GEO Database Research Articles

The C/C Genotype of rs1231760 in RGS2 Is a Risk Factor for the Progression of H. pylori-Positive Atrophic Gastritis by Increasing RGS2 Expression

Background: Chronic gastritis caused by Helicobacter pylori (H. pylori) infection can progress to gastric cancer through atrophic gastritis (AG). The risk of gastric cancer increases with the progression of AG. Therefore, investigating the risk factors for the progression of AG is important. Methods: Using the GTEx and GEO databases, we extracted thirty-four candidate genes involved in the progression of AG. Then, with in silico analysis using HaploReg v4.1 and JASPAR (Matrix ID: MA0113.3), we extracted rs1231760 of RGS2 as a key single-nucleotide polymorphism (SNP) that could be involved in the functional change in the candidate gene. A correlation analysis between the selected SNP and AG in 200 H. pylori-positive and 302 H. pylori-negative participants was conducted. For functional analysis of the SNP, a dual-luciferase assay using reporter plasmids with a major or minor allele sequence was carried out. Results: The frequency of the C/C genotype of rs1231760 was higher in the AG group than in the non-AG group (p = 0.0471). Functional analysis showed that the transcriptional activities were higher at the dexamethasone-stimulating C allele than at the others (p < 0.05). Conclusions: The C/C genotype of rs1231760 in RGS2 could be a biomarker of high-risk H. pylori-positive AG because of an increase in RGS2 expression.

BHLHE40-mediated transcriptional activation of GRIN2D in gastric cancer is involved in metabolic reprogramming.

Gastric cancer (GC) is the third leading cause of death in developed countries. The reprogramming of energy metabolism represents a hallmark of cancer, particularly amplified dependence on aerobic glycolysis. Here, we aimed to illustrate the functional role of glutamate ionotropic receptor N-methyl-D-aspartate type subunit 2D (GRIN2D) in the regulation of glycolysis in GC and the mechanisms involved. Differentially expressed genes were analyzed using the GEO and GEPIA databases, followed by prognostic value prediction using the Kaplan-Meier Plotter database. The effect of GRIN2D knockdown on the malignant behavior and glycolysis of GC cells was explored. GRIN2D expression was upregulated in GC cells and promoted the malignant behavior of GC cells by activating glycolysis. Class E basic helix-loop-helix protein 40 (BHLHE40) was overexpressed in GC cells and mediated transcriptional activation of GRIN2D. The anti-tumor effects of BHLHE40 knockdown on GC cells in vitro and in vivo were reversed by GRIN2D overexpression. Knockdown of GRIN2D or BHLHE40 downregulated the expression of mRNA of electron transport chain subunits and phosphorylation of p38 MARK and inhibited calcium efflux in GC cells. Overexpression of GRIN2D promoted calcium efflux, phosphorylation of p38 MARK protein, and proliferation of GES1 cells. Altogether, the findings derived from this study suggest that BHLHE40 knockdown suppresses the growth, mobility, and glycolysis of GC cells by inhibiting GRIN2D transcription and disrupting the BHLHE40/GRIN2D axis may be an attractive therapeutic strategy for GC.

Effect of acute exercise on gene expression in peripheral blood mononuclear cells of puberty children.

Studies have shown that acute exercise alters the expression of circulating neutrophil and peripheral blood mononuclear cell (PBMC) genes, affecting a wide range of processes from cellular growth to the promotion of inflammatory and anti-inflammatory responses. In this study, we identified differential gene expression associated with acute exercise and PBMC in puberty children through the GEO database. We utilized bioinformatics technology to analyze the impact of acute exercise on PBMC and identified key genes involving the puberty children. Combining two datasets of gene expression, we identified 197 shared differentially expressed genes (DEGs) in PBMC before and after exercise. We conducted pathway analysis of these DEGs and built a gene co-expression network. The identification of hub genes was achieved through the utilization of protein-protein interaction (PPI) networks. 268 shared DEGs were identified in the GSE11761 dataset in pre- and post-exercise groups, while 352 differential genes were identified between the pre- and post-exercise groups of females in the GSE14642 dataset. A total of 197 shared genes were identified between the two datasets. Seven core genes were identified by MCODE and CytoHubba, namely IL2RB, KLRD1, GZMB, TBX21, PRF1, HAVCR2, and GZMA. Bioinformatics techniques are effective in screening and analyzing PBMC-associated DEGs in children undergoing acute pre- and post-exercise puberty. Microarray analysis of PBMCs in children undergoing puberty can identify molecules that may predict acute exercise responses. This study provides insights for further exploration of the mechanisms and targets of acute exercise on the organism.

Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods.

Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics methods. The oocyte aging datasets GSE155179 and GSE158802 were obtained from the GEO database and analyzed as the training set. The GSE164371 dataset was then defined as the validation set. Differentially expressed genes were analyzed via the limma package and weighted gene coexpression network analysis, and intersected with cellular senescence-associated genes from the Cell Senescence database. The hub genes were identified via three machine learning algorithms, namely, support vector machine recursive feature elimination, random forest, and least absolute shrinkage and selection operator logistic, which were also confirmed via the validation set. Finally, a microRNA-mRNA‒transcription factor regulatory network and single-gene gene set enrichment analysis were performed to clarify the pathogenesis of oocyte aging. A competing endogenous RNA network of GSE155179 and GSE158802 with 124 mRNAs, 31 long noncoding RNAs, and 31 miRNAs was constructed. Two modules with 814 genes were considered the key modules of oocyte aging. PDIK1L, SIRT1, and MCU were subsequently identified as hub genes; on the basis of these hub genes, a regulatory network of oocyte aging with 8 miRNAs, 3 mRNAs, and 227 TFs was ultimately constructed. This study contributes to a deeper understanding of oocyte aging and may aid in the development of therapeutic approaches to improve reproductive outcomes in older women.

Analysis of immune status and prognostic model incorporating lactic acid metabolism-associated genes.

Cancer development is intricately linked with metabolic dysregulation, including lactic acid metabolism, which plays a pivotal role in tumor progression and immune evasion. However, its specific implications in gastric adenocarcinoma (STAD) remain unclear. This study introduces a novel methodology to evaluate lactic acid metabolism comprehensively in STAD, aiming to elucidate its prognostic significance and impact on immunotherapy efficacy. Targeted therapies directed at key lactic acid metabolism genes (LMGs) identified within the tumor microenvironment (TME) hold promise for personalized treatment strategies. Lactic acid metabolism patterns were assessed in 415 STAD patients using a panel of 21 LMGs. Cox regression and Lasso regression analyses were employed to develop a predictive risk model based on differentially expressed genes (DEGs). Validation of the model was conducted using independent cohorts from the GEO and TCGA databases, as well as additional datasets focused on immunotherapy responses. Further investigations into TME dynamics of lactic acid metabolism included functional assays targeting SLC16A3, a pivotal gene identified through our analyses. Patients were stratified into distinct risk groups based on their lactic acid metabolism profiles. Low-risk patients exhibited attenuated lactic acid metabolism, correlating with favorable clinical outcomes characterized by prolonged survival and enhanced responsiveness to immunotherapy. Notably, tumor cells within the TME demonstrated heightened levels of active lactic acid metabolism, particularly impacting tumor-infiltrating lymphocytes such as CD8 + T cells and regulatory T cells. Mechanistically, SLC16A3 emerged as a critical regulator promoting STAD cell proliferation, invasion, and migration while modulating the metabolic landscape. This study underscores the prognostic value of a lactic acid metabolism-based model in STAD, providing insights into its potential as a predictive biomarker for patient stratification and therapeutic targeting. The findings highlight SLC16A3 as a promising candidate for therapeutic intervention aimed at modulating lactic acid metabolism in the TME, thereby advancing personalized treatment strategies in gastric cancer management.

GPD1L may inhibit the development of esophageal squamous cell carcinoma through the PI3K/AKT signaling pathway: bioinformatics analysis and experimental exploration.

Esophageal squamous carcinoma (ESCC) is the most prevalent pathological subtype of esophageal cancer (EC). It has the characteristics of significant local invasion, quick disease progression, high recurrence rates, and a dismal prognosis for survival. Phosphatidylinositol 3-kinase/serine-threonine kinase (PI3K/AKT) is a signaling system whose aberrant activation regulates downstream factors, leading to the promotion of cancer development. This study looks at a protein called Glycerol-3-phosphate dehydrogenase 1-like (GPD1L), which strongly affects the development of several cancers. However, its association with ESCC development and its underlying mechanisms are not clear. In this paper, we analyzed six ESCC transcriptome data obtained from the GEO database. We utilized bioinformatics technology and immunohistochemistry to differentially analyze GPD1L levels of mRNA and protein expression in ESCC and normal adjacent tissues. Furthermore, we conducted survival, co-expression, enrichment, immune infiltration and drug sensitivity analysis. Moreover, we further investigated the role and mechanism of GPD1L by Western Blot (WB), Cell Counting Kit-8 (CCK8), wound healing assay, Transwell assay, and flow cytometry. Finally, the addition of IGF-1, the activator of PI3K/AKT, could rescue the inhibitory effect of GPD1L on ESCC. The findings manifest that the expression of GPD1L was low in ESCC, and functional experiments showed that GPD1L promoted apoptosis in vitro while blocking cell migration, invasion, and proliferation. Based on mechanism research, GPD1L's impact on ESCC could be explained by its suppression of the PI3K/AKT signaling pathway's activation. To sum up, our findings imply that GPD1L may impede the initiation and advancement of ESCC via modulating the PI3K/AKT signaling pathway. GPD1L is considered to be a promising therapeutic target and biomarker to diagnose and treat ESCC.

Comprehensive analysis of molecular mechanisms underlying kidney stones: gene expression profiles and potential diagnostic markers

BackgroundThe study aimed to investigate the molecular mechanisms underlying kidney stones by analyzing gene expression profiles. They focused on identifying differentially expressed genes (DEGs), performing gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and screening optimal feature genes using various machine learning algorithms.MethodsData from the GSE73680 dataset, comprising normal renal papillary tissues and Randall’s Plaque (RP) tissues, were downloaded from the GEO database. DEGs were identified using the limma R package, followed by GSEA and WGCNA to explore functional modules. Functional enrichment analysis was conducted using KEGG and Disease Ontology. Various machine learning algorithms were used for screening the most suitable feature genes, which were then assessed for their expression and diagnostic significance through Wilcoxon rank-sum tests and ROC curves. GSEA and correlation analysis were performed on optimal feature genes, and immune cell infiltration was assessed using the CIBERSORT algorithm.Results412 DEGs were identified, with 194 downregulated and 218 upregulated genes in kidney stone samples. GSEA revealed enriched pathways related to metabolic processes, immune response, and disease states. WGCNA identified modules correlated with kidney stones, particularly the yellow module. Functional enrichment analysis highlighted pathways involved in metabolism, immune response, and disease pathology. Through machine learning algorithms, KLK1 and MMP10 were identified as optimal feature genes, significantly upregulated in kidney stone samples, with high diagnostic value. GSEA further elucidated their biological functions and pathway associations.ConclusionThe study comprehensively analyzed gene expression profiles to uncover molecular mechanisms underlying kidney stones. KLK1 and MMP10 were identified as potential diagnostic markers and key players in kidney stone progression. Functional enrichment analysis provided insights into their roles in metabolic processes, immune response, and disease pathology. These results contribute significantly to a better understanding of kidney stone pathogenesis and may inform future diagnostic and therapeutic strategies.

Lactylation Modification as a Promoter of Bladder Cancer: Insights from Multi-Omics Analysis

Bladder cancer (BLAC) is a malignant tumor with high morbidity and mortality. The establishment of a prognostic model for BLAC is of great significance for clinical prognosis prediction and treatment guidance. Lactylation modification is a newly discovered post-transcriptional modification of proteins, which is closely related to the occurrence and development of tumors. Multiple omics data of BLAC were obtained from the GEO database and TCGA database. The Lasso algorithm was used to establish a prognostic model related to lactylation modification, and its predictive ability was tested with a validation cohort. Functional enrichment analysis, tumor microenvironment analysis, and treatment response evaluation were performed on the high- and low-risk groups. Single-cell and spatial transcriptome data were used to analyze the distribution characteristics of model genes and their changes during epithelial carcinogenesis. A prognostic model consisting of 12 genes was constructed. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The multiple ROC curve showed that the prediction efficiency of the model was higher than that of the traditional clinical tumor grading. Functional enrichment analysis showed that glycolysis and hypoxia pathways were significantly upregulated in the high-risk group. The high-risk group was more sensitive to most first-line chemotherapy drugs, while the low-risk group had a better response to immunotherapy. Single-cell sequencing analysis revealed the dynamic changes of model genes during the transition of epithelial cells to squamous-differentiated cells. Spatial transcriptome analysis showed the spatial distribution characteristics of the model genes. The lactylation-related models have a satisfactory predictive ability and the potential to guide the clinical treatment of BLAC. This model has significant biological implications at the single-cell level as well as at the spatial level.

Integrated multiomics characterization reveals cuproptosis-related hub genes for predicting the prognosis and clinical efficacy of ovarian cancer

BackgroundAs a prevalent malignancy in women, ovarian cancer (OC) presents a challenge in clinical practice because of its poor prognosis and poor therapeutic efficacy. The mechanism by which cuproptosis activity is accompanied by immune infiltration in OC remains unknown. Here, we investigated cuproptosis-related OC subtypes and relevant immune landscapes to develop a risk score (RS) model for survival prediction.MethodsCuproptosis-related genes (CRGs) were identified to construct molecular subtypes via an unsupervised clustering algorithm based on the expression profiles of survival-related CRGs in the GEO database. Single-cell datasets were used to estimate immune infiltration among subtypes. The RS oriented from molecular subtypes was developed via LASSO Cox regression in the TCGA OC dataset and independently validated in the GEO and TCGA datasets. Hub markers from RS were identified in tissues and cell lines. The function of the key gene from RS was identified in vitro.ResultsWe investigated cuproptosis activity and immune infiltration to establish three clinical subtypes of OC based the differentially expressed genes (DEGs) from CRGs to create an RS model validated for clinical efficacy and prognosis. Six hub genes from the RS served as ongenic markers in OC tissues and cell lines. The function of GAS1 in the RS model revealed that it exerts oncogenic effects.ConclusionsOur study provides a novel RS model including 6 hub genes associated with cuproptosis and immune infiltration to predict OC prognosis as well as clinical efficacy.

Transcriptome analyses reveal molecular mechanisms of novel compound heterozygous ACO2 variants causing infantile cerebellar retinal degeneration

Background and purposeInfantile cerebellar retinal degeneration (ICRD) (OMIM #614559) is a rare autosomal recessive inherited disease associated with mutations in the aconitase 2 (ACO2) gene. We report a Chinese girl with novel compound heterozygous variants in ACO2, who presented at 7 months of age with psychomotor retardation, truncal hypotonia, and ophthalmologic abnormalities. This study aims to investigate the potential molecular mechanisms underlying ACO2 deficiency-induced neuropathy.MethodsWhole exome sequencing was performed on family members to screen for potential pathogenic mutations, followed by Sanger sequencing for validation. Mitochondrial aconitase activity and mitochondrial DNA (mtDNA) copy number were measured using an aconitase activity detection kit and quantitative PCR, respectively. Transcriptome expression profiles from patient cells, and cerebellar and retinal organoids retrieved from the GEO database were integrated. Functional enrichment analysis and protein-protein interaction networks were used to identify key molecules, and their expression levels were validated using Western blot analysis.ResultsGenetic testing revealed novel compound heterozygous variations in the proband's ACO2 gene (NM:001098), including c.854A>G (p.Asn285Ser) and c.1183C>T (p.Arg395Cys). Predictive analysis of the tertiary structure of the ACO2 protein suggests that both p.Asn285Ser and p.Arg395Cys affect the binding ability of ACO2 to ligands. The mitochondrial aconitase activity and mtDNA copy number in the proband's leukocytes were significantly reduced. Transcriptomic data analysis identified 80 key candidate genes involved in ACO2-related neuropathy. Among these, LRP8 and ANK3, whose gene expression levels were significantly positively correlated with ACO2, were further validated by Western blot analysis.ConclusionsThis study expands the spectrum of pathogenic ACO2 variants, elucidates the potential molecular mechanisms underlying ACO2-related neuropathy, provides in-depth support for the pathogenicity of ACO2 genetic variations, and offers new insights into the pathogenesis of ICRD.

Complement Molecule C3a Exacerbates Early Brain Injury After Subarachnoid Hemorrhage by Inducing Neuroinflammation Through the C3aR-ERK-P2X7-NLRP3 Inflammasome Signaling Axis.

An important aspect of the pathophysiology of early brain damage (EBI) after subarachnoid hemorrhage (SAH) is inflammasome-mediated neuroinflammation. It has been demonstrated that C3aR activation exacerbates neuronal damage in a number of neurological disorders. This study aims to explore the role of C3a in activating the NLRP3 inflammasome and exacerbating neuroinflammation after SAH. Preprocessing of RNA-seq transcriptome datasets using bioinformatics analysis, and screening of differentially expressed genes between SAH patients and healthy individuals from the GEO database. Internal carotid artery puncture was performed to establish SAH models in rats and mice. SAH grading, neurological scoring, brain water content, behavioral analysis, and assessments using ELISA, Western blot, immunofluorescence, and immunohistochemistry were conducted. An in vitro model of SAH was induced in BV-2 cells treated with heme (200μM). The mechanism of C3a in post-SAH neuroinflammation was studied by interfering with and inhibiting C3aR. Results showed that the expression of C3aR was upregulated in the GEO dataset (serum of SAH patients) and identified as a key differential gene in SAH. Further, elevated levels of C3a were found in the cerebrospinal fluid of clinically collected SAH patients. In the cerebral cortex and/or serum of SAH rats, expression of C3a, IL-1β, IL-6, TNF-α, CD11b, and Ki67 were significantly increased, while IL-10 was significantly decreased. Correlation analysis revealed that C3a showed negative correlation with IL-10 and positive correlation with IL-1β, IL-6, TNF-α, CD11b, and Ki67. After stimulation with heme, protein levels of C3a increased in BV-2 cells. Interfering with C3aR significantly reduced LDH release, IL-1β secretion, Caspase1 activation, levels of NLRP3 expression and ASC oligomerization, and ATP release after heme stimulation in BV-2. Subsequently, the addition of inhibitors of ERK1/2 phosphorylation demonstrated that C3a promotes ATP efflux by activating ERK1/2 phosphorylation, thereby activating P2X7. Further addition of JNJ-55308942 (a P2X7R antagonist) revealed that C3a activated the NLRP3 inflammasome via P2X7. Finally, administering SB290157 (a C3aR inhibitor) in vivo effectively alleviated brain edema, reduced mortality, improved Garcia score, ameliorated motor dysfunction, and suppressed inflammation and NLRP3 inflammasome activation in mice after SAH. Overall, C3a exacerbates EBI-associated NLRP3 inflammasome and neuroinflammation via the C3aR-ERK-P2X7 pathway after SAH. Inhibiting C3aR may serve as a one possible treatment approach to alleviate SAH after EBI.

Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is known for its aggressive nature, lack of effective diagnostic tools and treatments, and generally poor prognosis. The objective of this study was to investigate metabolic changes in TNBC using metabolomics approaches and explore the underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were first examined between 18 TNBC patients and 21 healthy control (HC) subjects using liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly differential metabolites (DMs). Subsequently, receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways-tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis-were enriched, providing insight into the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as key components. In summary, this study highlights metabolic biomarkers that could potentially be used for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data offers a validated and in-depth understanding of TNBC metabolism.

Single-cell sequencing combined with transcriptomics and invivo and invitro analysis reveals the landscape offerroptosis in myocardial ischemia-reperfusion injury.

Myocardial ischemia-reperfusion injury (MIRI) is a significant risk factor for acute myocardial infarction and is closely associated with ferroptosis. This study aimed to identify key ferroptosis-related genes as potential diagnostic markers for MIRI and to explore their roles in immune infiltration and therapeutic targeting in myocardial tissue. We obtained single-cell RNA sequencing (scRNA-seq) and RNA-seq data on MIRI from the GEO database, applied Seurat and UMAP for data processing and clustering, and analyzed ligand-receptor interactions using CellPhoneDB. By scoring ferroptosis in cardiomyocytes, we identified differentially expressed genes and conducted GO and KEGG pathway analyses. A protein interaction network was then constructed using the STRING database, and seven key genes (Atp5h, Vdac2, Pkm, Cycs, Hspa8, Tpi1, Ldha) were identified through Lasso regression modeling, showing significant associations with immune responses. Invivo experiments in a mouse ischemia-reperfusion model confirmed the roles of these seven genes in MIRI via RT-qPCR. To further investigate the role of Hspa8 in ferroptosis and MIRI, siRNA knockdown experiments were performed in H9C2 rat cardiomyocytes, and its involvement in ferroptosis was validated by JC-1 staining and PCR analysis. This study reveals the importance of ferroptosis-related genes in MIRI through integrated bioinformatics and experimental approaches, offering new insights into diagnostic markers and immune-related therapeutic targets for MIRI.

Development of a novel disulfidptosis-correlated m6A/m1A/m5C/m7G gene signature to predict prognosis and therapeutic response for lung adenocarcinoma patients by integrated machine-learning

BackgroundLung adenocarcinoma (LUAD) represents a significant global health burden, necessitating advanced prognostic tools for improved patient management. RNA modifications (m6A, m1A, m5C, m7G), and disulfidptosis, a novel cell death mechanism, have emerged as promising biomarkers and therapeutic targets in cancer.MethodsWe systematically compiled disulfidptosis-correlated genes and RNA modification-related genes from existing literature. A novel disulfidptosis-correlated m6A/m1A/m5C/m7G riskscore was computed using integrated machine-learning algorithms. Transcriptomic data from TCGA and GEO databases were downloaded analyzed. Single-cell RNA-sequencing data from the TISCH database was processed using the Seurat package. Genes’ protein–protein interaction network was constructed using the String database. Functional phenotype analysis was performed using GSVA, ClusterProfiler, and IOBR packages. Consensus clustering divided patients into two distinct groups. Drug sensitivity predictions were obtained from the GDSC1 database and predicted using the Oncopredict package.ResultsThe disulfidptosis-correlated m6A/m1A/m5C/m7G risk score effectively stratified LUAD patients into prognostically distinct groups, demonstrating superior predictive accuracy compared to conventional clinical parameters. Patients in different risk groups exhibited significant molecular and clinical differences. Subsequent analyses identified two molecular subtypes associated with RNA modification and disulfidptosis, revealing differences in immune infiltration and prognosis. Functional enrichment analyses highlighted pathways involving RNA modification and disulfidptosis, underscoring their roles in LUAD pathogenesis. Single-cell analysis revealed distinct features between high- and low-risk status cells.ConclusionThis study introduces a novel disulfidptosis-correlated m6A/m1A/m5C/m7G risk score as a robust prognostic tool for LUAD, integrating insights from RNA modifications and cell death mechanisms. The risk score enhances prognostic stratification and identifies potential targets for personalized therapeutic strategies in LUAD. This comprehensive approach emphasizes the critical roles of RNA modifications and disulfidptosis in LUAD biology, paving the way for future research and clinical applications aimed at improving patient outcomes.

MiR-146a-5p-enriched exosomes inhibit M1 macrophage activation and inflammatory response by targeting CD80.

Previous studies have demonstrated that miR-146a-5p negatively regulated the intrinsic immune and inflammatory responses, whether the miR-146a-5p-enriched exosomes possess the anti-inflammation effect remains unclear. This study aimed to investigate the effect of miR-146a-5p-enriched exosomes on M1 macrophage activation and inflammatory response and the potential molecular mechanism. GEO database was used to analyze the expression of miR-146a-5p in serum exosomes of MASH patients. MiR-146a-5p levels in primary hepatocytes, macrophages, and serum exosomes of MASH mice were measured. MiR-146a-5p-enriched exosomes were constructed and the effects on M1 macrophages activation and inflammatory factors release were investigated. The target gene of miR-146a-5p was predicted and verified. Serum exosomal miR-146a-5p level was decreased in MASH patients analyzed by GEO database. The miR-146a-5p levels in primary cultured hepatocytes and macrophages of MASH mice were decreased. Serum exosomal miR-146a-5p level was decreased and negatively correlated with the concentrations of IL-6 in MASH mice. Furthermore, miR-146a-5p-enriched exosomes inhibited the M1 macrophages activation and the expression of pro-inflammatory factors MCP-1, IL-6, and TNF-α. CD80 was predicted as the potential target gene of miR-146a-5p, and the expression of CD80 was regulated by miR-146a-5p. In addition, the inhibitory effect of miR-146a-5p on M1 macrophages activation and inflammatory factors release was restored when CD80 was over-expressed. This study demonstrated that miR-146a-5p-enriched exosomes can inhibit the M1 macrophages activation and reduce the release of pro-inflammatory factors by targeting CD80.

ScRNA-seq and bulk RNA-seq identified NUPR1 as novel biomarkers related to CD4 + T cells infiltration for abdominal aortic aneurysm.

Developing a molecular signature associated with CD4 + T cell infiltration is essential for identifying biomarkers in abdominal aortic aneurysms (AAA). Establishing such a signature is vital for improving diagnostic accuracy and therapeutic strategies for AAA. This study focuses on CD4 + T cells, which are pivotal in the immune microenvironment of AAA, to pinpoint key targets. We identified CD4 + T cell-related biomarkers in AAA using bulk and single-cell RNA sequencing data from the GEO database. We employed CIBERSORT to assess immune cell infiltration and applied weighted gene co-expression network analysis and differential expression analyses to pinpoint key genes. A nomogram and related score were developed based on these genes. Single-cell RNA sequencing further analyzed their expression across cell types, and KEGG/GO analyses were conducted for candidate genes. Four genes (NUPR1, CCL4L2, CCL3L3, MMP9) were identified finally. Validation via qPCR and immunohistochemistry showed NUPR1 downregulation in aneurysms and an inverse relationship with CD4 + T cells infiltration. Immunofluorescence results indicated NUPR1 was mainly expressed in the cytoplasm of vascular smooth muscle cells (VSMCs). After VSMCs-specific overexpression of NUPR1 via adeno-associated virus, the AAA diameter decreased, while treatment with the NUPR1 nuclear translocation inhibitor ZZW-115 hydrochloride had no effect on AAA size. Overexpression of NUPR1 in VSMCs suppresses the migration of CD4 + T cells. The four-gene signature accurately predicts CD4 + T cell infiltration in AAA patients and may serve as a clinical index. NUPR1 could be a therapeutic target for the interaction between CD4 + T cells and AAA.

Identification of coagulation-related genes as potential diagnostic biomarkers for pediatric septic shock

This study aimed to identify important clotting associated genes (CRGs) associated with septic shock in children and explore possible important mechanisms of the disease. Five hub genes with diagnostic performance were identified using GEO database and data from literature. These hub genes have strong correlation with immune cells. ceRNA network was constructed to explore potential pathogenic mechanisms. Ten candidate small molecule compounds were identified. In summary, the hub genes may play an important role in the immunity and disease development of septic shock, providing new ideas and strategies for future diagnosis and mechanism evaluation of children with septic shock.

Histone Lysine Lactylation (Kla)-induced BCAM Promotes OSCC Progression and Cis-Platinum Resistance.

Histone lysine lactylation (Kla) plays a vital role in cancer progression. However, the prognostic value of histone Kla in oral squamous cell carcinoma (OSCC) remains unclear. OSCC RNA expression data were obtained from the TCGA and GEO databases. We explored the prognostic value of histone Kla in OSCC via constructing a Cox model and determined the role of Kla in OSCC drug susceptibility and tumor microenvironment (TME). In addition, the biological roles of candidate biomarkers were identified. A total of 1223 Kla-specific genes were obtained, with 228 DEKlaGs in OSCC. GO and KEGG enrichment analyses suggested that DEKlaGs contributed to inflammatory and cancer progression. A Cox model in accordance with BCAM, CGNL1, DGKG, and OLR1 could predict OSCC patient prognosis accurately. Subsequently, Kla-induced prognostic genes were identified to play a crucial role in OSCC drug therapy and TME. Moreover, BCAM was identified as a biomarker that promoted OSCC invasion, angiogenesis, and chemotherapy resistance. Kla was identified to be associated with OSCC prognosis, drug therapy, and TME. Histone Kla-induced BCAM was identified to play a crucial role during OSCC progression, suggesting that Kla is promising to be a novel therapeutic target for OSCC.

Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning

BackgroundNon-alcoholic fatty liver disease (NAFLD) and ulcerative colitis (UC) are two common health issues that have gained significant global attention. Previous studies have suggested a possible connection between NAFLD and UC, but the underlying pathophysiology remains unclear. This study investigates common genes, underlying pathogenesis mechanisms, identification of diagnostic markers applicable to both conditions, and exploration of potential therapeutic targets shared by NAFLD and UC.MethodsWe obtained datasets for NAFLD and UC from the GEO database. The DEGs in the GSE89632 dataset of the NAFLD and GSE87466 of the UC dataset were analyzed. WGCNA, a powerful tool for identifying modules of highly correlated genes, was employed for both datasets. The DEGs of NAFLD and UC and the modular genes were then intersected to obtain shared genes. Functional enrichment analysis was conducted on these shared genes. Next, we utilize the STRING database to establish a PPI network. To enhance visualization, we employ Cytoscape software. Subsequently, the Cytohubba algorithm within Cytoscape was used to identify central genes. Diagnostic biomarkers were initially screened using LASSO regression and SVM methods. The diagnostic value of ROC curve analysis was assessed to detect diagnostic genes in both training and validation sets for NAFLD and UC. A nomogram was also developed to evaluate diagnostic efficacy. Additionally, we used the CIBERSORT algorithm to explore immune infiltration patterns in both NAFLD and UC samples. Finally, we investigated the correlation between hub gene expression, diagnostic gene expression, and immune infiltration levels.ResultsWe identified 34 shared genes that were found to be associated with both NAFLD and UC. These genes were subjected to enrichment analysis, which revealed significant enrichment in several pathways, including the IL-17 signaling pathway, Rheumatoid arthritis, and Chagas disease. One optimal candidate gene was selected through LASSO regression and SVM: CCL2. The ROC curve confirmed the presence of CCL2 in both the NAFLD and UC training sets and other validation sets. This finding was further validated using a nomogram in the validation set. Additionally, the expression levels of CCL2 for NAFLD and UC showed a significant correlation with immune cell infiltration.ConclusionThis study identified a gene (CCL2) as a biomarker for NAFLD and UC, which may actively participate in the progression of NAFLD and UC. This discovery holds significant implications for understanding the progression of these diseases and potentially developing more effective diagnostic and treatment strategies.

SARS-CoV-2 nucleocapsid protein interaction with YBX1 displays oncolytic properties through PKM mRNA destabilization

BackgroundSARS-CoV-2, a highly contagious coronavirus, is responsible for the global pandemic of COVID-19 in 2019. Currently, it remains uncertain whether SARS-CoV-2 possesses oncogenic or oncolytic potential in influencing tumor progression. Therefore, it is important to evaluate the clinical and functional role of SARS-CoV-2 on tumor progression.MethodsHere, we integrated bioinformatic analysis of COVID-19 RNA-seq data from the GEO database and performed functional studies to explore the regulatory role of SARS-CoV-2 in solid tumor progression, including lung, colon, kidney and liver cancer.ResultsOur results demonstrate that infection with SARS-CoV-2 is associated with a decreased expression of genes associated with cancer proliferation and metastasis in lung tissues from patients diagnosed with COVID-19. Several cancer proliferation or metastasis related genes were frequently downregulated in SARS-CoV-2 infected intestinal organoids and human colon carcinoma cells. In vivo and in vitro studies revealed that SARS-CoV-2 nucleocapsid (N) protein inhibits colon and kidney tumor growth and metastasis through the N-terminal (NTD) and the C-terminal domain (CTD). The molecular mechanism indicates that the N protein of SARS-CoV-2 interacts with YBX1, resulting in the recruitment of PKM mRNA into stress granules mediated by G3BP1. This process ultimately destabilizes PKM expression and suppresses glycolysis.ConclusionOur study reveals a new function of SARS-CoV-2 nucleocapsid protein on tumor progression.