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Introduction Melanoma is a highly aggressive malignant skin tumor as well as the primary reason for skin cancer‐specific deaths. We first identified immune‐related long noncoding RNA (lncRNA) prognostic signature and found potential immunotherapeutic targets for melanoma cancer. Methods RNA‐seq data and clinical features of melanoma samples were obtained from The Cancer Genome Atlas. Samples of melanoma were randomly assigned to the training and testing cohort. The immune‐related lncRNA signature was then obtained via using univariate, LASSO, and multivariate Cox analysis of patients in the training cohort. Eight significant immune‐related lncRNA signature was then subsequently obtained through correlation analysis between immune‐related genes and lncRNAs. The association between risk score and immune cell infiltration was finally assessed using TIMER and CIBERSORT. Results Three hundred and fifty‐six immune‐related lncRNAs were obtained. Among them, eight immune‐related lncRNAs were identified to build a prognostic risk signature model. The model's performance was then confirmed using the Kaplan–Meier curves, risk plots, and time‐dependent receiver‐operating characteristic curves in the training cohort. The risk score was identified and confirmed as an independent prognostic factor through univariate and multivariate Cox regression analyses. These results were further verified in the testing and whole cohorts. CIBERSORT algorithm showed that the infiltration levels of T cells CD8, M1 macrophages, plasma cells, T cells CD4 memory activated, T cells gamma delta, and mast cells activated were significantly lower in the high‐risk group while the infiltration level of macrophages M0 was significantly lower in the low‐risk group. Conclusion The immune‐related lncRNA signature offers prognostic markers and potential immunotherapeutic targets for melanoma., We applied univariate Cox, LASSO, and multivariate Cox regression analysis to identify immune‐related lncRNAs with remarkable prognostic potential and constructed a multiple immune‐related lncRNAs signature to predict melanoma' prognosis. lncRNA signatures of immune‐related offers prognostic markers and potential immunotherapeutic targets for melanoma.

Magnesium (Mg) and its alloys as a type of different biodegradable materials have been used in the musculoskeletal field because of their excellent biocompatibility, biodegradability and mechanical properties similar to bone; besides, Mg could promote osteoblast differentiation in vitro and induce the formation of new bone in vivo. In the present study, we prepared the extracts of Mg–Zn–Mn alloy and examined their effects on the angiogenesis of human umbilical vein endothelial cells (HUVECs). In the present study, we prepared Mg–Zn–Mn alloy extracts of different concentrations and cultured HUVECs with these extracts. The DNA synthesis capacity, the cell viability, and the tube formation capacity of HUVECs could be significantly induced by 6.25% Mg alloy extract. In the meantime, the ratios of p-FGFR/FGFR, p-PI3K/PI3K, and p-AKT/AKT were significantly increased by 6.25% Mg alloy extract treatment, while decreased by FGFR/FGFR signaling pathway inhibitor BFJ398, indicating that 6.25% Mg alloy extract could promote the angiogenesis of HUVECs via activating FGF/FGFR signaling pathway. In conclusion, these data indicate that 6.25% Mg–Zn–Mn alloy extract induces the angiogenesis of HUVECs via FGF signaling pathway. Further in vivo experiments are needed to further confirm the present in vitro findings.

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

Background: Synovial sarcoma is an uncommon soft sarcoma that lacks prognostic prediction models. The nomograms were employed to predict patients’ survival of synovial sarcoma. Methods: Materials collected from 1941 synovial sarcoma cases in the SEER database were analyzed. We employed univariate and multivariate cox analyses to identify the independent prognostic variables. Based on these outcomes, the nomograms were built for predicting 1-, 3-, and 5- year overall survival rate and disease-specific survival rate and then validated in external dataset. C-indices, calibration plots and ROC curves were applied to assess nomogram accuracy. Results: Patients were randomly classified into the training (n=1361) and testing (n=580) cohorts. Age, race, sex, primary anatomic site, chemotherapy, subtypes, surgery, SEER historic stage and tumor size were identified as independent prognostic variables (PConclusion: we constructed the reliable nomograms for synovial sarcoma patients to predict overall and disease-specific survival, which can offer precise and personalized survival prediction.

Osteosarcoma is the most common malignant bone tumor in children and adolescence. Multiple immune-related genes have been reported in different cancers. The aim is to identify an immune-related gene signature for the prospective evaluation of prognosis for osteosarcoma patients. In this study, we evaluated the infiltration of immune cells in 101 osteosarcoma patients downloaded from TARGET using the ssGSEA to the RNA-sequencing of these patients, thus, high immune cell infiltration cluster, middle immune cell infiltration cluster and low immune cell infiltration cluster were generated. On the foundation of high immune cell infiltration cluster vs. low immune cell infiltration cluster and normal vs. osteosarcoma, we found 108 common differentially expressed genes which were sequentially submitted to univariate Cox and LASSO regression analysis. Furthermore, GSEA indicated some pathways with notable enrichment in the high- and low-immune cell infiltration cluster that may be helpful in understanding the potential mechanisms. Finally, we identified seven immune-related genes as prognostic signature for osteosarcoma. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that the seven immune-related genes signature was an innovative and significant prognostic factor independent of clinical features. These results of this study offer a means to predict the prognosis and survival of osteosarcoma patients with uncovered seven-gene signature as potential biomarkers.