Zhaoxiang Li

Tumor angiogenesis is critical for tumor metastasis by providing oxygen, nutrients, and metastatic pathways. As a potential anti‐angiogenic agent, Dihydroartemisinin (DHA) can effectively inhibit tumor metastasis. However, the mechanism how it regulates angiogenesis to affect tumor metastasis has not been fully clarified. To investigate the mechanisms of how DHA regulates melanoma progression. In this study, bioinformatics methods were used to analyze the correlation between angiogenesis and melanoma metastasis. Then, B16F10, A375, HUVECs and mouse metastasis models were adapted to clarify the inhibition of DHA in melanoma. GESA analysis revealed melanoma metastasis significantly positive correlated with angiogenesis. Meanwhile, DHA significantly decreased melanoma nodules and lung wet weight in metastatic tumor mice, and inhibited the expression of the angiogenic marker CD31 in vitro and in vivo. Similarly, DHA inhibited the expression of the angiogenic signal molecule VEGFR2 in A375 and B16F10 cells, and significantly suppressed the formation of their tubular structures. DHA‐treated supernatants significantly inhibited the tubule‐forming ability as well as lateral and longitudinal migration ability of HUVECs compared with untreated melanoma cell supernatants. Screening yielded the angiogenic pathways HIF‐1α/VEGF, PI3K/ATK/mTOR associated with melanoma metastasis, and DHA may inhibit tumor metastasis by inhibiting these angiogenic pathways in melanoma cells to inhibit tumor metastasis. Further non‐targeted metabolomics analysis revealed that DHA‐treated model mice produced differential metabolites that were also associated with angiogenic pathways. DHA inhibits melanoma invasion and metastasis by mediating angiogenesis. These results have important implications for the potential use of DHA in treatment of melanoma.

As a metastasis-prone malignancy, the metastatic form and location of melanoma seriously affect its prognosis. Although effective surgical methods and targeted drugs are available to enable the treatment of carcinoma in situ, for metastatic tumors, the diagnosis, prognosis assessment and development of immunotherapy are still pending. This study aims to integrate multiple bioinformatics approaches to identify immune-related molecular targets viable for the treatment and prognostic assessment of metastatic melanoma, thus providing new strategies for its use as an immunotherapy. Immunoinfiltration analysis revealed that M1-type macrophages have significant infiltration differences in melanoma development and metastasis. In total, 349 genes differentially expressed in M1-type macrophages and M2-type macrophages were extracted from the MSigDB database. Then we derived an intersection of these genes and 1111 melanoma metastasis-related genes from the GEO database, and 31 intersected genes identified as melanoma macrophage immunomarkers (MMIMs) were obtained. Based on MMIMs, a risk model was constructed using the Lasso algorithm and regression analysis, which contained 10 genes (NMI, SNTB2, SLC1A4, PDE4B, CLEC2B, IFI27, COL1A2, MAF, LAMP3 and CCDC69). Patients with high+ risk scores calculated via the model have low levels of infiltration by CD8⁺ T cells and macrophages, which implies a poor prognosis for patients with metastatic cancer. DCA decision and nomogram curves verify the high sensitivity and specificity of this model for metastatic cancer patients. In addition, 28 miRNAs, 90 transcription factors and 29 potential drugs were predicted by targeting the 10 MMIMs derived from this model. Overall, we developed and validated immune-related prognostic models, which accurately reflected the prognostic and immune infiltration characteristics of patients with melanoma metastasis. The 10 MMIMs may also be prospective targets for immunotherapy.

Psoriasis is a chronic, prolonged, and recurrent inflammatory skin disease and the current therapeutics can only alleviate the symptoms rather than cure it completely. Therefore, we aimed to identify the molecular signatures and specific biomarkers of psoriasis to provide novel clues for psoriasis and targeted therapy. In the present study, the Gene Expression Omnibus (GEO) database was used to retrieve three microarray datasets (GSE166388, GSE50790 and GSE42632) and to explore the differentially expressed genes (DEGs) in psoriasis using the Affy package in R software. The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment were utilized to determine the common DEGs and their capabilities. The STRING database was used to develop DEG-encoded proteins and a protein–protein interaction network (PPI) and the Cytohubba plugin to classify hub genes. Using the NetworkAnalyst platform, we detected transcription factors (TFs), microRNAs and drug candidates interacting with hub genes. In addition, the expression levels of hub genes in HaCaT cells were detected by western blot. We screened the up- and downregulated DEGs from the transcriptome microarrays of corresponding psoriasis patients. Functional enrichment of DEGs in psoriasis was mainly associated with positive regulation of leukocyte cell–cell adhesion and T cell activation, cytokine binding, cytokine activity and the Wnt signaling pathway. Through further data processing, we obtained 57 intersecting genes in the three datasets and probed them in STRING to determine the interaction of their expressed proteins and we obtained the critical 10 hub genes in the Cytohubba plugin, including TOP2A, CDKN3, MCM10, PBK, HMMR, CEP55, ASPM, KIAA0101, ESC02, and IL-1β. Using these hub genes as targets, we obtained 35 TFs and 213 miRNAs that may regulate these genes and 33 potential therapeutic agents for psoriasis. Furthermore, the expression levels of TOP2A, MCM10, PBK, ASPM, KIAA0101 and IL-1β were observably increased in HaCaT cells. In conclusion, we identified potential biomarkers, risk factors and drugs for psoriasis.