Characteristics of immune cell infiltration and associated diagnostic biomarkers in ulcerative colitis: results from bioinformatics analysis.
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ABSTRACT:
SUBMITTER: Xue G
PROVIDER: S-EPMC8291880 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We aimed to explore diagnostic biomarks and immune cell infiltration characteristics in ulcerative colitis (UC). We used the dataset GSE38713 as the training set and dataset GSE94648 as the test set. A total of 402 differentially expressed genes (DEGs) were obtained from GSE38713. Annotating, visualizing, and integrating discovery of these differential genes was performed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia Pathway (KEGG), and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction networks were constructed from the STRING database, and protein functional modules were identified using the CytoHubba plugin of Cytoscape. Random forest and LASSO regression were used to screen for UC-related diagnostic markers, and ROC curves were generated to validate their diagnostic value. The composition of 22 immune cells was analyzed, and the immune cell infiltration in UC was analyzed using CIBERSORT. Results: Seven diagnostic markers associated with UC were identified: TLCD3A, KLF9, EFNA1, NAAA,WDR4, CKAP4, and CHRNA1. Immune cell infiltration assessment revealed that macrophages M1, activated dendritic cells, and neutrophil cells infiltrated relatively more compared to normal control samples. Our results suggest a new functional feature of UC and suggest potential biomarkers for UC through comprehensive analysis of integrated gene expression data.
| S-EPMC10102327 | biostudies-literature
Project description:The coexistence of ulcerative colitis (UC) and nonalcoholic steatohepatitis (NASH) involves a intricate interplay, though the precise pathophysiological mechanisms remain elusive. To shed light on this, our study endeavors to unravel the shared gene signatures and molecular mechanisms by employing quantitative bioinformatics analysis on a publicly available RNA-sequencing database. Gene expression profiles of UC (GSE87466) and NASH (GSE89632) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using R software. After identifying common DEGs, functional enrichment analysis, protein-protein interaction (PPI) network analysis and module construction were performed to obtain candidate hub genes. GSE47908 for UC and GSE159676 for NASH were selected to validate the obtained candidate genes. A total of 119 common DEGs were found in NASH and UC patients. Functional and pathway analyses emphasized that viral infection, inflammation and immune response were enriched in these two diseases. After module construction and validation, CD2, CD8A, GNLY, IFI44, NKG7 and OAS2 were identified as hub genes. 6 hub genes and their combined prediction scores were found with an impressive accuracy and sensitivity. Functional estimation, gene set enrichment analysis and immune infiltration signature identification showed notable associations of the six hub genes with T cells, natural killer cells and type I interferon levels. In addition, we constructed UC combined with NASH mice model successfully with significantly higher expression of hub genes in both liver and colonic tissues than those in control group. Our study elucidates 6 hub genes of UC and NASH, which may participate in immune, inflammatory and antiviral effects. These findings provide some potential biochemical markers for further exploration of UC coexistence with NASH.
| S-EPMC10613305 | biostudies-literature
Project description:BackgroundParkinson's disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers.Materials and methodsThree substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the "limma" package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein-protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers.ResultsA total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package "UpSet." We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905).ConclusionBy affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.
| S-EPMC9909419 | biostudies-literature
Project description:BackgroundDiabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD). Currently, microalbuminuria is mainly used as a diagnostic indicator of DN, but there are still limitations and lack of immune-related diagnostic markers. In this study, we aimed to explore diagnostic biomarkers associated with immune infiltration of DN.MethodsImmune-related differentially expressed genes (DEGs) were derived from those at the intersection of the ImmPort database and DEGs identified from 3 datasets, which were based on the Gene Expression Omnibus (GEO). Functional enrichment analyses were performed; a protein-protein interaction (PPI) network was constructed; and hub genes were identified by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). After screening the key genes using least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), a prediction model for DN was constructed. The predictive performance of the model was quantified by receiver-operating characteristic curve, decision curve analysis, and nomogram. Next, infiltration of 22 types of immune cells in DN kidney tissue was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Expression of diagnostic markers was analyzed in DN and control patient groups to determine the genes with the maximum diagnostic potential. Finally, we explored the correlation between diagnostic markers and immune cells.ResultsOverall, 191 immune-related DEGs were identified, that primarily positively regulated with cell adhesion, T cell activation, leukocyte proliferation and migration, urogenital system development, lymphocyte differentiation and proliferation, and mononuclear cell proliferation. Gene sets were related to the PI3K-Akt, MAPK, Rap1, and WNT signaling pathways. Finally, CCL19, CD1C, and IL33 were identified as diagnostic markers of DN and recognized in the 3 datasets [area under the curve (AUC) =0.921]. Immune cell infiltration analysis demonstrated that CCL19 was positively correlated with macrophages M1 (R=0.47, P<0.001) and macrophages M2 (R=0.75, P<0.001). CD1C was positively correlated with macrophages M1 (R=0.47, P<0.05), macrophages M2 (R=0.75, P<0.01), and monocytes (R=0.42, P<0.01). IL33 was positively correlated with macrophages M1 (R=0.45, P<0.05), macrophages M2 (R=0.74, P<0.01), and monocytes (R=0.41, P<0.01).ConclusionsOur results provide evidence that CCL19, CD1C, and IL33, which are associated with immune infiltration, are the potential diagnostic biomarkers for DN candidates.
| S-EPMC9279778 | biostudies-literature
Project description:BackgroundCrohn's disease (CD) has an increasing incidence and prevalence worldwide. It is currently believed that both the onset and progression of the disease are closely related to immune system imbalance and the infiltration of immune cells. The aim of this study was to investigate the molecular immune mechanisms associated with CD and its fibrosis through bioinformatics analysis.MethodsThree datasets from the Gene Expression Omnibus data base (GEO) were downloaded for data analysis and validation. Single sample gene enrichment analysis (ssGSEA) was used to evaluate the infiltration of immune cells in CD samples. Immune cell types with significant differences were identified by Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Differentially expressed genes (DEGs) were screened and then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional correlation analysis, as well as protein-protein interaction (PPI) network analysis. The cytoHubba program and the GSE75214 dataset were used to screen for hub genes and plot Receiver operating characteristic (ROC)curves to screen for possible biomarkers of CD based on diagnostic efficacy. The hub genes of CD were correlated with five significantly different immune cells. In addition, validation was performed by real time quantitative PCR (RT-qPCR) experiments in colonic tissue of CD intestinal fibrosis rats to further identify hub genes that are more related to CD intestinal fibrosis.ResultsThe DEGs were analyzed separately by 10 algorithms and narrowed down to 9 DEGs after taking the intersection. 4 hub genes were further screened by the GSE75214 validation set, namely COL1A1, CXCL10, MMP2 and FGF2. COL1A1 has the highest specificity and sensitivity for the diagnosis of CD and is considered to have the potential to diagnose CD. Five immune cells with significant differences were screened between CD and health controls (HC). Through the correlation analysis between five kinds of immune cells and four biomarkers, it was found that CXCL10 was positively correlated with activated dendritic cells, effector memory CD8+ T cells. MMP2 was positively correlated with activated dendritic cells, gamma delta T cells (γδ T) and mast cells. MMP2 and COL1A1 were significantly increased in colon tissue of CD fibrosis rats.ConclusionMMP2, COL1A1, CXCL10 and FGF2 can be used as hub genes for CD. Among them, COL1A1 can be used as a biomarker for the diagnosis of CD. MMP2 and CXCL10 may be involved in the development and progression of CD by regulating activated dendritic cell, effector memory CD8+ T cell, γδ T cell and mast cell. In addition, MMP2 and COL1A1 may be more closely related to CD intestinal fibrosis.
| S-EPMC10445157 | biostudies-literature
Project description:BackgroundRheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by inflammatory cell infiltration, leading to persistent synovitis and joint destruction. The pathogenesis of RA remains unclear. This study aims to explore the immune molecular mechanism of RA through bioinformatics analysis.MethodsFive microarray datasets and a high throughput sequencing dataset were downloaded. CIBERSORT algorithm was performed to evaluate immune cell infiltration in synovial tissues between RA and healthy control (HC). Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the significantly different infiltrates of immune cells. Differentially expressed genes (DEGs) were screened by "Batch correction" and "RobustRankAggreg" methods. Functional correlation of DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Candidate biomarkers were identified by cytoHubba of Cytoscape, and their diagnostic effectiveness was predicted by Receiver Operator Characteristic Curve (ROC) analysis. The association of the identified biomarkers with infiltrating immune cells was explored using Spearman's rank correlation analysis in R software.ResultsTen significantly different types of immune cells between RA and HC were identified. A total of 202 DEGs were obtained by intersection of DEGs screened by two methods. The function of DEGs were significantly associated with immune cells. Five hub genes (CXCR4, CCL5, CD8A, CD247, and GZMA) were screened by R package "UpSet". CCL5+CXCR4 and GZMA+CD8A were verified to have the capability to diagnose RA and early RA with the most excellent specificity and sensitivity, respectively. The correlation between immune cells and biomarkers showed that CCL5 was positively correlated with M1 macrophages, CXCR4 was positively correlated with memory activated CD4+ T cells and follicular helper T (Tfh) cells, and GZMA was positively correlated with Tfh cells.ConclusionsCCL5, CXCR4, GZMA, and CD8A can be used as diagnostic biomarker for RA. GZMA-Tfh cells, CCL5-M1 macrophages, and CXCR4- memory activated CD4+ T cells/Tfh cells may participate in the occurrence and development of RA, especially GZMA-Tfh cells for the early pathogenesis of RA.
| S-EPMC8411707 | biostudies-literature
Project description:BackgroundPolymyositis (PM) is an acquirable muscle disease with proximal muscle involvement of the extremities as the main manifestation; it is a category of idiopathic inflammatory myopathy. This study aimed to identify the key biomarkers of PM, while elucidating PM-associated immune cell infiltration and immune-related pathways.MethodsThe gene microarray data related to PM were downloaded from the Gene Expression Omnibus database. The analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) networks were performed on differentially expressed genes (DEGs). The hub genes of PM were identified using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) algorithm, and the diagnostic accuracy of hub markers for PM was assessed using the receiver operating characteristic curve. In addition, the level of infiltration of 28 immune cells in PM and their interrelationship with hub genes were analyzed using single-sample GSEA.ResultsA total of 420 DEGs were identified. The biological functions and signaling pathways closely associated with PM were inflammatory and immune processes. A series of four expression modules were obtained by WGCNA analysis, with the turquoise module having the highest correlation with PM; 196 crossover genes were obtained by combining DEGs. Subsequently, six hub genes were finally identified as the potential biomarkers of PM using LASSO algorithm and validation set verification analysis. In the immune cell infiltration analysis, the infiltration of T lymphocytes and subpopulations, dendritic cells, macrophages, and natural killer cells was more significant in the PM.ConclusionWe identified the hub genes closely related to PM using WGCNA combined with LASSO algorithm, which helped clarify the molecular mechanism of PM development and might have great significance for finding new immunotherapeutic targets, and disease prevention and treatment.
| S-EPMC9548705 | biostudies-literature
Project description:AimsThis study is aimed at investigating the pathogenesis of rheumatoid arthritis (RA) by identifying key biomarkers, associated immune infiltration, and small-molecule compounds using bioinformatic analysis.MethodsSix datasets were obtained from the Gene Expression Omnibus database, and the batch effect was adjusted. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyse differentially expressed genes (DEGs). Furthermore, candidate small-molecule drugs associated with RA were selected from the Connectivity Map (CMap) database. The least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and multivariate logistic regression analyses were performed on DEGs to screen for RA diagnostic markers. The receiver operating characteristic curve, concordance index, and GiViTi calibration band were the metrics used to assess the diagnostic markers of RA identified in this analysis. The single-sample gene set enrichment analysis was performed to calculate the scores of infiltrating immune cells and evaluate the activities of immune-related pathways. Finally, the correlation between screening markers and RA diagnosis was determined.ResultsA total of 227 DEGs were identified. Functional enrichment analysis and KEGG revealed that DEGs were enriched by the immune response. CMap analysis identified 11 small-molecule compounds with therapeutic potential for RA. In gene expression, the activities of 13 immune cells and 12 immune-related pathways significantly differed between patients with RA and healthy controls. DPYSL3 and SPP1 had the potential to diagnose RA. SPP1 expression was positively correlated with DPYSL3 in 11 immune cells and 10 immune-related pathways.ConclusionThis study comprehensively analysed DEGs and immune infiltration and screened for potential diagnostic markers and small-molecule compounds of RA.
| S-EPMC9007666 | biostudies-literature
Project description:Heart failure (HF) and osteoarthritis (OA) are medical conditions that can significantly impact daily activities. Evidence has shown that HF and OA may share some pathogenic mechanisms. However, the underlying genomic mechanisms remain unclear. This study aimed to explore the underlying molecular mechanism and identify diagnostic biomarkers for HF and OA. With the cutoff criteria of fold change (FC) > 1.3 and P < .05, 920, 1500, 2195, and 2164 differentially expressed genes (DEGs) were identified in GSE57338, GSE116250, GSE114007, and GSE169077, respectively. After making the intersection of DEGs, we obtained 90 upregulated DEGs and 51 downregulated DEGs in HF datasets and 115 upregulated DEGs and 75 downregulated DEGs in OA datasets. Afterward, we conducted genome ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, protein-protein interaction (PPI) networks, and hub genes screening based on DEGs. Then, 4 common DEGs (fibroblast activation protein alpha [FAP], secreted frizzled-related protein 4 (SFRP4), Thy-1 cell surface antigen (THY1), matrix remodeling associated 5 [MXRA5]) between HF and OA were screened and validated in GSE5406 and GSE113825 datasets, based on which we established the support vector machine (SVM) models. The combined area under the receiver operating characteristic curve (AUC) of THY1, FAP, SFRP4, and MXRA5 in the HF training and test sets reached 0.949 and 0.928. While in the OA training set and test set, the combined AUC of THY1, FAP, SFRP4, and MXRA5 reached 1 and 1, respectively. The analysis of immune cells in HF revealed high levels of dendritic cell (DC), B cells, natural killer T cell (NKT), Type 1 regulatory T cell (Tr1), cytotoxic T cell (Tc), exhausted T cell (Tex), and mucosal-associated invariant T cell (MAIT), while displaying lower levels of monocytes, macrophages, NK, CD4 + T, gamma delta T (γδ T), T helper type 1 (Th1), T helper type 2 (Th2), and effector memory T cell (Tem). Moreover, the 4 common DEGs were positively correlated with DCs and B cells and negatively correlated with γδ T. In OA patients, the abundance of monocyte, macrophage, CD4 + naïve, and natural T regulatory cell (nTreg) was higher, while the infiltration of CD8 + T, γδ T, CD8 + naïve, and MAIT was lower. The expression of THY1 and FAP was significantly correlated with macrophage, CD8 + T, nTreg, and CD8 + naïve. SFRP4 was correlated with monocyte, CD8 + T, γδ T, CD4 + naïve, nTreg, CD8 + naïve and MAIT. MXRA5 was correlated with macrophage, CD8 + T, nTreg and CD8 + naïve. FAP, THY1, MXRA5, and SFRP4 may be diagnostic biomarkers for both HF and OA, and their correlation with immune cell infiltrations suggests shared immune pathogenesis.
| S-EPMC10313258 | biostudies-literature
Project description:IntroductionRecurrent implantation failure (RIF) is a frustrating challenge because the cause is unknown. The current study aims to identify differentially expressed genes (DEGs) in the endometrium on the basis of immune cell infiltration characteristics between RIF patients and healthy controls, as well as to investigate potential prognostic markers in RIF.MethodsGSE103465, and GSE111974 datasets from the Gene Expression Omnibus database were obtained to screen DEGs between RIF and control groups. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes Pathway analysis, Gene Set Enrichment Analysis, and Protein-protein interactions analysis were performed to investigate potential biological functions and signaling pathways. CIBERSORT was used to describe the level of immune infiltration in RIF, and flow cytometry was used to confirm the top two most abundant immune cells detected.Results122 downregulated and 66 upregulated DEGs were obtained between RIF and control groups. Six immune-related hub genes were discovered, which were involved in Wnt/-catenin signaling and Notch signaling as a result of our research. The ROC curves revealed that three of the six identified genes (AKT1, PSMB8, and PSMD10) had potential diagnostic values for RIF. Finally, we used cMap analysis to identify potential therapeutic or induced compounds for RIF, among which fulvestrant (estrogen receptor antagonist), bisindolylmaleimide-ix (CDK and PKC inhibitor), and JNK-9L (JNK inhibitor) were thought to influence the pathogenic process of RIF. Furthermore, our findings revealed the level of immune infiltration in RIF by highlighting three signaling pathways (Wnt/-catenin signaling, Notch signaling, and immune response) and three potential diagnostic DEGs (AKT1, PSMB8, and PSMD10).ConclusionImportantly, our findings may contribute to the scientific basis for several potential therapeutic agents to improve endometrial receptivity.
| S-EPMC9909740 | biostudies-literature