Jiangsu Shengze Hospital

Abstract Background Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by persistent mucosal inflammation and immune dysregulation. Dysregulated macrophage polarization is a central driver of immune imbalance in UC. Lipocalin-2 (LCN2), a natural immune-related protein, is upregulated in UC patients. However, its pro-inflammatory role and relationship with macrophage polarization remain unclear. Methods We explored the role of LCN2 in UC through clinical, animal, and cellular studies. Serum and colonic mucosa samples from healthy volunteers and active UC patients were collected to assess LCN2 expression via ELISA and immunohistochemistry. UC was induced in wild-type and LCN2-knockout mice using dextran sulfate sodium (DSS). Transcriptomic analysis, Western blotting, and immunofluorescence were used to evaluate LCN2 expression, localization, and function. Bone marrow-derived macrophages (BMDMs) from WT and LCN2-/- mice were stimulated with M-CSF and LPS, and the expression of the LCN2 receptor SLC22A17, M1 markers, inflammatory cytokines, and NF-κB pathway activation were analyzed. Results LCN2 expression was significantly upregulated in UC patients and DSS-treated mice. LCN2 binds to the macrophage receptor SLC22A17, activates the NF-κB pathway, promotes M1 polarization, and enhances the release of pro-inflammatory cytokines. LCN2 deficiency alleviated colonic inflammation and pathology in DSS-treated mice, reduced macrophage infiltration and M1 polarization, and decreased inflammatory factors expression by inhibiting SLC22A17 internalization and NF-κB. Conclusion This study reveals a novel mechanism by which LCN2 aggravates colitis by promoting pro inflammatory macrophage polarization. LCN2 may serve as a biomarker for UC severity and a potential therapeutic target via its interaction with SLC22A17 and activation of NF-κB signaling. Conflict of interest: Xu, Yan: No conflict of interest Mr. Huang, Junwei: No conflict of interest Chen, Wei: No conflict of interest Niu, Mengdi: No conflict of interest Chen, Miaodan: No conflict of interest Han, Yixuan: No conflict of interest Sun, Lixin: No conflict of interest Wang, Qiang: No conflict of interest Jiao, Chunhua: No conflict of interest

Lactylation, a novel post-translational histone modification, has emerged as a critical regulatory mechanism in various metabolic disorders. However, its role in the pathogenesis of type 2 diabetes (T2D) remains poorly understood. This study aims to investigate the potential of lactylation-related genes as diagnostic biomarkers for T2D. Differential analysis and weighted gene co-expression network analysis (WGCNA) were performed on the GSE164416 dataset. Genes obtained from these analyses were intersected with the lactylation-related genes to screen candidate genes. The LASSO, SVM-RFE and random forest algorithms were applied to screen the characteristic genes, and their diagnostic efficacy was verified in the independent cohort. The functions and immune associations were analyzed by GSVA, ssGSEA, and TF-miRNA regulatory network analysis, and qRT-PCR, western blot and CCK-8 experiments were conducted in the T2D cell model for verification. Lactylation-related IKZF1, S100A4 and VIM were identified as potential diagnostic markers for T2D. These three genes were significantly upregulated in T2D samples and exhibited excellent diagnostic performance (AUC > 0.80) in both the training set and validation set. The GSVA analysis revealed that these three genes were involved in key biological processes such as immune regulation, transcriptional modification, metabolic homeostasis and cytoskeleton remodeling. Cell experiments demonstrated that the three genes were upregulated in T2D cell models and knockdown of their expression could promote cell viability. This study identified and validated three potential diagnostic markers related to lactylation for T2D, providing new molecular evidence for the early diagnosis and mechanism research of this disease.

In the current review, we aim to elucidate the advancements concerning the roles and fundamental mechanisms of intermittent fasting (IF) and fasting-mimicking diet (FMD) in cancers. As a dietary intervention, IF and FMD potentially impede tumor growth by modulating multiple signaling pathways, such as AKT, Nrf2, and AMPK pathways. Moreover, IF and FMD have been reported to be associated with the tumor immune response by regulating various immune cells including tumor-associated macrophages (TAMs), monocytic myeloid-derived suppressor cells (MDSCs), T cells, and B cells. Additionally, IF and FMD can enhance the efficacy and tolerability of therapy, concurrently reducing therapy-induced side effects. Furthermore, several clinical trials have underscored the safety, feasibility, and positive impact on the quality of life associated with IF and FMD, thereby augmenting the effectiveness of conventional anti-- tumor therapies while ameliorating treatment-related side effects. This review provides a comprehensive synthesis of findings and elucidates the underlying mechanisms of IF and FMD in cancer progression and therapy.

Abstract Background： The primary objective of this study is to utilize Mendelian randomization analysis methods to investigate causal relationships between various metabolites and lung cancer. Our aims include not only validating direct associations between metabolites and lung cancer risk, but also exploring potential molecular mechanisms and regulatory pathways underlying these relationships. Through these efforts, we aim to provide new biomarkers and therapeutic targets for early diagnosis and personalized treatment strategies for lung cancer. Methods： This study aims to explore causal relationships between 1,091 metabolites and lung cancer (LC). The research design includes metabolite data from 8,299 individuals collected from the Canadian Longitudinal Study on Aging (CLSA), encompassing large-scale GWAS analyses. Summary statistics for LC come from GWAS covering 29,836 cases and 55,586 controls, as well as another dataset comprising 3,791 cases and 489,012 control individuals. In Mendelian randomization (MR) analysis, 753 metabolites were selected and studied using SNPs as instrumental variables, rigorously screened and confirmed for validity using F-statistics. Various MR methods were employed, including inverse variance weighting and Wald ratio, with significance determined by Bonferroni correction. Sensitivity analyses included Cochran's Q statistics and MR-Egger method to assess heterogeneity and pleiotropy among instrumental SNPs. Generalized summary data-based MR (GSMR) was used to validate causal relationships between LC and metabolites, with HEIDI method applied to exclude pleiotropic SNPs. Additionally, colocalization analysis explored shared regulatory genes of causal SNPs between LC and metabolites. Finally, SMR analysis further investigated relationships with gene expression. In summary, this study integrates multiple methods and large datasets to uncover potential associations and molecular mechanisms between various metabolites and LC. Results： In Mendelian randomization analysis, using IVW and Wald ratio methods, 6 metabolites significantly associated with lung cancer (LC) were identified: increased risk with 3-methylxanthine levels and X-18935 levels, decreased risk with Paraxanthine levels, Isovalerylcarnitine (C5) levels, Indolin-2-one levels, and 6-hydroxyindole sulfate levels. Additionally, 63 metabolites showed potential associations with LC. Validation analyses confirmed LC associations for 35 metabolites using external data. GSMR analysis validated LC associations for 32 metabolites, with consistent effect directions, including significant associations for Paraxanthine levels, X-18935 levels, and 6-hydroxyindole sulfate levels. Colocalization analysis revealed significant evidence of shared colocalization for 5 metabolites with LC, all associated with the same candidate causal SNP. Gene expression analysis demonstrated complex regulatory relationships between multiple genes (e.g., HIST1H4E and GATAD2A) and LC and its related metabolite levels. Conclusion： This study provides a comprehensive analysis of complex relationships between various metabolites and lung cancer, revealing their potential roles and regulatory mechanisms in disease development. By integrating different analytical and validation approaches, we offer important scientific insights for future personalized strategies in disease prevention and treatment. These findings not only contribute to discovering new biomarkers but also lay groundwork for targeted therapeutic approaches and personalized medical interventions.

Acute myocardial infarction (AMI) and the following heart failure are main causes of disability and death across the globe. Endothelial progenitor cell (EPC) levels are linked to AMI. Herein, we assessed the predictive value of EPCs for post-percutaneous coronary intervention (PCI) ventricular remodeling in AMI patients. This study retrospectively analyzed 215 AMI patients receiving PCI, who were then categorized into the VR ( n = 66) and N-VR ( n = 149) groups as per whether they developed post-PCI ventricular remodeling. Left ventricular ejection fraction (LVEF), N-terminal pro-brain natriuretic peptide (NT-pro-BNP), and EPCs were measured. The correlations of LVEF and NT-pro-BNP with EPCs, the predictive value of EPCs for post-PCI ventricular remodeling, and the risk of post-PCI ventricular remodeling in AMI patients with different EPC levels were analyzed by Spearman's analysis, receiver-operating characteristic curve, and Kaplan-Meier curve. LVEF and EPC levels were lower and NT-pro-BNP level was higher in the VR group than the N-VR group. EPC levels in the class III-IV group were lower than those in the class I-II group. EPC levels in AMI patients correlated positively with LVEF ( r = 0.683) and negatively with NT-pro-BNP ( r = -0.761). EPCs exhibited high predictive value for post-PCI ventricular remodeling in AMI [area under the curve (AUC) of 0.822] and anterior MI (AUC = 0.941) patients. AMI and anterior MI patients with low EPC levels had a higher risk of post-PCI ventricular remodeling. Low EPC levels have high predictive value for post-PCI ventricular remodeling, and increase the risk of post-PCI ventricular remodeling in AMI patients.

The comorbidity between cardiovascular diseases (CVD) and schizophrenia (SCZ) has attracted widespread attention from researchers, with shared genetic causes potentially providing important insights into their association. This study conducted a comprehensive analysis of genetic data from 17 types of CVD and SCZ using genome-wide multi-trait association studies (GWAS), employing statistical methods such as LDSC, MTAG, LAVA, and bidirectional Mendelian randomization to explore global and local genetic correlations and identify pleiotropic single nucleotide variants (SNVs). The analysis revealed a significant genetic correlation between CVD and SCZ, identifying 842 potential pleiotropic single nucleotide variants (SNVs) and multiple associated biological pathways. Notably, genes such as TRIM27, CENPM, and MYH7B played critical roles in the shared genetic variations of both types of diseases. This study reveals the complex genetic relationship between CVD and SCZ, highlighting potential shared biological mechanisms involving immune responses, metabolic factors, and neurodevelopmental processes, thereby providing new directions for future interventions and treatments.

[This corrects the article DOI: 10.3389/fphar.2022.957433.].

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease with poorly understood pathophysiology. Recent studies have highlighted systemic inflammation, especially the role of circulating inflammatory proteins, in ALS. This study investigates the potential causal link between these proteins and ALS. We employed a two-sample Mendelian Randomization(MR) approach, analyzing data from large-scale genome-wide association studies to explore the relationship between 91 circulating inflammatory proteins and ALS. This included various MR methods like MR Egger, weighted median, and inverse-variance weighted, complemented by sensitivity analyses for robust results. Significant associations were observed between levels of inflammatory proteins, including Adenosine Deaminase, Interleukin-17C, Oncostatin-M, Leukemia Inhibitory Factor Receptor, and Osteoprotegerin, and ALS risk. Consistencies were noted across different P-value thresholds. Bidirectional MR suggested that ALS risk might influence levels of certain inflammatory proteins. Our findings, via MR analysis, indicate a potential causal relationship between circulating inflammatory proteins and ALS. This sheds new light on ALS pathophysiology and suggests possible therapeutic targets. Further research is required to confirm these results and understand the specific roles of these proteins in ALS.

Increasing evidence has demonstrated that cancer cell metabolism is a critical factor in tumor development and progression; however, its role in glioblastoma (GBM) remains limited. In the present study, we classified GBM into three metabolism subtypes (MC1, MC2, and MC3) through cluster analysis of 153 GBM samples from the RNA-sequencing data of The Cancer Genome Atlas (TCGA) based on 2752 metabolism-related genes (MRGs). We further explored the prognostic value, metabolic signatures, immune infiltration, and immunotherapy sensitivity of the three metabolism subtypes. Moreover, the metabolism scoring model was established to quantify the different metabolic characteristics of the patients. Results showed that MC3, which is associated with a favorable survival outcome, had higher proportions of isocitrate dehydrogenase (IDH) mutations and lower tumor purity and proliferation. The MC1 subtype, which is associated with the worst prognosis, shows a higher number of segments and homologous recombination defects and significantly lower mRNA expression-based stemness index (mRNAsi) and epigenetic-regulation-based mRNAsi. The MC2 subtype has the highest T-cell exclusion score, indicating a high likelihood of immune escape. The results were validated using an independent dataset. Five MRGs (ACSL1, NDUFA2, CYP1B1, SLC11A1, and COX6B1) correlated with survival outcomes were identified based on metabolism-related co-expression module analysis. Laboratory-based validation tests further showed the expression of these MRGs in GBM tissues and how their expression influences cell function. The results provide a reference for developing clinical management approaches and treatments for GBM.

BackgroundBipolar disorder and metabolic syndrome are both associated with the expression of immune disorders. The current study aims to find the effective diagnostic candidate genes for bipolar affective disorder with metabolic syndrome.MethodsA validation data set of bipolar disorder and metabolic syndrome was provided by the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were found utilizing the Limma package, followed by weighted gene co-expression network analysis (WGCNA). Further analyses were performed to identify the key immune-related center genes through function enrichment analysis, followed by machine learning-based techniques for the construction of protein–protein interaction (PPI) network and identification of the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). The receiver operating characteristic (ROC) curve was plotted to diagnose bipolar affective disorder with metabolic syndrome. To investigate the immune cell imbalance in bipolar disorder, the infiltration of the immune cells was developed.ResultsThere were 2,289 DEGs in bipolar disorder, and 691 module genes in metabolic syndrome were identified. The DEGs of bipolar disorder and metabolic syndrome module genes crossed into 129 genes, so a total of 5 candidate genes were finally selected through machine learning. The ROC curve results-based assessment of the diagnostic value was done. These results suggest that these candidate genes have high diagnostic value.ConclusionPotential candidate genes for bipolar disorder with metabolic syndrome were found in 5 candidate genes (AP1G2, C1orf54, DMAC2L, RABEPK and ZFAND5), all of which have diagnostic significance.