Objective: Obstructive sleep apnea (OSA) is a disorder characterized by disruption in breathing and hypoventilation. In parallel, metabolic syndrome (MetS) mainly co-occur with OSA, however, their association has not been fully elucidated. Therefore, this study aimed to reveal the relationship between OSA and MetS using data from the National Health And Nutrition Examination Survey (NHANES) database and pooled data from Genome-Wide Association Studies (GWAS). Material and Methods: Data from the National Health and Nutrition Examination Survey and pooled data from genome-wide association analysis (GWAS) were used univariate and multivariate logistic regression analyses were carried out to evaluate the correlation between OSA and MetS, and multivariate logistic regression models were utilized for adjusting for potential confounders. Two-sample Mendelian randomization (MR) was used to assess the causal relationship between OSA and MetS. The variance-weighted inverse method was employed as the main method of analysis. Results: A positive relationship of OSA with Mets was evidenced by multivariate logistic regression analysis, and OSA was associated with higher incidence rates of all-cause and cardiovascular mortality. OSA is strongly associated with abdominal obesity, hypertension, hyperglycemia, high triglycerides, and low HDL. Furthermore, except for hypertriglyceridemia, MR analysis indicated that genetically driven OSA was causally associated with a higher risk of MetS. Conclusion: The positive relationship of OSA with Mets was revealed, and higher incidence rates of all-cause mortality and cardiovascular mortality were noted to be correlated with OSA. MR analysis further confirmed the causal relationship of OSA with MetS and cardiovascular disease.

Apolipoprotein M (ApoM) is a type of apolipoprotein. It is well known that high‑density lipoprotein (HDL) decreases inflammatory responses via the apoM‑sphingosine‑1‑phosphate (S1P) pathway. The present study further investigated the importance of ApoM in the inhibitory effects of HDL on inflammation. Mice with an apoM gene deficiency (apoM‑/‑) were employed to investigate the effects of ApoM on the expression of interleukin‑1β (IL‑1β), monocyte chemotactic protein‑1 (MCP‑1), S1P receptor‑1 (S1PR1) and 3β‑hydroxysterol Δ‑24‑reductase (DHCR24), as compared with in wild‑type mice (apoM+/+). Furthermore, cell culture experiments were performed using a permanent human hybrid endothelial cell line (EA.hy926). Cells were cultured in the presence of recombinant human apoM (rec‑apoM) or were induced to overexpress apoM (apoMTg); subsequently, cells were treated with tumor necrosis factor‑α (TNF‑α), in order to investigate the effects of ApoM on IL‑1β and MCP‑1. The results demonstrated that the mRNA expression levels of IL‑1β and MCP‑1 were significantly higher in the liver following administration of lipopolysaccharide in apoM‑/‑ mice compared with in apoM+/+ mice. In cell culture experiments, when cells were pre‑cultured with rec‑apoM or were engineered to overexpress apoM (apoMTg), they exhibited decreased expression levels of IL‑1β and MCP‑1 following TNF‑α treatment compared with in normal apoM‑expressing cells (apoMTgN). Furthermore, the mRNA expression levels of IL‑1β and MCP‑1 were significantly elevated following addition of the S1PR1 inhibitor W146, but not by the scavenger receptor class B type I inhibitor, block lipid transport‑1 (BLT‑1), in apoMTg cells prior to TNF‑α treatment. Conversely, there were no differences in these inflammatory biomarkers under the same conditions in apoMTgN cells. The mRNA expression levels of DHCR24 were significantly reduced by the addition of BLT‑1 prior to TNF‑α treatment in apoMTg cells; however, there was no difference in the expression of this inflammatory biomarker in apoMTgN cells. In conclusion, ApoM displayed inhibitory effects against the inflammatory response in vivo and in vitro; these effects may be induced via the S1PR1 and DHCR24 pathways.

Aim: Lung adenocarcinoma (LUAD), the most prevalent subtype of non-small cell lung cancer (NSCLC), presents significant clinical challenges due to its high mortality and limited therapeutic options. The molecular heterogeneity and the development of therapeutic resistance further complicate treatment, underscoring the need for a more comprehensive understanding of its cellular and molecular characteristics. This study sought to delineate novel cellular subpopulations and molecular subtypes of LUAD, identify critical biomarkers, and explore potential therapeutic targets to enhance treatment efficacy and patient prognosis. Methods: An integrative multi-omics approach was employed to incorporate single-cell RNA sequencing (scRNA-seq), bulk transcriptomic analysis, and genome-wide association study (GWAS) data from multiple LUAD patient cohorts. Advanced computational approaches, including Bayesian deconvolution and machine learning algorithms, were used to comprehensively characterize the tumor microenvironment, classify LUAD subtypes, and develop a robust prognostic model. Results: Our analysis identified eleven distinct cellular subpopulations within LUAD, with epithelial cells predominating and exhibiting high mutation frequencies in Tumor Protein 53 (TP53) and Titin (TTN) genes. Two molecular subtypes of LUAD [consensus subtype (CS)1 and CS2] were identified, each showing distinct immune landscapes and clinical outcomes. The CS2 subtype, characterized by increased immune cell infiltration, demonstrated a more favorable prognosis and higher sensitivity to immunotherapy. Furthermore, a multi-omics-driven machine learning signature (MOMLS) identified ribonucleotide reductase M1 (RRM1) as a critical biomarker associated with chemotherapy response. Based on this model, several potential therapeutic agents targeting different subtypes were proposed. Conclusion: This study presents a comprehensive multi-omics framework for understanding the molecular complexity of LUAD, providing insights into cellular heterogeneity, molecular subtypes, and potential therapeutic targets. Differential sensitivity to immunotherapy among various cellular subpopulations was identified, paving the way for future immunotherapy-focused research.

Background: Pulmonary arterial hypertension (PAH) is a severe vascular disorder with a multifactorial etiology, including potential genetic predispositions. Understanding the causal relationship between autoimmune diseases and the risk of developing PAH can inform clinical strategies for prevention and treatment. Methods: We conducted a two-sample Mendelian Randomization (MR) analysis to evaluate the causal effect of genetic predisposition to five autoimmune diseases (systemic lupus erythematosus [SLE], rheumatoid arthritis [RA], inflammatory bowel disease [IBD], multiple sclerosis [MS], and type 1 diabetes [T1D]) on the risk of PAH. This involved employing various MR methods (IVW, MR-Egger, Weighted median, Simple mode, and Weighted mode), as well as conducting tests for heterogeneity and horizontal pleiotropy. Results: The analysis revealed a significant association between genetic predisposition to RA and IBD with an increased risk of PAH (RA: OR = 1.28, 95% CI [1.01–1.61], p = 0.042; IBD: OR = 1.29, 95% CI [1.01–1.64], p = 0.043). However, no association was observed between genetically determined MS, SLE, and T1D with the risk of PAH (MS: p = 0.876; SLE: p = 0.564; T1D: p = 0.061). Additionally, tests for heterogeneity and pleiotropy provided no evidence of their influence, suggesting the robustness of these associations. Reverse MR analysis also indicated no significant effect of PAH on the genetic susceptibility to these autoimmune diseases. Conclusion: The findings suggest a possible genetic causative link between RA and IBD and the risk of developing PAH. Conversely, genetic predisposition to MS, SLE, and T1D does not appear to influence PAH risk. Understanding these relationships may offer insights into the pathophysiology of PAH and inform screening strategies within at-risk populations.

Background: The apolipoprotein M (ApoM)-sphingosine-1-phosphate (S1P) axis was recently identified, and research into its function has received increasing attention. However, there are some factors which might influence the results of studies into the function of the ApoM-S1P axis using the EA.hy926 cells. This study investigated related factors, including coagulation factor VIII (FVIII), ApoM, S1P receptor subtypes (S1PRs), C-myc-tagged, and His-tagged proteins in EA.hy926 cells, as well as the effects of ApoM overexpression on S1PRs. Methods: The expression of FVIII, ApoM, S1PRs, C-myc, and His-tagged proteins in EA.hy926 cells was investigated through cellular immunofluorescence. EA.hy926 cells were infected with lentiviruses carrying (OE group) or lacking (NC group) the ApoM gene sequence. A stable cell line expressing ApoM was obtained, and the expression of ApoM mRNA was detected through single tube duplex fluorescence reverse transcription quantitative polymerase chain reaction (RT-qPCR). S1PRs expression was detected by RT-qPCR and Western blotting. Results: The results showed that EA.hy926 cells expressed FVIII, ApoM, C-myc-tagged, and His-tagged proteins. Moreover, they highly expressed S1PR1, slightly expressed S1PR3, weakly expressed S1PR2, and did not express S1PR4 and S1PR5. ApoM overexpression significantly increased S1PR1 mRNA and protein expression but did not affect the expression of S1PR3. EA.hy926 cells expressed FVIII, suggesting the cell line possesses endothelial cell characteristics and could be used for in vitro studies of the ApoM-S1P axis. Conclusions: EA.hy926 cell line is suitable for investigation of the ApoM-S1P axis in vitro. However, Since EA.hy926 cells expressed endogenous ApoM, C-myc and His tagged proteins, the exogenous recombinant ApoM should not be labeled with C-myc and His tags for distinguishing from endogenous ApoM. In addition, overexpression of ApoM should be considered to significantly increase the expression of S1PR1 when studying the APOM-S1P axis.