Dataset Information

Identification of Immune Cell Landscape and Construction of a Novel Diagnostic Nomogram for Crohn's Disease.

ABSTRACT: Crohn's disease (CD) has an increasing incidence and prevalence worldwide. The etiology of CD remains unclear and there is no gold standard for diagnosis. The dysregulated immune response and different infiltration status of immune cells are critical for CD pathogenesis; therefore, it is important to provide an overview of immune-cell alterations in CD and explore a novel method for auxiliary diagnosis. Here we analyzed microarray datasets from Gene Expression Omnibus (GEO), and an extended version of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORTx) was utilized to estimate the fraction of 22 types of immune cells. Differentially expressed genes (DEGs) and a protein-protein interaction (PPI) network were identified, and we performed gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) to identify differentially regulated pathways in CD. Least absolute shrinkage and selection operator (LASSO) regression was conducted to filter features, and a diagnostic nomogram based on logistic regression was built and validated in an independent validation cohort. In the derivation cohort, we found a proportion of 17 immune-cell types to be significantly altered between CD and healthy controls and a total of 150 DEGs were identified, which were mostly related to the immune response. Among the 15 hub genes based on the PPI network, C-X-C chemokine ligand 8 (CXCL8) and interleukin-1B (IL-1B) showed the highest degree of interaction. Additionally, GSEA and GSVA identified five significantly enriched pathways, among which the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway was critical in the CD development. Furthermore, six variables comprising of CXCL8, IL-1B, M1 macrophages, regulatory T cells, CD8+ T cells, and plasma cells were identified by LASSO regression and incorporated into a logistic regression model. The nomogram displayed a good prediction, with a 0.915 area under the receiver operating curve (AUC) and a C-index of 0.915 [95% confidence interval (CI): 0.875-0.955]. Similar results were found in the validation cohort, with an AUC of 0.884 and a 0.884 C-index (95% CI: 0.843-0.924). These results provide novel in silico insight into cellular and molecular characteristics of CD and potential biomarkers for diagnosis and targeted therapy.

SUBMITTER: Chen H

PROVIDER: S-EPMC7212409 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of Immune Cell Landscape and Construction of a Novel Diagnostic Nomogram for Crohn's Disease.

Chen Hong H Chen Chunqiu C Yuan Xiaoqi X Xu Weiwei W Yang Mu-Qing MQ Li Qiwei Q Shen Zhenyu Z Yin Lu L

Frontiers in genetics 20200429

Crohn's disease (CD) has an increasing incidence and prevalence worldwide. The etiology of CD remains unclear and there is no gold standard for diagnosis. The dysregulated immune response and different infiltration status of immune cells are critical for CD pathogenesis; therefore, it is important to provide an overview of immune-cell alterations in CD and explore a novel method for auxiliary diagnosis. Here we analyzed microarray datasets from Gene Expression Omnibus (GEO), and an extended vers ...[more]

PMID: 32425988

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Project description:Schizophrenia (SCZ), which is characterized by debilitating neuropsychiatric disorders with significant cognitive impairment, remains an etiological and therapeutic challenge. Using transcriptomic profile analysis, disease-related biomarkers linked with SCZ have been identified, and clinical outcomes can also be predicted. This study aimed to discover diagnostic hub genes and investigate their possible involvement in SCZ immunopathology. The Gene Expression Omnibus (GEO) database was utilized to get SCZ Gene expression data. Differentially expressed genes (DEGs) were identified and enriched by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and disease ontology (DO) analysis. The related gene modules were then examined using integrated weighted gene co-expression network analysis. Single-sample gene set enrichment (GSEA) was exploited to detect immune infiltration. SVM-REF, random forest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to identify hub genes. A diagnostic model of nomogram was constructed for SCZ prediction based on the hub genes. The clinical utility of nomogram prediction was evaluated, and the diagnostic utility of hub genes was validated. mRNA levels of the candidate genes in SCZ rat model were determined. Finally, 24 DEGs were discovered, the majority of which were enriched in biological pathways and activities. Four hub genes (NEUROD6, NMU, PVALB, and NECAB1) were identified. A difference in immune infiltration was identified between SCZ and normal groups, and immune cells were shown to potentially interact with hub genes. The hub gene model for the two datasets was verified, showing good discrimination of the nomogram. Calibration curves demonstrated valid concordance between predicted and practical probabilities, and the nomogram was verified to be clinically useful. According to our research, NEUROD6, NMU, PVALB, and NECAB1 are prospective biomarkers in SCZ and that a reliable nomogram based on hub genes could be helpful for SCZ risk prediction.

Project description:BackgroundCoronary artery disease (CAD) is still a lethal disease worldwide. This study aims to identify clinically relevant diagnostic biomarker in CAD and explore the potential medications on CAD.MethodsGSE42148, GSE180081, and GSE12288 were downloaded as the training and validation cohorts to identify the candidate genes by constructing the weighted gene co-expression network analysis. Functional enrichment analysis was utilized to determine the functional roles of these genes. Machine learning algorithms determined the candidate biomarkers. Hub genes were then selected and validated by nomogram and the receiver operating curve. Using CIBERSORTx, the hub genes were further discovered in relation to immune cell infiltrability, and molecules associated with immune active families were analyzed by correlation analysis. Drug screening and molecular docking were used to determine medications that target the four genes.ResultsThere were 191 and 230 key genes respectively identified by the weighted gene co-expression network analysis in two modules. A total of 421 key genes found enriched pathways by functional enrichment analysis. Candidate immune-related genes were then screened and identified by the random forest model and the eXtreme Gradient Boosting algorithm. Finally, four hub genes, namely, CSF3R, EED, HSPA1B, and IL17RA, were obtained and used to establish the nomogram model. The receiver operating curve, the area under curve, and the calibration curve were all used to validate the accuracy and usefulness of the diagnostic model. Immune cell infiltrating was examined, and CAD patients were then divided into high- and low-expression groups for further gene set enrichment analysis. Through targeting the hub genes, we also found potential drugs for anti-CAD treatment by using the molecular docking method.ConclusionsCSF3R, EED, HSPA1B, and IL17RA are potential diagnostic biomarkers for CAD. CAD pathogenesis is greatly influenced by patterns of immune cell infiltration. Promising drugs offers new prospects for the development of CAD therapy.

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Project description:BackgroundCrohn's disease (CD) is a type of heterogeneous, dysfunctional immune-mediated intestinal chronic and recurrent inflammation caused by a variety of etiologies. Cuproptosis is a newly discovered form of programmed cell death that seems to contribute to the advancement of a variety of illnesses. Consequently, the major purpose of our research was to examine the role of cuproptosis-related genes in CD.MethodsWe obtained two CD datasets from the gene expression omnibus (GEO) database, and immune cell infiltration was created to investigate immune cell dysregulation in CD. Based on differentially expressed genes (DEGs) and the cuproptosis gene set, differentially expressed genes of cuproptosis (CuDEGs) were found. Then, candidate hub cuproptosis-associated genes were found using machine learning methods. Subsequently, using 437 CD samples, we explored two distinct subclusters based on hub cuproptosis-related genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Gene set variation analysis (GSVA) and immune infiltration analysis studies were also used to assess the distinct roles of the subclusters.ResultsOverall, 25 CuDEGs were identified, including ABCB6, BACE1, FDX1, GLS, LIAS, MT1M, PDHA1, etc. And most CuDEGs were expressed at lower levels in CD samples and were negatively related to immune cell infiltration. Through the machine learning algorithms, a seven gene cuproptosis-signature was identified and two cuproptosis-related subclusters were defined. Cluster-specific differentially expressed genes were found only in one cluster, and functional analysis revealed that they were involved in several immune response processes. And the results of GSVA showed positive significant enrichment in immune-related pathways in cluster A, while positive significant enrichment in metabolic pathways in cluster B. In addition, an immune infiltration study indicated substantial variation in immunity across different groups. Immunological scores were higher and immune infiltration was more prevalent in Cluster A.ConclusionAccording to the current research, the cuproptosis phenomenon occurs in CD and is correlated with immune cell infiltration and metabolic activity. This information indicates that cuproptosis may promote CD progression by inducing immunological response and metabolic dysfunction. This research has opened new avenues for investigating the causes of CD and developing potential therapeutic targets for the disease.

Dataset Information

Identification of Immune Cell Landscape and Construction of a Novel Diagnostic Nomogram for Crohn's Disease.

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Identification of Immune Cell Landscape and Construction of a Novel Diagnostic Nomogram for Crohn's Disease.

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