Archives of Dermatological Research, Journal Year: 2025, Volume and Issue: 317(1)
Published: April 19, 2025
Language: Английский
International Immunopharmacology, Journal Year: 2024, Volume and Issue: 128, P. 111502 - 111502
Published: Jan. 10, 2024
Language: Английский
Citations
13
International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(6), P. 2808 - 2808
Published: March 20, 2025
Research indicates that fine particulate matter (PM2.5) exposure is associated with the onset of non-alcoholic fatty liver disease (NAFLD), most prevalent chronic disorder. However, underlying pathogenesis mechanisms remain to be fully understood. Our study investigated hub long non-coding RNAs (lncRNAs) and messenger (mRNAs) hepatic steatosis caused by PM2.5 their pathological mechanisms. The analysis gene profiles in GSE186900 dataset from Gene Expression Omnibus (GEO) enabled identification 38 differentially expressed lncRNAs 1945 mRNAs. To explore further, a co-expression network was established utilizing weighted (WGCNA). Moreover, Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) databases were utilized for functional enrichment analysis. identified specific modules, particularly blue turquoise which showed strong correlation NAFLD. Through analysis, we several (including Gm15446, Tmem181b-ps, Adh6-ps1, Gm5848, Zfp141, Rmrp, Rb1) may involved modulating NAFLD, multiple metabolic pathways, inflammation, cell senescence, apoptosis, oxidative stress, various signaling pathways. our provide novel biomarkers potential targets diagnosis treatment
Language: Английский
Citations
1
Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15
Published: Feb. 27, 2024
Background Non-alcoholic fatty liver disease (NAFLD) is the most common chronic globally, with potential to progress non-alcoholic steatohepatitis (NASH), cirrhosis, and even hepatocellular carcinoma. Given absence of effective treatments halt its progression, novel molecular approaches NAFLD diagnosis treatment are paramount importance. Methods Firstly, we downloaded oxidative stress-related genes from GeneCards database retrieved NAFLD-related datasets GEO database. Using Limma R package WGCNA, identified differentially expressed closely associated NAFLD. In our study, 31 intersection by analyzing among genes, as through Weighted Gene Co-expression Network Analysis (WGCNA). a study between Oxidative Stress (OS), three hub using machine learning algorithms: Least Absolute Shrinkage Selection Operator (LASSO) regression, Support Vector Machine - Recursive Feature Elimination (SVM-RFE), RandomForest. Subsequently, nomogram was utilized predict incidence The CIBERSORT algorithm employed for immune infiltration analysis, single sample Set Enrichment (ssGSEA) functional enrichment Protein-Protein Interaction (PPI) networks explore relationships other intersecting OS. distribution these across six cell clusters determined single-cell RNA sequencing. Finally, utilizing relevant data Attie Lab Diabetes Database, tissues NASH mouse model, Western Blot (WB) Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) assays were conducted, this further validated significant roles CDKN1B TFAM in Results course research, strong association stress Subsequent analysis external validation pinpointed two genes: TFAM, demonstrating closest correlation Conclusion This investigation found that hold targets NAFLD, thereby offering innovative perspectives clinical management.
Language: Английский
Citations
8
Journal of Inflammation Research, Journal Year: 2024, Volume and Issue: Volume 17, P. 2103 - 2118
Published: April 1, 2024
Diabetic kidney disease (DKD), is a common microvascular complication and major cause of death in patients with diabetes.Disorders immune cells cytokines can accelerate DKD development number ways.As the composed complex highly differentiated cells, interactions among different cell types play important regulatory roles development.Here, we summarize latest research into molecular mechanisms underlying various renal DKD.In addition, discuss most recent studies related to single technology bioinformatics analysis field DKD.The aims our review were explore as potential therapeutic targets provide some guidance for future clinical treatments.
Language: Английский
Citations
5
Journal of Cellular and Molecular Medicine, Journal Year: 2024, Volume and Issue: 28(15)
Published: Aug. 1, 2024
The aetiology of bone metastasis in prostate cancer (PCa) remains unclear. This study aims to identify hub genes involved this process. We utilized machine learning, GO, KEGG, GSEA, Single-cell analysis, ROC methods for PCa using the TCGA and GEO databases. Potential drugs targeting these were identified. validated results 16 specimens from patients with analysed relationship between clinical features. impact APOC1 on was assessed through vitro experiments. Seven AUC values 0.727-0.926 APOC1, CFH, NUSAP1 LGALS1 highly expressed tissues, while NR4A2, ADRB2 ZNF331 exhibited an opposite trend. Immunohistochemistry further confirmed results. oxidative phosphorylation pathway significantly enriched by identified genes. Aflatoxin B1, benzo(a)pyrene, cyclosporine as potential drugs. expression correlated features metastasis. Silencing inhibited cell proliferation, clonality, migration vitro. 7 that potentially facilitate mitochondrial metabolic reprogramming. emerged a promising therapeutic target prognostic marker
Language: Английский
Citations
4
Frontiers in Pharmacology, Journal Year: 2025, Volume and Issue: 15
Published: Jan. 13, 2025
Lower-grade glioma (LGG) exhibits significant heterogeneity in clinical outcomes, and current prognostic markers have limited predictive value. Despite the growing recognition of histone modifications tumor progression, their role LGG remains poorly understood. This study aimed to develop a modification-based risk signature investigate its relationship with drug sensitivity guide personalized treatment strategies. We performed single-cell RNA sequencing analysis on samples (n = 4) characterize modification patterns. Through integrative TCGA-LGG 513) CGGA datasets 693 n 325), we constructed modification-related (HMRS) using machine learning approaches. The model's performance was validated multiple independent cohorts. further conducted comprehensive analyses molecular mechanisms, immune microenvironment, associated stratification. identified distinct patterns across five major cell populations developed robust 20-gene HMRS from 129 candidate genes that effectively stratified patients into high- low-risk groups significantly different survival outcomes (training set: AUC 0.77, 0.73, 0.71 for 1-, 3-, 5-year survival; P < 0.001). Integration features improved accuracy (C-index >0.70). High-risk tumors showed activation TGF-β IL6-JAK-STAT3 signaling pathways, mutation profiles including TP53 (63% vs 28%), IDH1 (68% 85%), ATRX (46% 20%) mutations. high-risk group demonstrated elevated stromal scores (P 0.001), infiltration, particularly memory CD4+ T cells 0.001) CD8+ Drug revealed differential responses six therapeutic agents Temozolomide targeted drugs 0.05). Our establishes novel model not only accurately predicts patient but also reveals potential targets. associations between stratification provide valuable insights integrated approach offers promising framework improving care through molecular-based assessment selection.
Language: Английский
Citations
0
Journal of Molecular Neuroscience, Journal Year: 2025, Volume and Issue: 75(1)
Published: Jan. 14, 2025
Language: Английский
Citations
0
Biomedicines, Journal Year: 2025, Volume and Issue: 13(2), P. 362 - 362
Published: Feb. 5, 2025
Background: Recent research indicates that lipid metabolism and autophagy play crucial roles in the development of Alzheimer’s disease (AD). Investigating relationship between AD diagnosis gene expression related to metabolism, autophagy, lipophagy may improve early identification therapeutic targets. Methods: Transcription datasets from patients were obtained Gene Expression Omnibus (GEO). Genes associated with sourced Set Enrichment Analysis (GSEA) database Human Autophagy Database (HADb). Lipophagy-related hub genes identified using a combination Limma analysis, weighted co-expression network analysis (WGCNA), machine learning techniques. Based on these genes, we developed an risk prediction nomogram validated its diagnostic accuracy three external validation datasets. Additionally, levels assessed through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: Our genes—ACBD5, GABARAPL1, HSPA8—as being progression. The constructed achieved area under curve (AUC) value 0.894 for prediction, all sets yielding AUC values greater than 0.8, indicating excellent efficacy. qRT-PCR results further corroborated associations development. Conclusions: This study lipophagy-related reliable model, offering insights into pathology facilitating patients.
Language: Английский
Citations
0
European journal of medical research, Journal Year: 2025, Volume and Issue: 30(1)
Published: Feb. 8, 2025
Emerging evidences have indicated a role of the complement system in pathogenesis diabetic nephropathy (DN). Thus, this study was conducted to explore system-related key biomarkers for patients with DN. DN microarray datasets were downloaded from GEO database, followed by differentially expressed genes (DEGs) screening. Complement (CSRGs) searched various databases. Weighted Gene Co-expression Network Analysis (WGCNA) employed screen DN-related genes, then differential CSRGs (DCSRGs) identified, protein–protein interaction (PPI) network construction. In addition, acquired two machine learning algorithms, immune infiltration analysis, Set Enrichment (GSEA), and potential drugs screening conducted. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) western blotting utilized detect ITGB2 expression. Then cell viability, inflammatory factors, expression epithelial–mesenchymal transition (EMT) fibrosis markers determined using Cell Counting Kit-8 (CCK-8) assay, enzyme linked immunosorbent assay (ELISA), assays, respectively. total, 1012 DEGs 974 screened, intersection analysis three (DN-related CSRGs) yielded 13 which considered as DCSRGs. Subsequently, 2 identified learning, namely VWF ITGB2. The both enriched pathways chemokine signaling pathway, CAMs, focal adhesion natural killer cell-mediated cytotoxicity, significantly correlated activated mast cells, resting NK macrophages. Also, related clinical features, including age, serum creatinine level, GFR (MDRD). Besides, mRNA protein levels HG-treated HK-2 cells remarkably elevated. Moreover, viability TNF-α, IL-6, IL-12, α-SMA, E-cadherin vimentin changed HG administration reversed ITGB2-silence. gene overexpressed DN, inhibition attenuated EMT inflammation
Language: Английский
Citations
0
Journal of Inflammation Research, Journal Year: 2025, Volume and Issue: Volume 18, P. 2105 - 2122
Published: Feb. 1, 2025
Background: Ferroptosis is a form of programmed cell death triggered by iron-dependent lipid peroxidation, characterized iron accumulation and elevated reactive oxygen species (ROS), leading to membrane damage. It associated with variety diseases. However, the cellular molecular links between ferroptosis, immune inflammation, brain-peripheral blood axis in Alzheimer's disease (AD) remain unclear. Methods: We integrated bulk RNA-seq data from AD brain tissue peripheral refined screening candidate genes through differential gene expression analysis, weighted co-expression network analysis (WGCNA), other approaches. Additionally, we analyzed single-cell (scRNA-seq) patients' blood, combined scRNA-seq experimental autoimmune encephalomyelitis (EAE) mouse tissue. This enabled us explore AD-related mechanisms cell-type-specific perspective. Finally, were validated ferroptosis models using reverse transcription quantitative PCR (RT-qPCR) immunofluorescence methods. Results: Bulk identified SLC11A1, an inflammatory AD. Single-cell further revealed that SLC11A1 was significantly pro-inflammatory (M1-type) microglia monocytes Moreover, microglial subpopulation M1-type highly ferroptosis. simultaneously expressed characteristic markers monocytes, suggesting these cells may originate thereby triggering neuroinflammation pathway. Cell experiments confirmed upregulated induced Conclusion: study reveals key role AD, particularly context inflammation. provides novel mechanistic perspective offers potential targets for future therapeutic strategies. Keywords: disease, RNA sequencing, microglia,
Language: Английский
Citations
0