Introduction Specific lipid-reducing therapeutics, including statins, are known for mitigating cardiovascular diseases due to their comprehensive benefits including anti-inflammatory properties, antioxidative stress response, and enhancement of endothelial function. The objective of our study was to determine the causative impact of lipid-reducing agents (HMGCR inhibitors, PCSK9 inhibitors, and NPC1L1 inhibitor) on the outcomes of pulmonary hypertension via a two-sample Mendelian randomization (MR) analysis. Methods Two types of genetic tools were employed to estimate the exposure to lipid-lowering drugs, comprising expression quantitative trait loci of the drug’s target genes and genetic variations close to or within the target genes related to low-density lipoprotein (LDL cholesterol derived from a genome-wide association study). We utilized summary-data-based MR (SMR) and inverse-variance-weighted MR (IVWMR) methodologies for estimating effect sizes. Results SMR analysis indicated that elevated HMGCR expression correlates with increased pulmonary hypertension risk (β=-0.964, se=0.276). Yet, no evident causative link between HMGCR-regulated LDL cholesterol and COVID-19 hospitalization was observed in the IVW-MR analysis (β = -0.21, se= 0.17). Conclusions Our Mendelian randomization investigation unveiled a possible positive impact of lipid-lowering therapeutics on the prognosis of pulmonary hypertension. Importantly, no causal relation was established between LDL cholesterol and pulmonary hypertension. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was supported by the Traditional Chinese Medicine Inheritance and Innovation and "Qin Medicine" Development [No. 2021-03-22-001]，Qin Chuangyuan Traditional Chinese Medicine Innovation Research and Development Transformation Project (No.2022-QCYZH-022),the Key Basic Natural Science Foundation of Shaanxi Province [No. 2022JZ-47], the Key Industrial Innovation Chain Project in Shaanxi Province of China [No. 2021ZDLSF02-03 and 2020ZDLSF01-08], the Key Program for the Shaanxi Provincial Health and Health Research Fund Project (No. 2022D024), and the Natural Science Foundation of Shaanxi Province [No. 2019JM-440 and No.2021JQ-911]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The data used in our study is sourced entirely from the Opengwas website, which is a public, ethically-reviewed database. We did not collect data directly from any participants or subjects; thus, our study does not involve direct research or data collection on individuals. The data from the Opengwas website has already undergone appropriate ethical review and approval. Consequently, we believe that our study does not require separate ethical approval in this context. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data used in our study is sourced entirely from the Opengwas website, which is a public, ethically-reviewed database.

Background. Stanford type A aortic dissection (TAAD) is one of the most life-threatening cardiovascular emergencies with high mortality and morbidity, and necroptosis is a newly identified type of programmed cell death and contributes to the pathogenesis of various cardiovascular diseases. However, the role of necroptosis in TAAD has not been elucidated. This study was aimed at determining the role of necroptosis in TAAD using bioinformatics analyses. Methods. The RNA sequencing dataset GSE153434 and the microarray dataset GSE52093 were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes of necroptosis (NRDEGs) were identified based on differentially expressed genes (DEGs) and necroptosis gene set. Gene set enrichment analysis (GSEA) was applied to evaluate the gene enrichment signaling pathway in TAAD. The STRING database and Cytoscape software were used to establish and visualize protein-protein interaction (PPI) networks and identify the key functional modules of NRDEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of NRDEGs were also performed. Additionally, Spearman correlations were used to construct the necroptosis-related transcription factor-target genes regulatory network, immune infiltration patterns were analyzed using the ImmuCellAI algorithm, and the correlation between immune cell-type abundance and NRDEGs expression was investigated. The expression levels of NRDEGs and immune infiltration were additionally verified in the GSE52093 dataset. Results. We found that the necroptosis pathway was considerably enriched and activated in TAAD samples. Overall, 25 NRDEGs were identified including MLKL, RIPK1, and FADD, and among them, 18 were verified in the validation set. Moreover, GO and KEGG enrichment analyses found that NRDEGs were primarily involved in the tumor necrosis factor signaling pathway, nucleotide-binding oligomerization domain-like receptor signaling pathway, and interleukin-17 signaling pathway. The imbalance of Th17/Treg cells was identified in the TAAD samples. Furthermore, correlation analysis indicated that expression of NRDEGs was positively associated with proinflammatory immune-cell infiltrations and negatively associated with anti-inflammatory or regulatory immune-cell infiltrations. Conclusions. The present findings suggest that necroptosis phenomenon exists in TAAD and correlates with immune cell infiltration, which indicate necroptosis may promote the development of TAAD through activating immune infiltration and immune response. This study paves a new road to future investigation of the pathogenic mechanisms and therapeutic strategies for TAAD.

BackgroundIn 2019, there were 28. 76 million patients with stroke in China, with ~25% of them suffering from cryptogenic stroke (CS). Patent foramen ovale (PFO) is related to CS, and PFO closure can reduce recurrent stroke. To date, no study has investigated the cost-effectiveness of PFO closure vs. medical therapy among such populations in China.MethodsA Markov model with a cycle length of 3 months was established to compare the 30-year cost-effectiveness of PFO closure and medical therapy. The transition probability of recurrent stroke was derived from the RESPECT study, and the costs and utility were obtained from domestic data or studies conducted in China. The primary outcome of this study was the incremental cost-effectiveness ratio (ICER), which represents the incremental cost per quality-adjusted life year (QALY). PFO closure was considered cost-effective if the ICER obtained was lower than the willingness-to-pay (WTP) threshold of 37,654 USD/QALY; otherwise, PFO closure was regarded as not being cost-effective. One-way and probabilistic sensitivity analyses were performed to test the robustness of the results.ResultsAfter a simulation of a 30-year horizon, a cryptogenic stroke patient with PFO was expected to have QALY of 13.15 (15.26 LY) if he received PFO closure and a corresponding value of 11.74 QALY (15.14 LY) after medical therapy. The corresponding costs in both cohorts are US $8,131 and US $4,186, respectively. Thus, an ICER of 2783 USD/QALY and 31264 USD/LY was obtained, which is lower than the WTP threshold. One-way and probabilistic sensitivity analyses showed that the results were robust.ConclusionWith respect to the WTP threshold of three times per capita GDP in China in 2021, PFO closure is a cost-effective method for Chinese cryptogenic stroke patients with PFO, as shown in the 30-year simulation.

Identifying biomarkers for abdominal aortic aneurysms (AAA) is key to understanding their pathogenesis, developing novel targeted therapeutics, and possibly improving patients outcomes and risk of rupture. Here, we identified AAA biomarkers from public databases using single-cell RNA-sequencing, weighted co-expression network (WGCNA), and differential expression analyses. Additionally, we used the multiple machine learning methods to identify biomarkers that differentiated large AAA from small AAA. Biomarkers were validated using GEO datasets. CIBERSORT was used to assess immune cell infiltration into AAA tissues and investigate the relationship between biomarkers and infiltrating immune cells. Therefore, 288 differentially expressed genes (DEGs) were screened for AAA and normal samples. The identified DEGs were mostly related to inflammatory responses, lipids, and atherosclerosis. For the large and small AAA samples, 17 DEGs, mostly related to necroptosis, were screened. As biomarkers for AAA, G0/G1 switch 2 (G0S2) (Area under the curve [AUC] = 0.861, 0.875, and 0.911, in GSE57691, GSE47472, and GSE7284, respectively) and for large AAA, heparinase (HPSE) (AUC = 0.669 and 0.754, in GSE57691 and GSE98278, respectively) were identified and further verified by qRT-PCR. Immune cell infiltration analysis revealed that the AAA process may be mediated by T follicular helper (Tfh) cells and the large AAA process may also be mediated by Tfh cells, M1, and M2 macrophages. Additionally, G0S2 expression was associated with neutrophils, activated and resting mast cells, M0 and M1 macrophages, regulatory T cells (Tregs), resting dendritic cells, and resting CD4 memory T cells. Moreover, HPSE expression was associated with M0 and M1 macrophages, activated and resting mast cells, Tregs, and resting CD4 memory T cells. Additional, G0S2 may be an effective diagnostic biomarker for AAA, whereas HPSE may be used to confer risk of rupture in large AAAs. Immune cells play a role in the onset and progression of AAA, which may improve its diagnosis and treatment.

目的 通过生物信息学方法对稳定性心绞痛患者外周血基因表达谱芯片进行分析,获取其外周血表达谱特征并筛选关键差异表达基因作为潜在的分子标记物并构建Nomogram诊断模型.方法 从NCBI中的基因表达综合(Gene Expression Omnibus,GEO)数据库中下载稳定性心绞痛患者和对照组的外周血基因表达谱芯片数据集GSE98583,使用R软件limma包筛选出具有显著意义的差异基因(differential expression genes,DEGs);利用clusterProfiler包进行基因本体(gene ontology,GO)与KEGG(kyoto encyclopedia of genes and genomes)通路富集分析;使用STRING在线分析工具构建蛋白交互网络和Cytoscape软件Cytohubba和Mcode插件筛选出关键基因;以关键基因为变量构建稳定性心绞痛Nomogram分子诊断预测模型.结果 通过比较稳定性心绞痛患者和正常受试者外周血基因表达谱,共筛选出303个差异表达基因,其中上调基因160条,下调基因43条;GO和KEGG分析表明,这些差异表达基因主要参与神经递质配体受体相互作用、脂肪吸收消化、钙调节信号通路、PI3K-Akt通路、NF-kappaB通路及氧化磷酸化等有关,使用Cytohubba进一步分析,筛选出10个关键基因BDNF,GFAP,SYN1,NES,PLG,HPGDS,KCNC1,APOA4,AMBP和TJP1,并建立了Nomogram诊断模型.结论 使用生物信息学方法揭示稳定性心绞痛外周血差异基因潜在特征,为稳定性心绞痛的早期诊断提供新的思路.

Aim: Coronary artery disease (CAD) is a heterogeneous disorder with high morbidity, mortality, and healthcare costs, representing a major burden on public health. Here, we aimed to improve our understanding of the genetic drivers of ferroptosis and necroptosis and the clustering of gene expression in CAD in order to develop novel personalized therapies to slow disease progression. Methods: CAD datasets were obtained from the Gene Expression Omnibus. The identification of ferroptosis- and necroptosis-related differentially expressed genes (DEGs) and the consensus clustering method including the classification algorithm used km and distance used spearman were performed to differentiate individuals with CAD into two clusters (cluster A and cluster B) based expression matrix of DEGs. Next, we identified four subgroup-specific genes of significant difference between cluster A and B and again divided individuals with CAD into gene cluster A and gene cluster B with same methods. Additionally, we compared differences in clinical information between the subtypes separately. Finally, principal component analysis algorithms were constructed to calculate the cluster-specific gene score for each sample for quantification of the two clusters. Results: In total, 25 ferroptosis- and necroptosis-related DEGs were screened. The genes in cluster A were mostly related to the neutrophil pathway, whereas those in cluster B were mostly related to the B-cell receptor signaling pathway. Moreover, the subgroup-specific gene scores and CAD indices were higher in cluster A and gene cluster A than in cluster B and gene cluster B. We also identified and validated two genes showing upregulation between clusters A and B in a validation dataset. Conclusion: High expression of CBS and TLR4 was related to more severe disease in patients with CAD, whereas LONP1 and HSPB1 expression was associated with delayed CAD progression. The identification of genetic subgroups of patients with CAD may improve clinician knowledge of disease pathogenesis and facilitate the development of methods for disease diagnosis, classification, and prognosis.