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Sphingolipid metabolism (SM) fuels tumorigenesis and the malignant progression of osteosarcoma (OS), which leads to an unfavorable prognosis. Elucidating the molecular mechanisms underlying SM in osteosarcoma and developing a SM-based prognostic signature could be beneficial in the clinical setting. This study included 88 frozen OS samples to recognize the vital SM-relevant genes in the development of OS utilizing univariate Cox regression. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was conducted on the SM- relevant genes to minimize the risk of overfitting. The prognostic signature was generate utilizing the multivariable Cox regression analysis and was verified in the validation cohort. Moreover, cellular and molecular mechanisms associated with SM have an unfavorable prognosis for OS patients and have been widely studied. Resultantly, an SM-based prognostic risk model was established according to critical prognostic genes (CBS, GLB1, and HACD1), which had an excellent ability to predict the prognosis of OS patients (AUC for the train cohort was 0.887 and AUC for validation cohort was 0.737). The high-risk OS patients identified based on this prognostic signature had significantly poor immune microenvironment, indicated by significantly low immune score (mean=216.290 ± 662.463), reduced infiltrations of 25 immune cells, including NK cells (LogFC= -0.3597), CD8+T cells ((LogFC=-0.2346), Cytolytic activity ((LogFC=-0.1998), etc. The immunosuppressive microenvironment could be due to dysregulated SM of glycolipids. Further, a nomogram was constructed by integrating the SM-based prognostic signature and clinical paraments to facilitate clinical application. The nomogram could accurately predict the prognosis of OS invalids. Collectively, this study clarified the function of SM in the development of OS and helped develop a tool for risk stratification based on SM-related genes with application in clinical settings. The results of our study will aid in identifying high-risk patients and provide individualized treatments.

Background Knee osteoarthritis is a common joint degenerative disease. Xiao Huoluo Pills (XHLP) has been used to treat degenerative diseases such as osteoarthritis and hyperosteogeny. However, XHLP’s specific effective ingredients and mechanism of action against osteoarthritis have not been explored. Therefore, bioinformatics technology and molecular docking technology are employed in this study to explore the molecular basis and mechanism of XHLP in the treatment of knee osteoarthritis. Methods Public databases (TCMSP, Batman-TCM, HERB, DrugBank, and UniProt) are used to find the effective active components and corresponding target proteins of XHLP (screening conditions: OB > 30%, DL ≥ 0.18). Differentially expressed genes related to cartilage lesions of knee osteoarthritis are obtained based on the GEO database (screening conditions: adjust P value < 0.01, |log2 FC|≥1.0). The Venn package in R language and the BisoGenet plug-in in Cytoscape are adopted to predict the potential molecules of XHLP in the treatment of knee osteoarthritis. The XHLP-active component-target interaction network and the XHLP-knee osteoarthritis-target protein core network are constructed using Cytoscape software. Besides, GO/KEGG enrichment analysis on core genes is performed using the Bioconductor package and clusterProfiler package in the R language to explain the biological functions and signal pathways of the core proteins. Finally, molecular docking is performed through software such as Vina, LeDock, Discovery Studio 2016, PyMOL, AutoDockTools 1.5.6, so as to verify the binding ability between the active components of the drug and the core target protein. Results XHLP has been screened out of 71 potentially effective active compounds for the treatment of OA, mainly including quercetin, Stigmasterol, beta-sitosterol, Izoteolin, and ellagic acid. Knee osteoarthritis cartilage lesion sequencing data (GSE114007) was screened out of 1672 differentially expressed genes, including 913 upregulated genes and 759 downregulated genes, displayed as heat maps and volcano maps. Besides, 33 core target proteins are calculated by Venn data package in R and BisoGenet plug-in in Cytoscape. The enrichment analysis on these target genes revealed that the core target genes are mainly involved in biological processes such as response to oxygen levels, mechanical stimulus, vitamin, drug, and regulation of smooth muscle cell proliferation. These core target genes are involved in signaling pathways related to cartilage degeneration of knee osteoarthritis such as TNF signaling pathway and PI3K-Akt signaling pathway. Finally, the molecular docking verification demonstrates that some active components of the drug have good molecular docking and binding ability with the core target protein, further confirming that XHLP has the effect of inhibiting cartilage degeneration in knee osteoarthritis. Conclusions In this study, based on the research foundation of bioinformatics and molecular docking technology, the active components and core target molecules of XHLP for the treatment of cartilage degeneration of knee osteoarthritis are screened out, and the potential mechanism of XHLP inhibiting cartilage degeneration of knee osteoarthritis is deeply explored. The results provide theoretical basis and new treatment plan for XHLP in the treatment of knee osteoarthritis.

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<b><i>Introduction:</i></b> Ischemic stroke (IS) is an extremely complex disease caused by the combined action of multiple environmental and genetic factors. <i>CYP1B1</i> is a member of the cytochrome P450 protein family, and it is an important human drug-metabolizing enzymes. We aimed to explore the association between <i>CYP1B1</i> genetic variants and IS risk in Chinese Han population. <b><i>Methods:</i></b> We recruited 1,150 participants to conduct a “case-control” study. The assessment of association between candidate <i>CYP1B1</i> genetic variants (rs2855658, rs10916, rs162560, rs2567206) and IS risk was performed by SNPStats online software. In addition, false-positive report probability analysis was used to detect whether the positive findings were just chance or noteworthy observations. Finally, the interaction of candidate SNPs in IS risk was evaluated by multifactor dimensionality reduction. <b><i>Results:</i></b> The results showed that <i>CYP1B1</i>-rs2855658 was a risk factor for IS among ≥60-year-old (dominant: <i>p</i> = 0.034; overdominant: <i>p</i> = 0.026), smoking (heterozygote: <i>p</i> = 0.009; dominant: <i>p</i> = 0.004; overdominant: <i>p</i> = 0.012; log-additive: <i>p</i> = 0.003), and drinking participants (homozygous: <i>p</i> = 0.036; dominant: <i>p</i> = 0.019; recessive: <i>p</i> = 0.012; log-additive: <i>p</i> = 0.006). <i>CYP1B1</i>-rs10916 also was a risk factor for IS patients among ≥60-year-old (heterozygote: <i>p</i> = 0.047; overdominant: <i>p</i> = 0.048), smoking (dominant: <i>p</i> = 0.050; overdominant: <i>p</i> = 0.049), and drinking participants (dominant: <i>p</i> = 0.019; overdominant: <i>p</i> = 0.038; log-additive: <i>p</i> = 0.013). <b><i>Conclusion:</i></b> <i>CYP1B1</i>-rs10916 and <i>CYP1B1</i>-rs2855658 can increase the IS risk in Chinese Han population who are ≥60 years old, smoking, or drinking alcohol.