Identification of Key Genes as Early Warning Signals of Acute Myocardial Infarction Based on Weighted Gene Correlation Network Analysis and Dynamic Network Biomarker Algorithm

Identification of key genes and diagnostic biomarkers for peripheral atherosclerosis: A multi-omics approach

Peripheral atherosclerosis (PAS), characterized by lipid plaque accumulation in arterial walls, significantly increases cardiovascular risk. This study aimed to identify molecular biomarkers and elucidate underlying mechanisms of PAS progression. We analyzed 2 gene expression omnibus datasets (GSE28829, GSE100927) to identify differentially expressed genes (P < .05, |log2FC| ≥ 0.585). Functional enrichment (Gene Ontology/Kyoto Encyclopedia of Genes and Genomes) and Mendelian randomization analyses were performed using genome-wide association study and expression quantitative trait loci data. Six hub genes were validated through single-cell RNA sequencing and independent datasets. A diagnostic nomogram was developed and evaluated using calibration curves, decision curve analysis, and receiver operating characteristic metrics. Integrated analysis revealed 6 key PAS-associated genes (leukocyte immunoglobulin-like receptor B1, hematopoietic cell-specific lyn substrate 1, plasminogen activator urokinase, C-type lectin domain family 2 member B, phosphatidylinositol-4-phosphate 5-kinase type 1 beta, cofilin 2). The diagnostic model demonstrated exceptional accuracy, achieving area under the receiver operating characteristic curves of 1.0 (training) and 0.975 (validation). Mendelian randomization confirmed causal relationships, with cofilin 2 and phosphatidylinositol-4-phosphate 5-kinase type 1 beta showing protective effects (odds ratio 0.74-0.90), while C-type lectin domain family 2 member B, hematopoietic cell-specific lyn substrate 1, leukocyte immunoglobulin-like receptor B1, and plasminogen activator urokinase emerged as risk factors. This multi-omics study identifies novel molecular signatures of PAS and establishes a robust diagnostic tool. The findings advance our understanding of PAS pathogenesis and pave the way for personalized therapeutic strategies.

Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1

Background

Psoriasis represents a persistent, immune-driven inflammatory condition affecting the skin, characterized by a lack of well-established biologic treatments without adverse events. Consequently, the identification of novel targets and therapeutic agents remains a pressing priority in the field of psoriasis research.

Methods

We collected single-cell RNA sequencing (scRNA-seq) datasets and inferred T cell differentiation trajectories through pseudotime analysis. Bulk transcriptome and scRNA-seq data were integrated to identify differentially expressed genes (DEGs). Machine learning was employed to screen candidate genes. Correlation analysis was used to predict the interactions between cells expressing insulin-induced gene 1 (INSIG1) and other immune cells. Finally, drug docking was performed on INSIG1, and the expression levels of INSIG1 in psoriasis were verified through clinical and in vivo experiments, and further in vivo experiments established the efficacy of tetrandrine in the treatment of psoriasis.

Results

T cells were initially categorized into seven states, with differentially expressed genes in T cells (TDEGs) identified and their functions and signaling pathways. INSIG1 emerged as a characteristic gene for psoriasis and was found to be downregulated in psoriasis and potentially negatively associated with T cells, influencing psoriasis fatty acid metabolism, as inferred from enrichment and immunoinfiltration analyses. In the cellular communication network, cells expressing INSIG1 exhibited close interactions with other immune cells through multiple signaling channels. Furthermore, drug sensitivity showed that tetrandrine stably binds to INSIG1, could be a potential therapeutic agent for psoriasis.

Conclusion

INSIG1 emerges as a specific candidate gene potentially regulating the fatty acid metabolism of patients with psoriasis. In addition, tetrandrine shows promise as a potential treatment for the condition.

Insulin-Induced Gene 1-Enhance Secretion of BMSC Exosome Enriched in miR-132-3p Promoting Wound Healing in Diabetic Mice

Chronic diabetic wounds represent a significant clinical challenge because of impaired healing processes, which require innovative therapeutic strategies. This study explores the therapeutic efficacy of insulin-induced gene 1-induced bone marrow mesenchymal stem cell exosomes (Insig1-exos) in promoting wound healing in diabetic mice. We demonstrated that Insig1 enhanced the secretion of bone marrow mesenchymal stem cell-derived exosomes, which are enriched with miR-132-3p. Through a series of in vitro and in vivo experiments, these exosomes significantly promoted the proliferation, migration, and angiogenesis of dermal fibroblasts under high-glucose conditions. They also regulated key wound-healing factors, including matrix metalloproteinase-9, platelet-derived growth factor, vascular endothelial growth factor, transforming growth factor-β1, and platelet endothelial cell adhesion molecule-1, thereby accelerating wound closure in diabetic mice. Histological analysis showed that Insig1-exos were more effective in promoting epithelialization, enhancing collagen deposition, and reducing inflammation. Additionally, inhibition of miR-132-3p notably diminished these therapeutic effects, underscoring its pivotal role in the wound-healing mechanism facilitated by Insig1-exos. This study elucidates the molecular mechanisms through which Insig1-exos promotes diabetic wound healing, highlighting miR-132-3p as a key mediator. These findings provide new strategies and theoretical foundations for treating diabetes-related skin injuries.

Specific circulating miRNAs are associated with plasma lipids in a healthy American cohort

Low-density lipoprotein cholesterol (LDL-c) is both a therapeutic target and a risk factor for cardiovascular disease (CVD). MicroRNA (miRNA) has been shown to regulate cholesterol homeostasis, and miRNA in blood circulation has been linked to hypercholesterolemia. However, few studies to date have associated miRNA with phenotypes like LDL-c in a healthy population. To this end, we analyzed circulating miRNA in relation to LDL-c in a healthy cohort of 353 participants using two separate bioinformatic approaches. The first approach found that miR-15b-5p and miR-16-5p were upregulated in individuals with at-risk levels of LDL-c. The second approach identified two miRNA clusters, one that positively and a second that negatively correlated with LDL-c. Included in the cluster that positively correlated with LDL-c were miR-15b-5p and miR-16-5p, as well as other miRNA from the miR-15/107, miR-30, and let-7 families. Cross-species analyses suggested that several miRNAs that associated with LDL-c are conserved between mice and humans. Finally, we examined the influence of diet on circulating miRNA. Our results robustly linked circulating miRNA with LDL-c, suggesting that miRNA could be used as biomarkers for hypercholesterolemia or targets for developing cholesterol-lowering drugs.NEW & NOTEWORTHY This study explored the association between circulating microRNA (miRNA) and low-density lipoprotein cholesterol (LDL-c) in a healthy population of 353 participants. Two miRNAs, miR-15b-5p and miR-16-5p, were upregulated in individuals with at-risk LDL-c levels. Several miRNA clusters were positively and negatively correlated with LDL-c and are known to target mRNA involved in lipid metabolism. The study also investigated the influence of diet on circulating miRNA, suggesting potential biomarkers for hypercholesterolemia.

Detecting early-warning biomarkers associated with heart-exosome genetic-signature for acute myocardial infarction: A source-tracking study of exosome

The genetic information of plasma total-exosomes originating from tissues have already proven useful to assess the severity of coronary artery diseases (CAD). However, plasma total-exosomes include multiple sub-populations secreted by various tissues. Only analysing the genetic information of plasma total-exosomes is perturbed by exosomes derived from other organs except the heart. We aim to detect early-warning biomarkers associated with heart-exosome genetic-signatures for acute myocardial infarction (AMI) by a source-tracking analysis of plasma exosome. The source-tracking of AMI plasma total-exosomes was implemented by deconvolution algorithm. The final early-warning biomarkers associated with heart-exosome genetic-signatures for AMI was identified by integration with single-cell sequencing, weighted gene correction network and machine learning analyses. The correlation between biomarkers and clinical indicators was validated in impatient cohort. A nomogram was generated using early-warning biomarkers for predicting the CAD progression. The molecular subtypes landscape of AMI was detected by consensus clustering. A higher fraction of exosomes derived from spleen and blood cells was revealed in plasma exosomes, while a lower fraction of heart-exosomes was detected. The gene ontology revealed that heart-exosomes genetic-signatures was associated with the heart development, cardiac function and cardiac response to stress. We ultimately identified three genes associated with heart-exosomes defining early-warning biomarkers for AMI. The early-warning biomarkers mediated molecular clusters presented heterogeneous metabolism preference in AMI. Our study introduced three early-warning biomarkers associated with heart-exosome genetic-signatures, which reflected the genetic information of heart-exosomes carrying AMI signals and provided new insights for exosomes research in CAD progression and prevention.

Acute ischemia induces spatially and transcriptionally distinct microglial subclusters

BACKGROUND

Damage in the ischemic core and penumbra after stroke affects patient prognosis. Microglia immediately respond to ischemic insult and initiate immune inflammation, playing an important role in the cellular injury after stroke. However, the microglial heterogeneity and the mechanisms involved remain unclear.

METHODS

We first performed single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) on middle cerebral artery occlusion (MCAO) mice from three time points to determine stroke-associated microglial subclusters and their spatial distributions. Furthermore, the expression of microglial subcluster-specific marker genes and the localization of different microglial subclusters were verified on MCAO mice through RNAscope and immunofluorescence. Gene set variation analysis (GSVA) was performed to reveal functional characteristics of microglia sub-clusters. Additionally, ingenuity pathway analysis (IPA) was used to explore upstream regulators of microglial subclusters, which was confirmed by immunofluorescence, RT-qPCR, shRNA-mediated knockdown, and targeted metabolomics. Finally, the infarct size, neurological deficits, and neuronal apoptosis were evaluated in MCAO mice after manipulation of specific microglial subcluster.

RESULTS

We discovered stroke-associated microglial subclusters in the brains of MCAO mice. We also identified novel marker genes of these microglial subclusters and defined these cells as ischemic core-associated (ICAM) and ischemic penumbra-associated (IPAM) microglia, according to their spatial distribution. ICAM, induced by damage-associated molecular patterns, are probably fueled by glycolysis, and exhibit increased pro-inflammatory cytokines and chemokines production. BACH1 is a key transcription factor driving ICAM generation. In contrast, glucocorticoids, which are enriched in the penumbra, likely trigger IPAM formation, which are presumably powered by the citrate cycle and oxidative phosphorylation and are characterized by moderate pro-inflammatory responses, inflammation-alleviating metabolic features, and myelinotrophic properties.

CONCLUSIONS

ICAM could induce excessive neuroinflammation, aggravating brain injury, whereas IPAM probably exhibit neuroprotective features, which could be essential for the homeostasis and survival of cells in the penumbra. Our findings provide a biological basis for targeting specific microglial subclusters as a potential therapeutic strategy for ischemic stroke.