Intersection Of Differentially Expressed Genes Research Articles

Identification of immune-related hub genes in chronic obstructive pulmonary disease

ObjectiveAs a prevalent persistent respiratory disease, chronic obstructive pulmonary disease (COPD) is featured by airflow limitation and chronic inflammation. This study focused on the identification of immune-related hub genes in COPD. MethodsWe employed the GSE38974 dataset to analyze differentially expressed genes (DEGs) of COPD. Then, we obtained COPD immune-related DEGs (COPD-IMDEGs) based on the intersection of DEGs and immune-related genes. Subsequently, we carried out Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses on COPD-IMDEGs. We established a protein-protein interaction network based on COPD-IMDEGs. The hub genes were determined by utilizing the Maximal Clique Centrality method. We utilized receiver operating characteristic (ROC) curves to analyze the clinical significance of hub genes in COPD. In addition, potential drugs targeting hub genes were predicted based on interactions between hub gene-corresponding proteins and drugs. ResultsA total of 45 COPD-IMDEGs were obtained through differential analysis. Enrichment analyses showed that COPD-IMDEGs were associated with cytokines, growth factors, and receptor ligands. Ten COPD-IMDEGs were identified as hub genes. As shown by ROC curves, these genes had potential value in identifying COPD patients. Drug prediction results showed that simvastatin and other drugs targeted hub genes. ConclusionThis study analyzed the potential biological functions enriched by COPD-IMDEGs, identified ten genes as biological markers for diagnosing COPD, and predicted potential drugs for treating COPD.

Identification and RT-qPCR Validation of Biomarkers Based on Butyrate Metabolism-Related Genes to Predict Recurrent Miscarriage.

To date, the cause of recurrent miscarriage (RM) in at least 50% of patients remains unknown. However, no study has explored the correlation between butyrate metabolism-related genes (BMRGs) and RM. RM-related datasets (GSE165004, GSE111974, GSE73025, and GSE179996) were obtained from the Gene Expression Omnibus (GEO) database. First, 595 differentially expressed genes (DEGs) were identified between the RM and control samples in GSE165004. Subsequently, 213 differentially expressed BMRGs (DE-BMRGs) were identified by considering the intersection of DEGs with BMRGs. The protein-protein interaction (PPI)network of DE-BMRGs contained 156 nodes and 250 edges, and a key module was obtained. In total, four biomarkers (ACTR2, ANXA2, PFN1, and OAS1) were acquired through least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF). Immune analysis revealed two immune cells and three immune-related gene sets that were significantly different between the RM and control groups, namely, T helper cells, regulatory T cells (Treg), MHC class I, parainflammation, and type I IFN response. In addition, a TF-mRNA network based on the top 100 nodes ranked in the order of connectivity was created, including 100 nodes and 253 edges, such as MTERF2-ACTR2, NKX23-PFN1, STAT1-OAS1, and SP100-ANXA2. Finally, 3 drugs (withaferin A, N-ethylmaleimide, and etoposide) were predicted to interact with 2 biomarkers (ANXA2 and ACTR2). Eventually, ANXA2 and OAS1 were significantly downregulated, and PFN1 was markedly overexpressed in the RM group, as determined by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Our findings authenticated four butyrate metabolism-related biomarkers for the diagnosis of RM, providing a scientific reference for further studies on RM treatment.

Investigation of the Mechanism of SEMA5A and Its Associated Autophagy-Related Genes in Gastric Cancer.

Semaphorin 5A (SEMA5A) and autophagy-related genes (ARGs) are pivotal in the pathogenesis of gastric cancer (GC). However, the potential regulatory role of SEMA5A in autophagy via its associated ARGs and the underlying molecular mechanisms remain unresolved. GC-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify differentially expressed genes (DEGs) between GC and control samples. The intersection of DEGs with ARGs produced candidate genes, which were further analyzed using Spearman correlation with SEMA5A to identify signature genes. Stratification of GC samples based on signature gene expression, followed by Kaplan-Meier survival analysis, identified key genes. Subsequent analyses, including gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint evaluation, were conducted on the key genes and SEMA5A. The mRNA expression level was quantified using real-time quantitative polymerase chain reaction (RT-qPCR). Ninety candidate genes were identified for Spearman correlation with SEMA5A, revealing TNFSF11, BMP6, ITPR1, and DLC1 with correlation coefficients exceeding 0.3. Survival analysis underscored DLC1 and BMP6 as key genes due to significant prognostic differences. GSEA implicated SEMA5A, BMP6, and DLC1 in the ECM receptor interaction pathway. Immune infiltration analysis indicated a negative correlation of SEMA5A and BMP6 with M1 macrophages, while DLC1 exhibited the strongest association with the immune checkpoint PDCD1LG2 (p < 0.05, cor = 0.43). The mRNA expression level of SEMA5A was significantly upregulated in AGS parental cells compared to GES-1 cells (p < 0.01), whereas DLC1 and BMP6 mRNA levels were markedly downregulated in AGS parental cells relative to GES-1 (p < 0.0001). ARGs BMP6 and DLC1, associated with SEMA5A, were identified, and their prognostic significance in GC was demonstrated. Additionally, their regulatory mechanisms were further elucidated through immune infiltration analysis and molecular network construction, providing a theoretical foundation for future research on the molecular mechanisms in patients with GC.

Fe3O4@R837 Nanoplatform Enhances Chemical Dynamic Therapy and Immunotherapy: Integrated Transcriptomic Analysis Reveals Key Genes in Breast Cancer Prognosis.

Breast cancer represents a substantial contributor to mortality rates among women with cancer. Chemical dynamic therapy is a promising anticancer strategy that utilizes the Fenton reaction to transform naturally occurring hydrogen peroxide (H2O2) into hydroxyl radicals (•OH). Additionally, cancer immunotherapy using immune drugs, such as imiquimod (R837), has shown promise in activating T cells to kill tumor cells. In this study, we proposed a Fe3O4@R837 smart nanoplatform that can trigger the Fenton reaction and induce immune responses in breast cancer treatment. Furthermore, we performed transcriptome sequencing on breast cancer samples and used the R package (limma) to analyze differential expression profiles and select differentially expressed genes (DEGs). We obtained clinical information and RNA expression matrix data from The Cancer Genome Atlas database to perform survival analysis and identify prognostic-related genes (PRGs) and molecular subtypes with distinct prognoses. We used the TIMER 2.0 web and other methods to determine the tumor immune microenvironment and immune status of different prognostic subtypes. We identified DPGs by taking the intersection of DEGs and PRGs and performed functional analyses, including gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, to elucidate potential mechanisms. Subsequently, we constructed a protein-protein interaction network using the STRING database to visualize the interactions between the DPGs. We screened hub genes from the DPGs using the Cytoscape plugin and identified six hub genes: CD3E, GZMK, CD27, SH2D1A, ZAP70, and TIGIT. Our results indicate that these six key genes regulate immune cell recruitment to increase T-cell cytotoxicity and kill tumors. Targeting these key genes can enhance immunotherapy and improve the breast cancer prognosis.

Identification of a ferroptosis-related gene signature for the prognosis of pediatric neuroblastoma.

Ferroptosis-related genes are correlated with the prognosis of patients with neuroblastoma (NB) remains unknown. This study aims to establish a prognostic ferroptosis-related gene model for predicting prognostic value in pediatric NB patients. The gene expression array and clinical characteristics of NB were downloaded from a public database. Correlations between ferroptosis-related genes and drug responses were analyzed by Childhood Cancer Therapeutics. The prognostic model was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and was validated in NB patients from the ICGC cohort. The survival analysis was performed by Cox regression analysis. single-sample gene set enrichment analysis (ssGSEA) was used to quantify the immune cell infiltration correlation. Overall, 70 genes were identified as ferroptosis-related differentially expressed genes (DEGs) from 247 samples. Then, 13 ferroptosis-related genes were correlated with OS in the univariate Cox regression analysis. Five prognostic ferroptosis-related DEGs (pFR-DEGs) (STEAP3, MAP1LC3A, ULK2, MTOR and TUBE1), which were defined as the intersection of DEGs and prognostic ferroptosis-related genes, were identified and utilized to construct the prognostic signature. The correlation between five pFR-DEGs and drug responses was analyzed, and the box plots indicated that MTOR gene expression was highest, suggesting that MTOR expression is related to progressive NB disease. The receiver operating characteristic (ROC) curve showed that the model had moderate predictive power. The survival analysis indicated that the high-risk group had poor overall survival (OS) (P=2.087×10-06). Univariate and multivariate analyses identified the risk score as a significant prognostic risk factor [P=0.003, hazard ratio (HR) =1.933]. Immune cell infiltration correlation analysis showed that the high-risk group was related to more immune cells. The present study indicated a difference in ferroptosis-related gene expression between low- and high-risk NB patients. The ferroptosis-related signature could serve as a prognostic prediction tool. Additionally, immune infiltration might play an important role in different risk groups for NB patients.

Bioinformatics analyses and experimental validation of ferroptosis-related genes in bronchopulmonary dysplasia pathogenesis.

We aimed to study the involvement of ferroptosis in the pathogenesis of bronchopulmonary dysplasia (BPD) by conducting bioinformatics analyses and identifying and validating the associated ferroptosis-related genes to explore new directions for treating BPD. The dataset GSE32472 on BPD was downloaded from the public genome database. Using R language, differentially expressed genes (DEGs) between the BPD and normal group were screened. In the present study, we adopted weighted gene correlation network analysis (WGCNA) for identifying BPD-related gene modules and ferroptosis-related genes were extracted from FerrDb. Their results were intersected to obtain the hub genes. After that, to explore the hub gene-related signaling pathways, the hub genes were exposed to gene ontology enrichment analysis. With the purpose of verifying the mRNA expression of the hub genes, a single-gene gene set enrichment analysis and quantitative reverse transcription polymerase chain reaction were conducted. Immune cell infiltration in BPD was analyzed using the CIBERSORT inverse fold product algorithm. A total of 606 DEGs were screened. WGCNA provided the BPD-related gene module darkgreen4. The intersection of DEGs, intramodular genes, and ferroptosis-related genes revealed six ferroptosis-associated hub genes (ACSL1, GALNT14, WIPI1, MAPK14, PROK2, and CREB5). Receiver operating characteristic curve analysis demonstrated that the hub genes screened for BPD were of good diagnostic significance. According to the results of immune infiltration analysis, the proportions of CD8, CD4 naive, and memory resting T cells and M2 macrophage were elevated in the normal group, and the proportions of M0 macrophage, resting mast cell, and neutrophils were increased in the BPD group. A total of six ferroptosis-associated hub genes in BPD were identified in this study, and they may be potential new therapeutic targets for BPD.

A comprehensive analysis of m6A/m7G/m5C/m1A-related gene expression and immune infiltration in liver ischemia–reperfusion injury by integrating bioinformatics and machine learning algorithms

BackgroundLiver ischemia–reperfusion injury (LIRI) is closely associated with immune infiltration, which commonly occurs after liver surgery, especially liver transplantation. Therefore, it is crucial to identify the genes responsible for LIRI and develop effective therapeutic strategies that target immune response. Methylation modifications in mRNA play various crucial roles in different diseases. This study aimed to identify potential methylation-related markers in patients with LIRI and evaluate the corresponding immune infiltration.MethodsTwo Gene Expression Omnibus datasets containing human liver transplantation data (GSE12720 and GSE151648) were downloaded for integrated analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to investigate the functional enrichment of differentially expressed genes (DEGs). Differentially expressed methylation-related genes (DEMRGs) were identified by overlapping DEG sets and 65 genes related to N6-methyladenosine (m6A), 7-methylguanine (m7G), 5-methylcytosine (m5C), and N1-methyladenosine (m1A). To evaluate the relationship between DEMRGs, a protein–protein interaction (PPI) network was utilized. The core DEMRGs were screened using three machine learning algorithms: least absolute shrinkage and selection operator, random forest, and support vector machine-recursive feature elimination. After verifying the diagnostic efficacy using the receiver operating characteristic curve, we validated the expression of the core DEMRGs in clinical samples and performed relative cell biology experiments. Additionally, the immune status of LIRI was comprehensively assessed using the single sample gene set enrichment analysis algorithm. The upstream microRNA and transcription factors of the core DEMRGs were also predicted.ResultsIn total, 2165 upregulated and 3191 downregulated DEGs were identified, mainly enriched in LIRI-related pathways. The intersection of DEGs and methylation-related genes yielded 28 DEMRGs, showing high interaction in the PPI network. Additionally, the core DEMRGs YTHDC1, METTL3, WTAP, and NUDT3 demonstrated satisfactory diagnostic efficacy and significant differential expression and corresponding function based on cell biology experiments. Furthermore, immune infiltration analyses indicated that several immune cells correlated with all core DEMRGs in the LIRI process to varying extents.ConclusionsWe identified core DEMRGs (YTHDC1, METTL3, WTAP, and NUDT3) associated with immune infiltration in LIRI through bioinformatics and validated them experimentally. This study may provide potential methylation-related gene targets for LIRI immunotherapy.

Identification of oxidative phosphorylation-related genes in moyamoya disease by combining bulk RNA-sequencing analysis and machine learning.

Introduction: Moyamoya disease (MMD) is a chronic cerebrovascular disease that can lead to ischemia and hemorrhagic stroke. The relationship between oxidative phosphorylation (OXPHOS) and MMD pathogenesis remains unknown. Methods: The gene expression data of 60 participants were acquired from three Gene Expression Omnibus (GEO) datasets, including 36 and 24 in the MMD and control groups. Differentially expressed genes (DEGs) between MMD patients MMD and control groups were identified. Machine learning was used to select the key OXPHOS-related genes associated with MMD from the intersection of DEGs and OXPHOS-related gene sets. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), gene set enrichment analysis (GSEA), Immune infiltration and microenvironments analysis were used to analyze the function of key genes. Machine learning selected four key OXPHOS-related genes associated with MMD: CSK, NARS2, PTPN6 and SMAD2 (PTPN6 was upregulated and the other three were downregulated). Results: Functional enrichment analysis showed that these genes were mainly enriched in the Notch signaling pathway, GAP junction, and RNA degradation, which are related to several biological processes, including angiogenesis, proliferation of vascular smooth muscle and endothelial cells, and cytoskeleton regulation. Immune analysis revealed immune infiltration and microenvironment in these MMD samples and their relationships with four key OXPHOS-related genes. APC co-inhibition (p = 0.032), HLA (p = 0.001), MHC I (p = 0.013), T cellco- inhibition (p = 0.032) and Type I IFN responses (p < 0.001) were significantly higher in the MMD groups than those in the control groups. The CSK positively correlated with APC co-inhibition and T cell-co-inhibition. The NARS2 negatively correlated with Type I IFN response. The SMAD2 negatively correlated with APC co-inhibition and Type I IFN response. The PTPN6 positively correlated with HLA, MHC I and Type I IFN responses. Discussion: This study provides a comprehensive understanding of the role of OXPHOS in MMD and will contribute to the development of new treatment methods and exploration of MMD pathogenesis.

Prediction of the survival status and tumor microenvironment in colorectal cancer through genotyping analysis based on toll-like receptors.

Colorectal cancer (CRC) ranks third in both the incidence and mortality rates among male and female cancers, and it is the leading digestive system cancer. Due to the inter- and intratumor heterogeneity of cancer, the TNM system is insufficient for predicting prognosis, necessitating the use of molecular biomarkers for prognostic prediction. Toll-like receptors (TLRs) have been associated with CRC survival rates. This study focused on the investigation of the role and potential value of TLRs in CRC genotyping to aid in immunotherapy for CRC patients. Differential gene expression analysis was performed on CRC transcriptomic data from The Cancer Genome Atlas database. TLRs were referred from the literature, and their intersection with differentially expressed genes (DEGs) in CRC yielded TLR-DEGs. The expression patterns of TLR-DEGs were predicted using the STRING website, and copy number variations of TLR-DEGs were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on TLR-DEGs. ConsensusClusterPlus R package was used for clustering CRC patients, and ESTIMATE and GSEAbase were employed to analyze immune characteristics of different subtypes. Immune phenotyping scores and tumor immune dysfunction and exclusion scores were evaluated. DEGs of different subtypes were analyzed, followed by GO and KEGG enrichment analyses, the protein-protein interaction (PPI) network analysis, and further selection of hub genes. The sensitivity of drugs was assessed using the identified hub genes. We identified 37 TLR-DEGs, and the PPI analysis revealed their coexpression, although they were distributed on different chromosomes. Enrichment analyses indicated that the 37 TLR-DEGs were linked to cancer cell immune response. Based on these TLR-DEGs, CRC patients were classified into three subtypes. Cluster2 exhibited lower survival rates and higher immune infiltration levels and predicted poorer response to immune checkpoint inhibitor therapy. The intersection of DEGs from cluster2 and cluster1 with DEGs from cluster2 and cluster3 yielded a set of 426 commonly shared DEGs. Enrichment analyses revealed that these shared DEGs might regulate immune cell viability. Eight common hub genes for different subtypes were further identified to predict drug-related correlations. The developed TLR genotyping was used to predict the survival status and tumor microenvironment of CRC, providing a foundation for understanding the molecular mechanisms of TLR signaling and deepening its clinical significance.

Deciphering the Genetic Links between Psychological Stress, Autophagy, and Dermatological Health: Insights from Bioinformatics, Single-Cell Analysis, and Machine Learning in Psoriasis and Anxiety Disorders.

The relationship between psychological stress, altered skin immunity, and autophagy-related genes (ATGs) is currently unclear. Psoriasis is a chronic skin inflammation of unclear etiology that is characterized by persistence and recurrence. Immune dysregulation and emotional disturbances are recognized as significant risk factors. Emerging clinical evidence suggests a possible connection between anxiety disorders, heightened immune system activation, and altered skin immunity, offering a fresh perspective on the initiation of psoriasis. The aim of this study was to explore the potential shared biological mechanisms underlying the comorbidity of psoriasis and anxiety disorders. Psoriasis and anxiety disorders data were obtained from the GEO database. A list of 3254 ATGs was obtained from the public database. Differentially expressed genes (DEGs) were obtained by taking the intersection of DEGs between psoriasis and anxiety disorder samples and the list of ATGs. Five machine learning algorithms used screening hub genes. The ROC curve was performed to evaluate diagnostic performance. Then, GSEA, immune infiltration analysis, and network analysis were carried out. The Seurat and Monocle algorithms were used to depict T-cell evolution. Cellchat was used to infer the signaling pathway between keratinocytes and immune cells. Four key hub genes were identified as diagnostic genes related to psoriasis autophagy. Enrichment analysis showed that these genes are indeed related to T cells, autophagy, and immune regulation, and have good diagnostic efficacy validated. Using single-cell RNA sequencing analysis, we expanded our understanding of key cellular participants, including inflammatory keratinocytes and their interactions with immune cells. We found that the CASP7 gene is involved in the T-cell development process, and correlated with γδ T cells, warranting further investigation. We found that anxiety disorders are related to increased autophagy regulation, immune dysregulation, and inflammatory response, and are reflected in the onset and exacerbation of skin inflammation. The hub gene is involved in the process of immune signaling and immune regulation. The CASP7 gene, which is related with the development and differentiation of T cells, deserves further study. Potential biomarkers between psoriasis and anxiety disorders were identified, which are expected to aid in the prediction of disease diagnosis and the development of personalized treatments.

Integrative Analyses of Bulk and Single-Cell RNA Seq Identified the Shared Genes in Acute Respiratory Distress Syndrome and Rheumatoid Arthritis.

Acute respiratory distress syndrome (ARDS), a progressive status of acute lung injury (ALI), is primarily caused by an immune-mediated inflammatory disorder, which can be an acute pulmonary complication of rheumatoid arthritis (RA). As a chronic inflammatory disease regulated by the immune system, RA is closely associated with the occurrence and progression of respiratory diseases. However, it remains elusive whether there are shared genes between the molecular mechanisms underlying RA and ARDS. The objective of this study is to identify potential shared genes for further clinical drug discovery through integrated analysis of bulk RNA sequencing datasets obtained from the Gene Expression Omnibus database, employing differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). The hub genes were identified through the intersection of common DEGs and WGCNA-derived genes. The Random Forest (RF) and least absolute shrinkage and selection operator (LASSO) algorithms were subsequently employed to identify key shared target genes associated with two diseases. Additionally, RA immune infiltration analysis and COVID-19 single-cell transcriptome analysis revealed the correlation between these key genes and immune cells. A total of 59 shared genes were identified from the intersection of DEGs and gene clusters obtained through WGCNA, which analyzed the integrated gene matrix of ALI/ARDS and RA. The RF and LASSO algorithms were employed to screen for target genes specific to ALI/ARDS and RA, respectively. The final set of overlapping genes (FCMR, ADAM28, HK3, GRB10, UBE2J1, HPSE, DDX24, BATF, and CST7) all exhibited a strong predictive effect with an area under the curve (AUC) value greater than 0.8. Then, the immune infiltration analysis revealed a strong correlation between UBE2J1 and plasma cells in RA. Furthermore, scRNA-seq analysis demonstrated differential expression of these nine target genes primarily in T cells and NK cells, with CST7 showing a significant positive correlation specifically with NK cells. Beyond that, transcriptome sequencing was conducted on lung tissue collected from ALI mice, confirming the substantial differential expression of FCMR, HK3, UBE2J1, and BATF. This study provides unprecedented evidence linking the pathophysiological mechanisms of ALI/ARDS and RA to immune regulation, which offers novel understanding for future clinical treatment and experimental research.

Mechanisms of Ferroptosis-Related Genes in Gallbladder Cancer Based on Bioinformatics Analysis.

Gallbladder Cancer (GBC) is a lethal malignancy with limited treatment options and poor prognosis. Recent studies have emphasized the role of ferroptosis, a regulated form of cell death, in various cancers, including GBC. We applied bioinformatics methodologies on four GBC datasets to identify differentially expressed genes (DEGs). An intersection of DEGs from the four datasets with ferroptosis and GBC-associated genes was done to identify key ferroptosis-related genes in GBC. GSVA pathway enrichment analysis and immune cell infiltration assessment were conducted to explore their functional roles and interactions. Seven ferroptosis-related genes, EZH2, MUC1, PVT1, GOT1, CDO1, LIFR, and TFAP2A, were identified to be related to GBC. These genes were associated with vital signaling pathways like the G2/M checkpoint and DNA repair and showed significant correlations with immune cell infiltration in GBC. Receiver Operating Characteristic (ROC) curve analysis revealed their high diagnostic potential, with Area Under the Curve (AUC) values ranging from 0.796 to 0.953. Our findings underscore the pivotal role of ferroptosis in GBC and the potential of ferroptosis-related genes as diagnostic biomarkers. This study lays a foundation for further research into ferroptosis-based therapeutic strategies for GBC.

Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment

BACKGROUND The cognitive impairment in type 2 diabetes mellitus (T2DM) is a multifaceted and advancing state that requires further exploration to fully comprehend. Neuroinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease. AIM To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment. METHODS To identify differentially expressed genes (DEGs) between T2DM and controls, we used data from the Gene Expression Omnibus database GSE125387. To identify T2DM module genes, we used Weighted Gene Co-Expression Network Analysis. All the genes were subject to Gene Set Enrichment Analysis. Protein-protein interaction network construction and machine learning were utilized to identify three hub genes. Immune cell infiltration analysis was performed. The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis. Validation experiments including reverse transcription quantitative real-time PCR, Western blotting and immunohistochemistry were conducted both in vivo and in vitro . To identify potential drugs associated with hub genes, we used the Comparative Toxicogenomics Database (CTD). RESULTS A total of 576 DEGs were identified using GSE125387. By taking the intersection of DEGs, T2DM module genes, and immune-related genes, a total of 59 genes associated with the immune system were identified. Afterward, machine learning was utilized to identify three hub genes (H2-T24 , Rac3 , and Tfrc ). The hub genes were associated with a variety of immune cells. The three hub genes were validated in GSE152539. Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro , consistent with the bioinformatics analysis. Additionally, 11 potential drugs associated with RAC3 and TFRC were identified based on the CTD. CONCLUSION Immune-related genes that differ in expression in the hippocampus are closely linked to microglia. We validated the expression of three hub genes both in vivo and in vitro , consistent with our bioinformatics results. We discovered 11 compounds associated with RAC3 and TFRC . These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.

Role of Radiation-related Lung Function Genes in Prognosis and Immune Infiltration of Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a common malignant tumor with no obvious clinical symptoms in its early stages. Patients can be divided into radiotherapysensitive groups (RS) and radiotherapy-resistant groups (RR) due to their varying conditions. The therapeutic effect of radiotherapy is quite different between the two groups. Therefore, this paper explores the role of radiation-related lung function genes in LUAD and its immune landscape. Firstly, we divided LUAD samples from the TCGA cohort into RS and RR groups and analyzed differential expression to obtain differentially expressed genes (DEGs). Then, DEGs and patients' grouping information were input into the weighted co-expression network, and the genes in the radiotherapy-related modules were identified. Furthermore, after the intersection of DEGs and lung function-related genes, the prognosis-related genes were obtained through univariate Cox and Lasso-Cox analyses, respectively, and the risk model was constructed. Finally, the differences in prognosis and immunity of the samples in the risk model were explored. Additionally, we also performed a qPCR experiment on lung function-related genes. In this paper, radiation-related genes of LUAD were identified through a series of bioinformatics analyses. By conducting enrichment analysis on these genes, several pathways related to LUAD radiation were identified, and DEGs associated with significant prognosis were determined. Furthermore, a radiation-related risk model of LUAD was developed. All samples were divided into high-risk and low-risk groups based on the risk score, and the differences in immune cell infiltration abundance and immune function between these groups were evaluated. The qPCR experimental results demonstrated a significant difference in the expression of genes related to lung function. The prognosis-related genes identified in this paper and the risk model created can serve as a reference for diagnosing and treating LUAD.

Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes

BackgroundOsteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes.MethodsWe obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website.ResultsWe identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified.Conclusion4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.

Identification and validation of ferroptosis-related lncRNA signature in intervertebral disc degeneration

Low back pain influences people of every age and is one of the major contributors to the global cost of illness. Intervertebral disc degeneration (IVDD) is a major contributor to low back pain, but its pathogenesis is unknown. Recently, ferroptosis has been shown to have a substantial role in modulating IVDD progression. However, the function of ferroptosis-related long non-coding RNAs (lncRNAs) has rarely been reported in IVDD. Consequently, the research was conducted to explore the ferroptosis-related lncRNA signature in the IVDD occurrence and development. We analyzed two datasets (GSE167199 and GSE167931) archived in the NCBI Gene Expression Omnibus (GEO) public database. We screened differentially expressed genes (DEGs) and differentially expressed lncRNAs (DELncs) in these datasets using the limma package. Ferroptosis-related genes (FRGs) were derived from the FerrDb V2 website and the intersection of DEGs and FRGs was considered as differentially expressed ferroptosis-related genes (DFGs). These genes were then subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Correlations between DFGs and DELncs were shown by Pearson test to determine differential expression of ferroptosis-related lncRNAs. The Pearson test showed that CPEB1-HTR2A-AS1 and ACSL3-DNAJC27-AS1 pairs had correlation coefficients over 0.9. Twenty ferroptosis-related lncRNAs were identified and validated in IVDD. Eight of these lncRNAs were upregulated in IVDD nucleus pulposus cells, including HTR2A-AS1, MIF-AS1, SLC8A1-AS1, LINC00942, DUXAP8, LINC00161, LUCAT1 and LINC01615. Twelve were downregulated in IVDD nucleus pulposus cells, including DNAJC27-AS1, H19, LINC01588, LINC02015, FLNC1, CARMN, PRKG1-AS1, APCDD1L-DT, LINC00839, LINC00536, LINC00710 and LINC01535. Eighteen of the 20 lncRNAs (excluding H19 and LUCAT1) were identified as ferroptosis-related lncRNAs for the first time and verified in IVDD. We have identified a ferroptosis-related lncRNA signature involved in IVDD and revealed a close relationship between CPEB1 and HTR2A-AS1, and between ACSL3 and DNAJC27-AS1. Our findings indicate that ferroptosis-related lncRNAs are a new target set for the early detection and therapy of IVDD.

Identification and validation of NAD+ metabolism-related biomarkers in patients with diabetic peripheral neuropathy.

The mechanism of Nicotinamide Adenine Dinucleotide (NAD+) metabolism-related genes (NMRGs) in diabetic peripheral neuropathy (DPN) is unclear. This study aimed to find new NMRGs biomarkers in DPN. DPN related datasets GSE95849 and GSE185011 were acquired from the Gene Expression Omnibus (GEO) database. 51 NMRGs were collected from a previous article. To explore NMRGs expression in DPN and control samples, differential expression analysis was completed in GSE95849 to obtain differentially expressed genes (DEGs), and the intersection of DEGs and NMRGs was regarded as DE-NMRGs. Next, a protein-protein interaction (PPI) network based on DE-NMRGs was constructed and biomarkers were screened by eight algorithms. Additionally, Gene Set Enrichment Analysis (GSEA) enrichment analysis was completed, biomarker-based column line graphs were constructed, lncRNA-miRNA-mRNA and competing endogenouse (ce) RNA networks were constructed, and drug prediction was completed. Finally, biomarkers expression validation was completed in GSE95849 and GSE185011. 5217 DEGs were obtained from GSE95849 and 21 overlapping genes of DEGs and NMRGs were DE-NMRGs. Functional enrichment analysis revealed that DE-NMRGs were associated with glycosyl compound metabolic process. The PPI network contained 93 protein-interaction pairs and 21 nodes, with strong interactions between NMNAT1 and NAMPT, NADK and NMNAT3, ENPP3 and NUDT12 as biomarkers based on 8 algorithms. Expression validation suggested that ENPP3 and NUDT12 were upregulated in DPN samples (P < 0.05). Moreover, an alignment diagram with good diagnostic efficacy based on ENPP3 and NUDT12 were identified was constructed. GSEA suggested that ENPP3 was enriched in Toll like receptor (TLR) pathway, NUDT12 was enriched in maturity onset diabetes of the young and insulin pathway. Furthermore, 18 potential miRNAs and 36 Transcription factors (TFs) were predicted and the miRNA-mRNA-TF networks were constructed, suggesting that ENPP3 might regulate hsa-miR-34a-5p by affecting MYNN. The ceRNA network suggested that XLOC_013024 might regulate hsa-let-7b-5p by affecting NUDT12. 15 drugs were predicted, with 8 drugs affecting NUDT12 such as resveratrol, and 13 drugs affecting ENPP3 such as troglitazone. ENPP3 and NUDT12 might play key roles in DPN, which provides reference for further research on DPN.

Comprehensive analysis of lactate-related gene profiles and immune characteristics in lupus nephritis.

The most frequent cause of kidney damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN), which is also a significant risk factor for morbidity and mortality. Lactate metabolism and protein lactylation might be related to the development of LN. However, there is still a lack of relative research to prove the hypothesis. Hence, this study was conducted to screen the lactate-related biomarkers for LN and analyze the underlying mechanism. To identify differentially expressed genes (DEGs) in the training set (GSE32591, GSE127797), we conducted a differential expression analysis (LN samples versus normal samples). Then, module genes were mined using WGCNA concerning LN. The overlapping of DEGs, critical module genes, and lactate-related genes (LRGs) was used to create the lactate-related differentially expressed genes (LR-DEGs). By using a machine-learning algorithm, ROC, and expression levels, biomarkers were discovered. We also carried out an immune infiltration study based on biomarkers and GSEA. A sum of 1259 DEGs was obtained between LN and normal groups. Then, 3800 module genes in reference to LN were procured. 19 LR-DEGs were screened out by the intersection of DEGs, key module genes, and LRGs. Moreover, 8 pivotal genes were acquired via two machine-learning algorithms. Subsequently, 3 biomarkers related to lactate metabolism were obtained, including COQ2, COQ4, and NDUFV1. And these three biomarkers were enriched in pathways 'antigen processing and presentation' and 'NOD-like receptor signaling pathway'. We found that Macrophages M0 and T cells regulatory (Tregs) were associated with these three biomarkers as well. Overall, the results indicated that lactate-related biomarkers COQ2, COQ4, and NDUFV1 were associated with LN, which laid a theoretical foundation for the diagnosis and treatment of LN.

Study on pyroptosis-related genes Casp8, Gsdmd and Trem2 in mice with cerebral infarction.

Cerebral infarction is the main cause of death in patients with cerebrovascular diseases. Our research aimed to screen and validate pyroptosis-related genes in cerebral infarction for the targeted therapy of cerebral infarction. A total of 1,517 differentially expressed genes (DEGs) were obtained by DESeq2 software analysis. Gene set enrichment analysis results indicated that genes of middle cerebral artery occlusion (MCAO) mice aged 3 months and 18 months were enriched in pyroptosis, respectively. Differentially expressed pyroptosis-related genes (including Aim2, Casp8, Gsdmd, Naip2, Naip5, Naip6 and Trem2) were obtained through intersection of DEGs and genes from pyroptosis Gene Ontology Term (GO:0070269), and they were up-regulated in the brain tissues of MCAO mice in GSE137482. In addition, Casp8, Gsdmd, and Trem2 were verified to be significantly up-regulated in MCAO mice in GSE93376. The evaluation of neurologic function and triphenyltetrazolium chloride staining showed that the MCAO mouse models were successfully constructed. Meanwhile, the expressions of TNF-α, pyroptosis-related proteins, Casp8, Gsdmd and Trem2 in MCAO mice were significantly up-regulated. We selected Trem2 for subsequent functional analysis. OGD treatment of BV2 cell in vitro significantly upregulated the expressions of Trem2. Subsequent downregulation of Trem2 expression in OGD-BV2 cells further increased the level of pyroptosis. Therefore, Trem2 is a protective factor regulating pyroptosis, thus influencing the progression of cerebral infarction. Casp8, Gsdmd and Trem2 can regulate pyroptosis, thus affecting cerebral infarction.

Transcriptome analysis of frontal fibrosis alopecia revealed involvement of immune cells and ferroptosis.

Frontal fibrosis alopecia (FFA) is a primary cicatricial alopecia and has received increasing attention in recent years. However, the pathogenesis of FFA has not been fully elucidated. Herein, we collected the transcriptome data of scalp lesions of seven patients with FFA and seven healthy controls. The differential expression analysis and weighted gene co-expression network analysis were conducted and we identified 458 differentially expressed genes (DEGs) in two key modules. Later, we performed functional enrichment analysis and functional modules identification, revealing the participation of immune response and fatty acid metabolism. Based on the results, we processed further studies. On the one hand, we analyzed the infiltrating immune cells of FFA through CIBERSORT algorithm, indicating the activation of M1 macrophage and CD8+ T cell. On the other hand, considering lipid metabolism of FFA and oxidative stress of hair follicle cells in alopecia, we explored the potential ferroptosis of FFA. By intersection of DEGs and ferroptosis-related genes from FerrDb database, 19 genes were identified and their expression was validated in an external dataset containing 36 FFA cases and 12 controls. Then, we used LASSO algorithms to construct a four-gene diagnostic model, which achieved an AUC of 0.924 in validation dataset. Additionally, the immune cells were found to be related to ferroptosis in FFA. Taken together, this study contributed to reveal the molecular mechanisms of FFA and is expected to inspire future research on treatment.