This study aimed to identify key genes related to copper metabolism in Parkinson's disease (PD), providing insight into their roles in disease progression. Using bioinformatic analyses, the study identified hub genes related to copper metabolism in PD patients. Differentially expressed genes (DEGs) were identified using the limma package, and copper-metabolism-related genes (CMRGs) were sourced from the Genecard database. Immune cell-related genes were derived through immune infiltration and Weighted Gene Co-expression Network Analysis (WGCNA). Hub genes were pinpointed by integrating DEGs, CMRGs, and immune cell-related genes. Functional analyses included Receiver Operating Characteristic (ROC) analysis, Ingenuity Pathway Analysis (IPA), and networks for miRNA-mRNA-transcription factor (TF), Competitive Endogenous RNA (ceRNA), and hub gene-drug interactions. Validation was performed in cerebrospinal fluid (CSF) samples from PD patients, while in vitro experiments utilized GBE1- overexpressing SH-SY5Y cells to examine cell proliferation, migration, and viability. Nine hub genes (HPRT1, GLS, SNCA, MDH1, GBE1, DDC, STXBP1, ACHE, and AGTR1) were identified from 753 CMRGs, 416 DEGs, and 951 immune cell-related genes. ROC analysis showed high predictive accuracy for PD, and principal component analysis (PCA) effectively distinguished PD patients from controls. IPA identified 20 significant pathways, and various networks highlighted miRNA, TF, and drug interactions with the hub genes. Hub gene expression was validated in PD CSF samples. GBE1-overexpressing cells displayed enhanced proliferation, migration, and viability. The study identified nine copper metabolism-related genes as potential therapeutic targets in PD, highlighting their relevance in PD pathology and possible treatment pathways.

Osteoarthritis (OA) is a common degenerative joint disease characterized by the progressive degradation of articular cartilage, synovial inflammation, and subchondral bone remodeling. This review explores the interplay between aging, PANoptosis, and inflammation in OA progression. Age-related cellular and immune dysfunctions, including cellular senescence, senescence-associated secretory phenotypes (SASPs), and immunosenescence, significantly contribute to joint degeneration. In OA, dysregulated apoptosis, necroptosis, and pyroptosis, particularly in chondrocytes, exacerbate cartilage damage. Apoptosis, mediated by the JNK pathway, reduces chondrocyte density, while necroptosis and pyroptosis, involving RIPK-1/RIPK-3 and the NLRP3 inflammasome, respectively, amplify inflammation and cartilage destruction. Inflammatory cytokines and damage-associated molecular patterns (DAMPs) further enhance these PANoptotic pathways. Current therapeutic strategies primarily focus on anti-inflammatory agents such as non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids, with growing interest in anti-senescence drugs targeting cellular senescence and SASP. Additionally, exploring PANoptosis mechanisms offers potential for innovative OA treatments.

The slow-developing neurological disorder Alzheimer’s disease (AD) has no recognized etiology. A bioinformatics investigation verified copper metabolism indicators for AD development. GEO contributed AD-related datasets GSE1297 and GSE5281. Differential expression analysis and WGCNA confirmed biomarker candidate genes. Each immune cell type in AD and control samples was scored using single sample gene set enrichment analysis. Receiver Operating Characteristic (ROC) analysis, short Time-series Expression Miner (STEM) grouping, and expression analysis between control and AD samples discovered copper metabolism indicators that impacted AD progression. We test clinical samples and cellular function to ensure study correctness. Biomarker-targeting miRNAs and lncRNAs were predicted by starBase. Trust website anticipated biomarker-targeting transcription factors. In the end, Cytoscape constructed the TF/miRNA-mRNA and lncRNA-miRNA networks. The DGIdb database predicted biomarker-targeted drugs. We identified 57 differentially expressed copper metabolism-related genes (DE-CMRGs). Next, fourteen copper metabolism indicators impacting AD progression were identified: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38. A TF/miRNA-mRNA regulation network was then established with two miRNAs (hsa-miR-34a-5p and 34c-5p), six TFs (NFKB1, RELA, MYC, HIF1A, JUN, and SP1), and four biomarkers. The DGIdb database contained 171 drugs targeting ten copper metabolism-relevant biomarkers (BRCA1, MITF, NFKBIA, CD38, CCK2, HPRT1, SPHK1, LDHA, SCG5, and SYT1). Copper metabolism biomarkers CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38 alter AD progression, laying the groundwork for disease pathophysiology and novel AD diagnostic and treatment.

Purpose: Atherosclerosis (AS) is a chronic inflammatory vascular disease and the predominant cause of ischemic stroke (IS). AS is a potential pathogenetic factor in IS. However, the processes by which they interact remain unknown. The purpose of this paper was to investigate the shared gene signatures and putative molecular processes in AS and IS. Methods: Gene Expression Omnibus (GEO) data for AS and IS microarrays were retrieved. The co-expression modules associated with AS and IS were identified using the Weighted Gene Co-Expression Network Analysis (WGCNA). We constructed an interaction network of shared differentially expressed genes in AS and IS and conducted an enrichment analysis using ClueGO software. We validated the results in a separate cohort through differential gene analysis. Additionally, we retrieved AS and IS-related miRNAs from the Human microRNA Disease Database (HMDD) and predicted their target genes using miRWalk. We then built a network of miRNAs-mRNAs-KEGG pathways using the shared genes. Results: Through WGCNA, we identified five modules and six modules as significant in AS and IS, respectively. A ClueGO enrichment analysis of common genes showed that highly active CCR1 chemokine receptor binding is critical to AS and IS pathogenesis. The differential analysis expression results in another cohort closely matched these findings. The miRNA-mRNA network suggested that hsa-miR-330-5p, hsa-miR-143-3p, hsa-miR-16-5p, hsa-miR-152-3p might regulate the shared gene KRAS, which could be a key player in AS and IS. Conclusion: We integrated ischemic stroke and carotid atherosclerosis public database data and found that ATF3, CCL3, CCL4, JUNB, KRAS, and ZC3H12A may affect both, making them novel biomarkers or therapeutic target genes. Clinical samples and expression trends supported our analyses of pivotal genes.

Carotid atherosclerotic plaques are the manifestation of atherosclerosis in the carotid arteries and can significantly increase the incidence of cerebrovascular disease. Macrophages and smooth muscle cells are crucial for their development. To reveal the mechanism of carotid atherosclerotic plaque formation, we performed single-nucleus RNA sequencing of the carotid plaque tissue and identified 11 cell types, and the macrophages were divided into 5 different macrophage subpopulations. The macrophages and smooth muscle cells in the patients with symptomatic carotid atherosclerotic plaques caused intraplaque cell death via the mitochondrial autophagic pathway, resulting in plaque instability and rupture, which in turn led to clinical cardiovascular and cerebrovascular events. The findings provide new insights into carotid atherosclerosis formation, and this may provide new directions for the prevention and treatment of carotid atherosclerosis.