This work aimed to investigate tumor-infiltrating immune cells (TIICs) and immune-associated genes in the tumor microenvironment of osteosarcoma. An algorithm known as ESTIMATE was applied for immune score assessment, and osteosarcoma cases were assigned to the high and low immune score groups. Immuneassociated genes between these groups were compared, and an optimal immune-related risk model was built by Cox regression analyses. The deconvolution algorithm (referred to as CIBERSORT) was applied to assess 22 TIICs for their amounts in the osteosarcoma microenvironment. Osteosarcoma cases with high immune score had significantly improved outcome (P<0.01). The proportions of naive B cells and M0 macrophages were significantly lower in high immune score tissues compared with the low immune score group (P<0.05), while the amounts of M1 macrophages, M2 macrophages, and resting dendritic cells were significantly higher (P<0.05). Important immune-associated genes were determined to generate a prognostic model by Cox regression analysis. Interestingly, cases with high risk score had poor outcome (P<0.01). The areas under the curve (AUC) for the risk model in predicting 1, 3 and 5-year survival were 0.634, 0.781, and 0.809, respectively. Gene set enrichment analysis suggested immunosuppression in high-risk osteosarcoma patients, in association with poor outcome.

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The Notch signaling pathway, known to be a highly conserved signaling pathway in embryonic development and adult tissue homeostasis, participates in cell fate decisions that include cellular differentiation, cell survival and cell death. However, other studies have shown that aberrant in Notch signaling is pro-tumorigenic, particularly in hepatocellular carcinoma (HCC). HCC is one of the most common malignant tumors in the world and has a high mortality rate. Growing evidence supports that Notch signaling plays a critical role in the development of HCC by regulating the tumor microenvironment, tumorigenesis, progression, angiogenesis, invasion and metastasis. Accordingly, overexpression of Notch is closely associated with poor prognosis in HCC. In this review, we focus on the pro-tumorigenic role of Notch signaling in HCC, summarize the current knowledge of Notch signaling and its role in HCC development, and outline the therapeutic potential of targeting Notch signaling in HCC.

The mechanisms underlying the therapeutic effect of Salvia miltiorrhiza (SM) on diabetic nephropathy (DN) were examined using a systematic network pharmacology approach and molecular docking. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to screen active ingredients of SM. Targets were obtained using the SwissTargetPrediction and TCMSP databases. Proteins related to DN were retrieved from the GeneCards and DisGeNET databases. A protein–protein interaction (PPI) network was constructed using common SM/DN targets in the STRING database. The Metascape platform was used for GO function analysis, and the Cytoscape plug-in ClueGO was used for KEGG pathway enrichment analysis. Molecular docking was performed using iGEMDOCK and AutoDock Vina software. Pymol and LigPlos were used for network mapping. Sixty-six active ingredients and 189 targets of SM were found. Sixty-four targets overlapped with DN-related proteins. The PPI network revealed that AKT1, VEGFA, IL6, TNF, MAPK1, TP53, EGFR, STAT3, MAPK14, and JUN were the 10 most relevant targets. Go and KEGG analyses revealed that the common targets of DN and SM were mainly involved in advanced glycation end products, oxidative stress, inflammatory response, and immune regulation. Molecular docking revealed that potential DN-related targets, includingTNF, NOS2, and AKT1, more stably bound with salvianolic acid B than with tanshinone IIA. In conclusion, this study revealed the active components and potential molecular therapeutic mechanisms of SM in DN and provides a reference for the wide application of SM in clinically managing DN.