Transcription Factor Regulatory Networks Research Articles

Identification of co-expressed central genes and transcription factors in acute myocardial infarction and diabetic nephropathy

BackgroundAcute myocardial infarction (AMI) and diabetic nephropathy (DN) are common clinical co-morbidities, but they are challenging to manage and have poor prognoses. There is no research on the bioinformatics mechanisms of comorbidity, and this study aims to investigate such mechanisms.MethodsWe downloaded the AMI data (GSE66360) and DN datasets (GSE30528 and GSE30529) from the Gene Expression Omnibus (GEO) platform. The GSE66360 dataset was divided into two parts: the training set and the validation set, and GSE30529 was used as the training set and GSE30528 as the validation set. After identifying the common differentially expressed genes (DEGs) in AMI and DN in the training set, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and protein–protein interaction (PPI) network construction were performed. A sub-network graph was constructed by MCODE, and 15 hub genes were screened by the Cytohubba plugin. The screened hub genes were validated, and the 15 screened hub genes were subjected to GO, KEGG, Gene MANIA analysis, and transcription factor (TF) prediction. Finally, we performed TF differential analysis, enrichment analysis, and TF and gene regulatory network construction.ResultsA total of 46 genes (43 up-regulated and 3 down-regulated) were identified for subsequent analysis. GO functional analysis emphasized the presence of genes mainly in the vesicle membrane and secretory granule membrane involved in antigen processing and presentation, lipopeptide binding, NAD + nucleosidase activity, and Toll-like receptor binding. The KEGG pathways analyzed were mainly in the phagosome, neutrophil extracellular trap formation, natural killer cell-mediated cytotoxicity, apoptosis, Fc gamma R-mediated phagocytosis, and Toll-like receptor signaling pathways. Eight co-expressed hub genes were identified and validated, namely TLR2, FCER1G, CD163, CTSS, CLEC4A, IGSF6, NCF2, and MS4A6A. Three transcription factors were identified and validated in AMI, namely NFKB1, HIF1A, and SPI1.ConclusionsOur study reveals the common pathogenesis of AMI and DN. These common pathways and hub genes may provide new ideas for further mechanistic studies.

Comprehensive analysis of the REST transcription factor regulatory networks in IDH mutant and IDH wild-type glioma cell lines and tumors

The RE1-silencing transcription factor (REST) acts either as a repressor or activator of transcription depending on the genomic and cellular context. REST is a key player in brain cell differentiation by inducing chromatin modifications, including DNA methylation, in a proximity of its binding sites. Its dysfunction may contribute to oncogenesis. Mutations in IDH1/2 significantly change the epigenome contributing to blockade of cell differentiation and glioma development. We aimed at defining how REST modulates gene activation and repression in the context of the IDH mutation-related phenotype in gliomas. We studied the effects of REST knockdown, genome wide occurrence of REST binding sites, and DNA methylation of REST motifs in IDH wild type and IDH mutant gliomas. We found that REST target genes, REST binding patterns, and TF motif occurrence proximal to REST binding sites differed in IDH wild-type and mutant gliomas. Among differentially expressed REST targets were genes involved in glial cell differentiation and extracellular matrix organization, some of which were differentially methylated at promoters or gene bodies. REST knockdown differently impacted invasion of the parental or IDH1 mutant glioma cells. The canonical REST-repressed gene targets showed significant correlation with the GBM NPC-like cellular state. Interestingly, results of REST or KAISO silencing suggested the interplay between these TFs in regulation of REST-activated and repressed targets. The identified gene regulatory networks and putative REST cooperativity with other TFs, such as KAISO, show distinct REST target regulatory networks in IDH-WT and IDH-MUT gliomas, without concomitant DNA methylation changes. We conclude that REST could be an important therapeutic target in gliomas.

Characterization of PANoptosis-related genes and the immune landscape in moyamoya disease

Moyamoya disease (MMD) is a cerebrovascular narrowing and occlusive condition characterized by progressive stenosis of the terminal portion of the internal carotid artery and the formation of an abnormal network of dilated, fragile perforators at the base of the brain. However, the role of PANoptosis, an apoptotic mechanism associated with vascular disease, has not been elucidated in MMD. In our study, a total of 40 patients’ genetic data were included, and a total of 815 MMD-related differential genes were screened, including 215 upregulated genes and 600 downregulated genes. Among them, DNAJA3, ESR1, H19, KRT18 and STK3 were five key genes. These five key genes were associated with a variety of immune cells and immune factors. Moreover, GSEA (gene set enrichment analysis) and GSVA (gene set variation analysis) showed that the different expression levels of the five key genes affected multiple signaling pathways associated with MMD. In addition, they were associated with the expression of MMD-related genes. Then, based on the five key genes, a transcription factor regulatory network was constructed. In addition, targeted therapeutic drugs against MMD-related genes were obtained by the Cmap drug prediction method: MST-312, bisacodyl, indirubin, and tropanyl-3,5-dimethylbenzoate. These results suggest that the PANoptosis-related genes may contribute to the pathogenesis of MMD through multiple mechanisms.

A single-cell profile reveals the transcriptional regulation responded for Abelmoschus manihot (L.) treatment in diabetic kidney disease

BackgroundHuangkui capsule (HKC), as an ethanol extract of Abelmoschus manihot (L.), has a significant efficacy in treatment of the patients with diabetic kidney disease (DKD). The bioactive ingredients of HKC mainly include the flavonoids such as rutin, hyperoside, hibifolin, isoquercetin, myricetin, quercetin and quercetin-3-O-robinobioside. PurposeTo explore the molecular mechanisms of A. manihot in treatment of DKD. Study designA single-cell RNA sequencing analysis of kidneys in db/db mice with and without HKC administration. MethodsUrinary biochemical and histopathological examination in C57BL/6 and db/db mice of DKD and HKC groups was done. Single-cell RNA sequencing pipeline was then performed. The regulatory mechanisms of seven flavonoids in HKC were revealed by cell communication, prediction of transcription factor regulatory network, and molecular docking. ResultsBy constructing ligand-receptor regulatory network and performing molecular docking between 75 receptors with different activities and seven flavonoids. 11 key receptors in 4 cell types (segment 3 proximal convoluted tubular cell, ascending limbs of the loop of Henle, distal convoluted tubule, and T cell) in kidneys were found to be directly interacted with HKC. The interactions regulated 8 downstream regulons. The docking receptors in T cell led to transcriptional event differences in the regulons such as Cebpb, Rel, Tbx21 and Klf2 and consequently affected the activation, differentiation, and infiltration of T cell, while the receptors Tgfbr1 and Ldlr in stromal cells of kidneys were closely associated with the downstream transcriptional events of renal injury and proteinuria in DKD. ConclusionThe current study provides novel information of the key receptors and regulons in renal cells for a better understanding of the cell type specific molecular mechanisms of A. manihot in treatment of DKD.

Molecular subtype identification of cerebral ischemic stroke based on ferroptosis-related genes

Cerebral ischemic stroke (CIS) has the characteristics of a high incidence, disability, and mortality rate. Here, we aimed to explore the potential pathogenic mechanisms of ferroptosis-related genes (FRGs) in CIS. Three microarray datasets from the Gene Expression Omnibus (GEO) database were utilized to analyze differentially expressed genes (DEGs) between CIS and normal controls. FRGs were obtained from a literature report and the FerrDb database. Weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) network were used to screen hub genes. The receiver operating characteristic (ROC) curve was adopted to evaluate the diagnostic value of key genes in CIS, followed by analysis of immune microenvironment, transcription factor (TF) regulatory network, drug prediction, and molecular docking. In total, 128 CIS samples were divided into 2 subgroups after clustering analysis. Compared with cluster A, 1560 DEGs were identified in cluster B. After the construction of the WGCNA and PPI network, 5 hub genes, including MAPK3, WAS, DNAJC5, PRKCD, and GRB2, were identified for CIS. Interestingly, MAPK3 was a FRG that differentially expressed between cluster A and cluster B. The expression levels of 5 hub genes were all specifically highly in cluster A subtype. It is noted that neutrophils were the most positively correlated with all 5 real hub genes. PRKCD was one of the target genes of FASUDIL. In conclusion, five real hub genes were identified as potential diagnostic markers, which can distinguish the two subtypes well.

Single-cell data revealed exhaustion of characteristic NK cell subpopulations and T cell subpopulations in hepatocellular carcinoma.

The treatment and prognosis of patients with advanced hepatocellular carcinoma (HCC) have been a major medical challenge. Unraveling the landscape of tumor immune infiltrating cells (TIICs) in the immune microenvironment of HCC is of great significance to probe the molecular mechanisms. Based on single-cell data of HCC, the cell landscape was revealed from the perspective of TIICs. Special cell subpopulations were determined by the expression levels of marker genes. Differential expression analysis was conducted. The activity of each subpopulation was determined based on the highly expressed genes. CTLA4+ T-cell subpopulations affecting the prognosis of HCC were determined based on survival analysis. A single-cell regulatory network inference and clustering analysis was also performed to determine the transcription factor regulatory networks in the CTLA4+ T cell subpopulations. 10 cell types were identified and NK cells and T cells showed high abundance in tumor tissues. Two NK cells subpopulations were present, FGFBP2+ NK cells, B3GNT7+ NK cells. Four T cells subpopulations were present, LAG3+ T cells, CTLA4+ T cells, RCAN3+ T cells, and HPGDS+ Th2 cells. FGFBP2+ NK cells, and CTLA4+ T cells were the exhaustive subpopulation. High CTLA4+ T cells contributed to poor prognostic outcomes and promoted tumor progression. Finally, a network of transcription factors regulated by NR3C1, STAT1, and STAT3, which were activated, was present in CTLA4+ T cells. CTLA4+ T cell subsets in HCC exhibited functional exhaustion characteristics that probably inhibited T cell function through a transcription factor network dominated by NR3C1, STAT1, and STAT3.

Single-cell transcriptome analysis reveals characteristic transcription factors in polydactyly.

The study aims to provide guidance on the identification of multiple-digit malformations as potential biomarkers and therapeutic targets. Single-cell RNA sequencing (scRNA-seq) data of four multiple-finger malformation samples were downloaded from the GEO public database. Fibroblasts and keratinocytes were divided into cellular subpopulations and the transcription factors of different subpopulations were analyzed. The regulatory network of transcription factors and their target genes were constructed to analyze the functionality of regulons. Examination of the transcriptional profile data from 11,806 single cells uncovered significant associations between regulons and cell function in polydactyly. Specifically, the analysis highlighted the involvement of HOX family members and GLI2 transcription factors, including HOXD13, MSX2, LHX2, EMX2, LEF1, CREB3L2, and LHX2, in the polydactyly process within fibroblast cells. Furthermore, it sheds light on the roles of HES2 and GLIS1 in the formation and development of keratinocytes. Significant presence of transcription factors, especially HOXD13, MSX2, and LHX2, may be strongly related to the development of polydactyly.

Differential Regulation of Male-Hormones-Related Enhancers Revealed by Chromatin Accessibility and Transcriptional Profiles in Pig Liver.

Surgical castration can effectively avoid boar taint and improve pork quality by removing the synthesis of androstenone in the testis, thereby reducing its deposition in adipose tissue. The expression of genes involved in testis-derived hormone metabolism was altered following surgical castration, but the upstream regulatory factors and underlying mechanism remain unclear. In this study, we systematically profiled chromatin accessibility and transcriptional dynamics in liver tissue of castrated and intact full-sibling Yorkshire pigs. First, we identified 897 differentially expressed genes and 6864 differential accessible regions (DARs) using RNA- and ATAC-seq. By integrating the RNA- and ATAC-seq results, 227 genes were identified, and a significant positive correlation was revealed between differential gene expression and the ATAC-seq signal. We constructed a transcription factor regulatory network after motif analysis of DARs and identified a candidate transcription factor (TF) SP1 that targeted the HSD3B1 gene, which was responsible for the metabolism of androstenone. Subsequently, we annotated DARs by incorporating H3K27ac ChIP-seq data, marking 2234 typical enhancers and 245 super enhancers involved in the regulation of all testis-derived hormones. Among these, four typical enhancers associated with HSD3B1 were identified. Furthermore, an in-depth investigation was conducted on the androstenone-related enhancers, and an androstenone-related mutation was identified in a newfound candidatetypical enhancer (andEN) with dual-luciferase assays. These findings provide further insights into how enhancers function as links between phenotypic and non-coding area variations. The discovery of upstream TF and enhancers of HSD3B1 contributes to understanding the regulatory networks of androstenone metabolism and provides an important foundation for improving pork quality.

GEO dataset mining analysis reveals novel Staphylococcus aureus virulence gene regulatory networks and diagnostic targets in mice.

Staphylococcus (S.) aureus infection is a serious, worldwide health concern, particularly in many communities and hospitals. Understanding the S. aureus pathogenetic regulatory network will provide significant insights into diagnostic target screening to improve clinical treatment of diseases caused by S. aureus. We screened differentially expressed genes between normal mice and S. aureus-infected mice. We used the Gene Expression Omnibus (GEO) DataSets database for functional analysis (GO-analysis) and the DAVID and KEGG databases for signaling pathway analyses. We next integrated the gene and pathway analyses with Transcriptional Regulatory Element Database (TRED) to build an antimicrobial resistance gene regulatory network of S. aureus. We performed association analysis of network genes and diseases using DAVID online annotation tools. We identified a total of 437 virulence genes and 15 transcription factors (TFs), as well as 444 corresponding target genes, in the S. aureus TF regulatory network. We screened seven key network nodes (Met, Mmp13, Il12b, Il4, Tnf, Ptgs2, and Ctsl), four key transcription factors (Jun, C3, Spil, and Il6) and an important signaling pathway (TNF). We hypothesized that the cytokine activity and growth factor activity of S. aureus are combinatorically cross-regulated by Met, Mmp13, Il12b, Il4, Tnf, Ptgs2, and Ctsl genes, the TFs Jun, C3, Spi1, and Il6, as well as the immune response, cellular response to lipopolysaccharide, and inflammatory response. Our study provides information and reference values for the molecular understanding of the S. aureus pathogenetic gene regulatory network.

Cdx1 and Gsc distinctly regulate the transcription of BMP4 target gene ventx3.2 by directly binding to the proximal promoter region in Xenopus gastrulae

A comprehensive regulatory network of transcription factors controls the dorsoventral patterning of the body axis in developing vertebrate embryos. Bone morphogenetic protein signaling is essential for activating the Ventx family of homeodomain transcription factors, which regulates embryonic patterning and germ layer identity during Xenopus gastrulation. Although Ventx1.1 and Ventx2.1 of the Xenopus Ventx family have been extensively investigated, Ventx3.2 remains largely understudied. Therefore, this study aimed to investigate the transcriptional regulation of ventx3.2 during the embryonic development of Xenopus. We used goosecoid (Gsc) genome-wide chromatin immunoprecipitation-sequencing data to isolate and replicate the promoter region of ventx3.2. Serial deletion and site-directed mutagenesis were used to identify the cis-acting elements for Gsc and caudal type homeobox 1 (Cdx1) within the ventx3.2 promoter. Cdx1 and Gsc differentially regulated ventx3.2 transcription in this study. Additionally, positive cis-acting and negative response elements were observed for Cdx1 and Gsc, respectively, within the 5' flanking region of the ventx3.2 promoter. This result was corroborated by mapping the active Cdx1 response element (CRE) and Gsc response element (GRE). Moreover, a point mutation within the CRE and GRE completely abolished the activator and repressive activities of Cdx1 and Gsc, respectively. Furthermore, the chromatin immunoprecipitation-polymerase chain reaction confirmed the direct binding of Cdx1 and Gsc to the CRE and GRE, respectively. Inhibition of Cdx1 and Gsc activities at their respective functional regions, namely, the ventral marginal zone and dorsal marginal zone, reversed their effects on ventx3.2 transcription. These results indicate that Cdx1 and Gsc modulate ventx3.2 transcription in the ventral marginal zone and dorsal marginal zone by directly binding to the promoter region during Xenopus gastrulation.

Innovative prognostic modeling in ESCC: leveraging scRNA-seq and bulk-RNA for dendritic cell heterogeneity analysis.

Globally, esophageal squamous cell carcinoma (ESCC) stands out as a common cancer type, characterized by its notably high rates of occurrence and mortality. Recent advancements in treatment methods, including immunotherapy, have shown promise, yet the prognosis remains poor. In the context of tumor development and treatment outcomes, the tumor microenvironment (TME), especially the function of dendritic cells (DCs), is significantly influential. Our study aims to delve deeper into the heterogeneity of DCs in ESCC using single-cell RNA sequencing (scRNA-seq) and bulk RNA analysis. In the scRNA-seq analysis, we utilized the SCP package for result visualization and functional enrichment analysis of cell subpopulations. CellChat was employed to identify potential oncogenic mechanisms in DCs, while Monocle 2 traced the evolutionary trajectory of the three DC subtypes. CopyKAT assessed the benign or malignant nature of cells, and SCENIC conducted transcription factor regulatory network analysis, offering a preliminary exploration of DC heterogeneity. In Bulk-RNA analysis, we constructed a prognostic model for ESCC prognosis and immunotherapy response, based on DC marker genes. This model was validated through quantitative PCR (qPCR) and immunohistochemistry (IHC), confirming the gene expression levels. In this study, through intercellular communication analysis, we identified GALECTIN and MHC-I signaling pathways as potential oncogenic mechanisms within dendritic cells. We categorized DCs into three subtypes: plasmacytoid (pDC), conventional (cDC), and tolerogenic (tDC). Our findings revealed that pDCs exhibited an increased proportion of cells in the G2/M and S phases, indicating enhanced cellular activity. Pseudotime trajectory analysis demonstrated that cDCs were in early stages of differentiation, whereas tDCs were in more advanced stages, with pDCs distributed across both early and late differentiation phases. Prognostic analysis highlighted a significant correlation between pDCs and tDCs with the prognosis of ESCC (P< 0.05), while no significant correlation was observed between cDCs and ESCC prognosis (P = 0.31). The analysis of cell malignancy showed the lowest proportion of malignant cells in cDCs (17%), followed by pDCs (29%), and the highest in tDCs (48%), with these results being statistically significant (P< 0.05). We developed a robust ESCC prognostic model based on marker genes of pDCs and tDCs in the GSE53624 cohort (n = 119), which was validated in the TCGA-ESCC cohort (n = 139) and the IMvigor210 immunotherapy cohort (n = 298) (P< 0.05). Additionally, we supplemented the study with a novel nomogram that integrates clinical features and risk assessments. Finally, the expression levels of genes involved in the model were validated using qPCR (n = 8) and IHC (n = 16), thereby confirming the accuracy of our analysis. This study enhances the understanding of dendritic cell heterogeneity in ESCC and its impact on patient prognosis. The insights gained from scRNA-seq and Bulk-RNA analysis contribute to the development of novel biomarkers and therapeutic targets. Our prognostic models based on DC-related gene signatures hold promise for improving ESCC patient stratification and guiding treatment decisions.

Relapse to cocaine seeking is regulated by medial habenula NR4A2/NURR1 in mice

Drugs of abuse can persistently change the reward circuit in ways that contribute to relapse behavior, partly via mechanisms that regulate chromatin structure and function. Nuclear orphan receptor subfamily4 groupA member2 (NR4A2, also known as NURR1) is an important effector of histone deacetylase 3 (HDAC3)-dependent mechanisms in persistent memory processes and is highly expressed in the medial habenula (MHb), a region that regulates nicotine-associated behaviors. Here, expressing the Nr4a2 dominant negative (Nurr2c) in the MHb blocks reinstatement of cocaine seeking in mice. We use single-nucleus transcriptomics to characterize the molecular cascade following Nr4a2 manipulation, revealing changes in transcriptional networks related to addiction, neuroplasticity, and GABAergic and glutamatergic signaling. The network controlled by NR4A2 is characterized using a transcription factor regulatory network inference algorithm. These results identify the MHb as a pivotal regulator of relapse behavior and demonstrate the importance of NR4A2 as a key mechanism driving the MHb component of relapse.

Novel underlying genetic markers for asthenozoospermia due to abnormal spermatogenesis and reproductive organ inflammation.

Asthenozoospermia, a male fertility disorder, has a complex and multifactorial etiology. Moreover, the effectiveness of different treatments for asthenozoospermia remains uncertain. Hence, by using bioinformatics techniques, the present study aimed to determine the underlying genetic markers and pathogenetic mechanisms associated with asthenozoospermia due to abnormal spermatogenesis and inflammation of the reproductive tract. GSE160749 dataset was downloaded from the Gene Expression Omnibus database, and the data were filtered to obtain 1336 differentially expressed genes (DEGs) associated with asthenozoospermia. These DEGs were intersected with the epithelial mesenchymal transition datasets to yield 61 candidate DEGs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed, and the results revealed that these candidate DEGs were significantly enriched in the enzyme-linked receptor pathway and the thyroid hormone pathway. A protein-protein interaction network was constructed to identify the key genes of asthenozoospermia. A total of five key genes were identified, among which SOX9 was significantly upregulated, while HSPA4, SMAD2, HIF1A and GSK3B were significantly downregulated. These findings were validated by conducting reverse transcription-quantitative PCR for clinical semen samples. To determine the underlying molecular mechanisms, a regulatory network of transcription factors and miRNA-mRNA interactions was predicted. The expression levels of HSPA4, SMAD2 and GSK3B were positively associated with several related etiological genes of asthenozoospermia. In total, five key genes were closely associated with the level and type of immune cells; higher levels of activated B cells and CD8 T cells were observed in asthenozoospermia. Thus, the findings of the present study may provide clues to determine the underlying novel diagnostic genetic markers and treatment strategies for asthenozoospermia.

Comprehensive analysis of the potential pathogenesis of COVID-19 infection and liver cancer.

A growing number of clinical examples suggest that coronavirus disease 2019 (COVID-19) appears to have an impact on the treatment of patients with liver cancer compared to the normal population, and the prevalence of COVID-19 is significantly higher in patients with liver cancer. However, this mechanism of action has not been clarified. To investigate the disease relevance of COVID-19 in liver cancer. Gene sets for COVID-19 (GSE180226) and liver cancer (GSE87630) were obtained from the Gene Expression Omnibus database. After identifying the common differentially expressed genes (DEGs) of COVID-19 and liver cancer, functional enrichment analysis, protein-protein interaction network construction and screening and analysis of hub genes were performed. Subsequently, the validation of the differential expression of hub genes in the disease was performed and the regulatory network of transcription factors and hub genes was constructed. Of 518 common DEGs were obtained by screening for functional analysis. Fifteen hub genes including aurora kinase B, cyclin B2, cell division cycle 20, cell division cycle associated 8, nucleolar and spindle associated protein 1, etc., were further identified from DEGs using the "cytoHubba" plugin. Functional enrichment analysis of hub genes showed that these hub genes are associated with P53 signalling pathway regulation, cell cycle and other functions, and they may serve as potential molecular markers for COVID-19 and liver cancer. Finally, we selected 10 of the hub genes for in vitro expression validation in liver cancer cells. Our study reveals a common pathogenesis of liver cancer and COVID-19. These common pathways and key genes may provide new ideas for further mechanistic studies.

Dissecting the Mechanisms of Intestinal Immune Homeostasis by Analyzing T-Cell Immune Response in Crohn's Disease and Colorectal Cancer.

Crohn's disease (CD) and colorectal cancer (CRC) represent a group of intestinal disorders characterized by intricate pathogenic mechanisms linked to the disruption of intestinal immune homeostasis. Therefore, comprehending the immune response mechanisms in both categories of intestinal disorders is of paramount significance in the prevention and treatment of these debilitating intestinal ailments. IIn this study, we conducted single-cell analysis on paired samples obtained from primary colorectal tumors and individuals with Crohn's disease, which was aimed at deciphering the factors influencing the composition of the intestinal immune microenvironment. By aligning T cells across different tissues, we identified various T cell subtypes, such as γδ T cell, NK T cell, and regulatory T (Treg) cell, which maintained immune system homeostasis and were confirmed in enrichment analyses. Subsequently, we generated pseudo-time trajectories for subclusters of T cells in both syndromes to delineate their differentiation patterns and identify key driver genes Result: Furthermore, cellular communication and transcription factor regulatory networks are all essential components of the intricate web of mechanisms that regulate intestinal immune homeostasis. The identified complex cellular interaction suggested potential T-lineage immunotherapeutic targets against epithelial cells with high copy number variation (CNV) levels in CD and CRC. Finally, the analysis of regulon networks revealed several promising candidates for cell-specific transcription factors (TFs). This study focused on the immune molecular mechanism under intestinal diseases. It contributed to the novel insight of depicting a detailed immune landscape and revealing T-cell responding mechanisms in CD and CRC.

Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes.

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.

Comprehensive Analysis of Key Endoplasmic Reticulum Stress-Related Genes and Immune Infiltrates in Stanford Type A Aortic Dissection.

Type A aortic dissection is a fatal disease. However, the role of endoplasmic reticulum stress-related genes (ERSRGs) in type A aortic dissection has not yet been fully clarified. Differentially expressed genes in the aorta of type A aortic dissection patients were analyzed based on the GSE52093 database. The ERSRGs were downloaded from the GeneCards website. Functional enrichment analysis and protein-protein interaction analysis were performed on the acquired differentially expressed ERSRGs. The mRNA expression of the 5 top key differentially expressed ERSRGs was further explored in GSE153434 and clinical samples. Immune infiltration correlation analysis was performed on the validated key genes. Finally, we constructed regulatory networks of transcription factors, miRNAs, and chemicals. Twelve differentially expressed ERSRGs were identified, of which 8 genes were downregulated and 4 genes were upregulated. GeneMANIA was adopted to analyze these genes and their interacting proteins, and the results showed that the main function was calcium ion transport. Four key genes, ACTC1, CASQ2, SPP1, and REEP1, were validated in GSE153434 and clinical samples. The area under the ROC curve values for ACTC1, CASQ2, SPP1, and REEP1 were 0.92, 0.96, 0.89, and 1.00, respectively. ACTC1 and REEP1 correlated with multiple immune cells. Many transcription factors, microRNAs, and chemicals were identified with the potential to regulate these 4 key genes. In this study, we identified 12 differentially expressed ERSRGs by analyzing the Gene Expression Omnibus database. Four key genes may influence the development of type A aortic dissection by regulating endoplasmic reticulum stress. These results expand our understanding of type A aortic dissection, and the 4 key genes are expected to be diagnostic markers and potential therapeutic targets.

Identification of pivotal genes and regulatory networks associated with atherosclerotic carotid artery stenosis based on comprehensive bioinformatics analysis and machine learning.

Bioinformatics methods were applied to investigate the pivotal genes and regulatory networks associated with atherosclerotic carotid artery stenosis (ACAS) and provide new insights for the treatment of this disease. The study utilized five ACAS datasets (GSE100927, GSE11782, GESE28829, GSE41571, and GSE43292) downloaded from the NCBI GEO database. The first four datasets were combined as the training set (n = 99), while GSE43292 (n = 64) was used as the validation set. Difference analysis and functional enrichment analysis were then performed on the training set. The pathogenic targets of ACAS were screened by protein-protein interaction networks and MCODE analyses, combined with three machine learning algorithms. The results were next verified by analysis of inter-group differences and ROC curve analysis. Next, immune-related function and immune cell correlation analyses were performed, and plaques of human ACAS were applied to verify the results via immunohistochemistry (IH) and immunofluorescence (IF). Finally, the competing endogenous RNAs (ceRNA) and transcription factors (TFs) regulatory networks of the characterized genes were constructed. A total of 177 differentially expressed genes were identified, including 67 genes downregulated and 110 genes upregulated. Gene set enrichment analysis revealed that five pathways were active in the experimental group, including xenograft rejection, autoimmune thyroid disease, graft-versus-host disease, leishmaniasis infection, and lysosomes. Four key genes were identified, with C3AR1 being upregulated and FBLN5, PPP1R12A, and TPM1 being downregulated. The analysis of inter-group differences demonstrated that the four characterized genes were differentially expressed in both the control and experimental groups. The ROC analysis showed that they had high AUC values in both the training and validation sets. Therefore, a predictive ACAS patient nomogram model based on the screened genes was established. Correlation analysis revealed a positive correlation between C3AR1 expression and neutrophils, which was further validated in IH and IF. One or multiple lncRNAs may compete with the characterized genes for binding miRNAs. Additionally, each characterized gene interacts with multiple TFs. Four pivotal genes were screened, and relevant ceRNA and TFs were predicted. These molecules may exert a crucial role in ACAS and serve as potential biomarkers and therapeutic targets.

Unraveling the enigma of B cells in diffuse large B-cell lymphoma: unveiling cancer stem cell-like B cell subpopulation at single-cell resolution.

Diffuse large B-cell lymphoma (DLBCL) represents the most prevalent form of aggressive non-Hodgkin lymphoma. Despite receiving standard treatment, a subset of patients undergoes refractory or recurrent cases, wherein the involvement of cancer stem cells (CSCs) could be significant. We comprehensively characterized B cell subpopulations using single-cell RNA sequencing data from three DLBCL samples and one normal lymph tissue. The CopyKat R package was employed to assess the malignancy of B cell subpopulations based on chromosomal copy number variations. CIBERSORTx software was utilized to estimate the proportions of B cell subpopulations in 230 DLBCL tissues. Furthermore, we employed the pySCENIC to identify key transcription factors that regulate the functionality of B cell subpopulations. By employing CellphoneDB, we elucidated the interplay among tumor microenvironment components within the B cell subpopulations. Finally, we validated our findings through immunofluorescence experiments. Our analysis revealed a specific cancer stem cell-like B cell subpopulation exhibiting self-renewal and multilineage differentiation capabilities based on the exploration of B cell subpopulations in DLBCL and normal lymph tissues at the single-cell level. Notably, a high infiltration of cancer stem cell-like B cells correlated with a poor prognosis, potentially due to immune evasion mediated by low expression of major histocompatibility complex molecules. Furthermore, we identified key transcription factor regulatory networks regulated by HMGB3, SAP30, and E2F8, which likely played crucial roles in the functional characterization of the cancer stem cell-like B cell subpopulation. The existence of cancer stem cell-like B cells in DLBCL was validated through immunofluorescent staining. Finally, cell communication between B cells and tumor-infiltrating T cell subgroups provided further insights into the functional characterization of the cancer stem cell-like B cell subpopulation. Our research provides a systematic description of a specific cancer stem cell-like B cell subpopulation associated with a poor prognosis in DLBCL. This study enhances our understanding of CSCs and identifies potential therapeutic targets for refractory or recurrent DLBCL patients.

MORA and EnsembleTFpredictor: An ensemble approach to reveal functional transcription factor regulatory networks.

Our study aimed to identify biologically relevant transcription factors (TFs) that control the expression of a set of co-expressed or co-regulated genes. We developed a fully automated pipeline, Motif Over Representation Analysis (MORA), to detect enrichment of known TF binding motifs in any query sequences. MORA performed better than or comparable to five other TF-prediction tools as evaluated using hundreds of differentially expressed gene sets and ChIP-seq datasets derived from known TFs. Additionally, we developed EnsembleTFpredictor to harness the power of multiple TF-prediction tools to provide a list of functional TFs ranked by prediction confidence. When applied to the test datasets, EnsembleTFpredictor not only identified the target TF but also revealed many TFs known to cooperate with the target TF in the corresponding biological systems. MORA and EnsembleTFpredictor have been used in two publications, demonstrating their power in guiding experimental design and in revealing novel biological insights.