system in children. The molecular mechanisms underlying this disease are complex, and not completely understood. Quantitative real-time PCR (qRT-PCR) was applied to quantify the expression of miR-20a-5p and its target gene ATG7 in clinical NB tissues. The biological function of miR-20a-5p and ATG7 in SH-SY5Y cells was investigated through in vitro studies (Real-Time cell kinetic analyzer, colony formation assay, caspase-Glo 3/7 assay and western blotting). The luciferase reporter assay was conducted to verify the biological relationship between miR-20a-5p and ATG7. Here we found that miR-20a-5p expression was significantly downregulated whereas its target autophagy-related gene 7 (ATG7) was increased along with clinical staging of NB progression. Correlation analysis showed that miR-20a-5p had a negative correlation trend with ATG7. In SH-SY5Y cells, forced expression of miR-20a-5p suppressed ATG7 expression, autophagy initiation and cellular proliferation while promoted apoptosis, suggesting a potential association between miR-20a-5p and ATG7. Further bioinformatic target prediction combined with protein expression and luciferase reporter assay verified that miR-20a-5p inhibited ATG7 by directly binding to its 3′-UTR, confirming the involvement of miR-20a-5p in the regulation of ATG7 in NB. These results clarified that miR-20a-5p inhibited cell proliferation and promoted apoptosis through negative regulation of ATG7 and thus autophagy suppression in SH-SY5Y cells. Therefore, defining the context-specific roles of autophagy in NB and regulatory mechanisms involved will be critical for developing autophagy-targeted therapeutics against NB. Both miR-20a-5p and ATG7 would be potential therapeutic targets for future NB treatment.