Peeking inside the sphingolipid network in lung cancer

Prediction of immune molecules activity during burn wound healing among elderly patients: in-silico analyses: experimental research

Introduction

Burn injuries lead to dysregulation of immune molecules, impacting cellular and humoral immune pathways. This study aims to determine the prediction of immune molecule activity during burn wound healing among elderly patients.

Methods

The current study utilized the Gene Expression Omnibus (GEO) database to extract the proper gene set. Also, the literature review was conducted in the present study to find immune signatures. The study used the "enrich r" website to identify the biological functions of extracted genes. The critical gene modules related to mortality were identified using the weighted gene co-expression network analysis (WGCNA) R package.

Results

The appreciated GSE was extracted. According to the data, the most upregulated signatures were related to natural killer (NK) cells, and the most downregulated signatures were associated with M1 macrophages. Also, the results of WGCNA have shown that the most related gene modules (P<107 and score 0.17) to mortality were investigated, and the modules 100 first genes were extracted. Additionally, the enrich r analysis has demonstrated related pathways, including the immune process, including regulation of histamine secreted from mast cell (P<0.05), T helper 17 cell differentiation (P<0.05), and autophagy (P<0.05) were obtained. Finally, by network analysis, the critical gene "B3GNT5" were obtained (degree>ten and "betweenness and centrality">30 were considered).

Conclusion

The study identified significant changes in macrophage and NK cell expression patterns post-burn injury, linking them to potential improvements in clinical outcomes and wound healing. The gene B3GNT5, associated with mortality, was highlighted as a key marker for prognostic evaluation.

Drug Targeting of Acyltransferases in the Triacylglyceride and 1-O-AcylCeramide Biosynthetic Pathways

Acyltransferase enzymes (EC 2.3.) are a large group of enzymes that transfer acyl groups to a variety of substrates. This review focuses on fatty acyltransferases involved in the biosynthetic pathways of glycerolipids and sphingolipids and how these enzymes have been pharmacologically targeted in their biologic context. Glycerolipids and sphingolipids, commonly treated independently in their regulation and biologic functions, are put together to emphasize the parallelism in their metabolism and bioactive roles. Furthermore, a newly considered signaling molecule, 1-O-acylceramide, resulting from the acylation of ceramide by DGAT2 enzyme, is discussed. Finally, the implications of DGAT2 as a putative ceramide acyltransferase (CAT) enzyme, with a putative dual role in TAG and 1-O-acylceramide generation, are explored. SIGNIFICANCE STATEMENT: This manuscript reviews the current status of drug development in lipid acyltransferases. These are current targets in metabolic syndrome and other diseases, including cancer. A novel function for a member in this group of lipids has been recently reported in cancer cells. The responsible enzyme and biological implications of this added member are discussed.

The Landscape of Lipid Metabolism in Lung Cancer: The Role of Structural Profiling

The aim of this study was to explore the relationship between lipids with different structural features and lung cancer (LC) risk and identify prospective biomarkers of LC. Univariate and multivariate analysis methods were used to screen for differential lipids, and two machine learning methods were used to define combined lipid biomarkers. A lipid score (LS) based on lipid biomarkers was calculated, and a mediation analysis was performed. A total of 605 lipid species spanning 20 individual lipid classes were identified in the plasma lipidome. Higher carbon atoms with dihydroceramide (DCER), phosphatidylethanolamine (PE), and phosphoinositols (PI) presented a significant negative correlation with LC. Point estimates revealed the inverse associated with LC for the n-3 PUFA score. Ten lipids were identified as markers with an area under the curve (AUC) value of 0.947 (95%, CI: 0.879-0.989). In this study, we summarized the potential relationship between lipid molecules with different structural features and LC risk, identified a panel of LC biomarkers, and demonstrated that the n-3 PUFA of the acyl chain of lipids was a protective factor for LC.