Dataset Information

A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.

ABSTRACT: Early risk assessments and interventions for metabolic syndrome (MetS) are limited because of a lack of effective biomarkers. In the present study, several candidate genes were selected as a blood-based transcriptomic signature for MetS. We collected so far the largest MetS-associated peripheral blood high-throughput transcriptomics data and put forward a novel feature selection strategy by combining weighted gene co-expression network analysis, protein-protein interaction network analysis, LASSO regression and random forest approaches. Two gene modules and 51 hub genes as well as a 9-hub-gene signature associated with metabolic syndrome were identified. Then, based on this 9-hub-gene signature, we performed logistic analysis and subsequently established a web nomogram calculator for metabolic syndrome risk (https://xjtulgz.shinyapps.io/DynNomapp/). This 9-hub-gene signature showed excellent classification and calibration performance (AUC = 0.968 in training set, AUC = 0.883 in internal validation set, AUC = 0.861 in external validation set) as well as ideal potential clinical benefit.

SUBMITTER: Liu G

PROVIDER: S-EPMC8806918 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.

Liu Guanzhi G Luo Sen S Lei Yutian Y Wu Jianhua J Huang Zhuo Z Wang Kunzheng K Yang Pei P Huang Xin X

Bioengineered 20211201 1

Early risk assessments and interventions for metabolic syndrome (MetS) are limited because of a lack of effective biomarkers. In the present study, several candidate genes were selected as a blood-based transcriptomic signature for MetS. We collected so far the largest MetS-associated peripheral blood high-throughput transcriptomics data and put forward a novel feature selection strategy by combining weighted gene co-expression network analysis, protein-protein interaction network analysis, LASS ...[more]

PMID: 34516309

Similar Datasets

Project description:BackgroundKawasaki disease (KD) is a systemic vasculitis primarily affecting the coronary arteries in children. Despite growing attention to its symptoms and pathogenesis, the exact mechanisms of KD remain unclear. Mitophagy plays a critical role in inflammation regulation, however, its significance in KD has only been minimally explored. This study sought to identify crucial mitophagy-related biomarkers and their mechanisms in KD, focusing on their association with immune cells in peripheral blood.MethodsThis research used four datasets from the Gene Expression Omnibus (GEO) database that were categorized as the merged and validation datasets. Screening for differentially expressed mitophagy-related genes (DE-MRGs) was conducted, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A weighted gene co-expression network analysis (WGCNA) identified the hub module, while machine-learning algorithms [random forest-recursive feature elimination (RF-RFE) and support vector machine-recursive feature elimination (SVM-RFE)] pinpointed the hub genes. Receiver operating characteristic (ROC) curves were generated for these genes. Additionally, the CIBERSORT algorithm was used to assess the infiltration of 22 immune cell types to explore their correlations with hub genes. Interactions between transcription factors (TFs), genes, and Gene-microRNAs (miRNAs) of hub genes were mapped using the NetworkAnalyst platform. The expression difference of the hub genes was validated using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR).ResultsInitially, 306 DE-MRGs were identified between the KD patients and healthy controls. The enrichment analysis linked these MRGs to autophagy, mitochondrial function, and inflammation. The WGCNA revealed a hub module of 47 KD-associated DE-MRGs. The machine-learning algorithms identified cytoskeleton-associated protein 4 (CKAP4) and serine-arginine protein kinase 1 (SRPK1) as critical hub genes. In the merged dataset, the area under the curve (AUC) values for CKAP4 and SRPK1 were 0.933 [95% confidence interval (CI): 0.901 to 0.964] and 0.936 (95% CI: 0.906 to 0.966), respectively, indicating high diagnostic potential. The validation dataset results corroborated these findings with AUC values of 0.872 (95% CI: 0.741 to 1.000) for CKAP4 and 0.878 (95% CI: 0.750 to 1.000) for SRPK1. The CIBERSORT analysis connected CKAP4 and SRPK1 with specific immune cells, including activated cluster of differentiation 4 (CD4) memory T cells. TFs such as MAZ, SAP30, PHF8, KDM5B, miRNAs like hsa-mir-7-5p play essential roles in regulating these hub genes. The qRT-PCR results confirmed the differential expression of these genes between the KD patients and healthy controls.ConclusionsCKAP4 and SRPK1 emerged as promising diagnostic biomarkers for KD. These genes potentially influence the progression of KD through mitophagy regulation.

Project description:BackgroundThe unfolded protein response (UPR) is a critical biological process related to a variety of physiological functions and cardiac disease. However, the role of UPR-related genes in acute myocardial infarction (AMI) has not been well characterized. Therefore, this study aims to elucidate the mechanism and role of the UPR in the context of AMI.MethodsGene expression profiles related to AMI and UPR pathway were downloaded from the Gene Expression Omnibus database and PathCards database, respectively. Differentially expressed genes (DEGs) were identified and then functionally annotated. The random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify potential diagnostic UPR-AMI biomarkers. Furthermore, the results were validated by using external data sets, and discriminability was measured by the area under the curve (AUC). A nomogram based on the feature genes was developed to predict the AMI-risk rate. Then we utilized two algorithms, CIBERSORT and MCPcounter, to investigate the relationship between the key genes and immune microenvironment. Additionally, we performed uniform clustering of AMI samples based on the expression of UPR pathway-related genes. The weighted gene co-expression network analysis was conducted to identify the key modules in various clusters, enrichment analysis was performed for the genes existing in different modules.ResultsA total of 14 DEGs related to the UPR pathway were identified. Among the 14 DEGs, CEBPB, ATF3, EIF2S3, and TSPYL2 were subsequently identified as biomarkers by the LASSO and RF algorithms. A diagnostic model was constructed with these four genes, and the AUC was 0.939. The calibration curves, receiver operating characteristic (ROC) curves, and the decision curve analysis of the nomogram exhibited good performance. Furthermore, immune cell infiltration analysis revealed that four feature genes were linked with the infiltration of immune cells such as neutrophils. The cluster analysis of the AMI samples identified two distinct clusters, each with differential expression of genes related to the UPR pathway, immune cell infiltration, and inflammatory cytokine secretion. Weighted gene coexpression network analysis and enrichment analysis showed that both clusters were associated with the UPR.ConclusionsOur study highlights the importance of the UPR pathway in the pathogenesis of myocardial infarction, and identifies four genes CEBPB, ATF3, EIF2S3, and TSPYL2 as diagnostic biomarkers for AMI, providing new ideas for the clinical diagnosis and treatment of AMI.

Project description:Spinal cord injury (SCI) is a profound ailment lacking a well-defined molecular mechanism and effective treatments. Cuproptosis, identified as a recently discovered cell death pathway, exhibits diverse roles in various cancers. Nevertheless, its involvement in SCI is yet to be elucidated. Firstly, the RNA sequencing data of 1, 3, 7 dpi SCI samples were collected from GEO database. We performed differential expression analysis on these samples with varying cuproptosis-related scores calculating by ssGSEA. Subsequently, we conducted enrichment analyses with KEGG, GO, and GSEA. Simultaneously, we executed WGCNA analysis using cuproptosis-related scores, selecting the most relevant module for enrichment analysis. Hub genes were identified at the intersection of PPI analysis results from two modules and cuproptosis-related DEGs. Additionally, relying on the immune infiltration landscape associated with cuproptosis, we carried out immune cell correlation analysis on hub genes. Finally, to corroborate our earlier findings, we utilized single-cell RNA-seq analysis and in vitro experimental validation. Based on ssGSEA, differential expression analysis and WGCNA analysis, we identified two modules that were highly relevant to cell division and immune processes, respectively. From these modules, we identified two hub genes, Cd48 and Mpeg1, which exhibited a strong positive correlation (R = 0.92) and shared similar pathways. Furthermore, we observed a positive correlation between M2 macrophages and Cd48/Mpeg1. To validate our findings, we performed external cohort validation using a single-cell RNA sequencing dataset. The results confirmed that Mpeg1 was highly expressed in microglia (macrophages in center nervous system) following spinal cord injury. Additionally, we conducted in vitro experiments to further validate the molecular functions of Mpeg1 in SCI. In summary, targeting Mpeg1, as well as cuproptosis and immune cell infiltration, holds promise as a potential strategy for reducing spinal cord tissue damage and promoting recovery after SCI. These findings provide valuable insights for future therapeutic interventions.

Project description:Background: Aortic dissection (AD) is a life-threatening disease. Chromatin regulators (CRs) are indispensable epigenetic regulators. We aimed to identify differentially expressed chromatin regulators (DECRs) for AD diagnosis. Methods: We downloaded the GSE52093 and GSE190635 datasets from the Gene Expression Omnibus database. Following the merging and processing of datasets, bioinformatics analysis was applied to select candidate DECRs for AD diagnosis: CRs exertion; DECR identification using the "Limma" package; analyses of enrichment of function and signaling pathways; construction of protein-protein interaction (PPI) networks; application of machine-learning algorithms; evaluation of receiver operating characteristic (ROC) curves. GSE98770 served as the validation dataset to filter DECRs. Moreover, we collected peripheral-blood samples to further validate expression of DECRs by real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Finally, a nomogram was built for clinical use. Results: A total of 841 CRs were extracted from the merged dataset. Analyses of functional enrichment of 23 DECRs identified using Limma showed that DECRs were enriched mainly in epigenetic-regulation processes. From the PPI network, 17 DECRs were selected as node DECRs. After machine-learning calculations, eight DECRs were chosen from the intersection of 13 DECRs identified using support vector machine recursive feature elimination (SVM-RFE) and the top-10 DECRs selected using random forest. DECR expression between the control group and AD group were considerably different. Moreover, the area under the ROC curve (AUC) of each DECR was >0.75, and four DECRs (tumor protein 53 (TP53), chromobox protein homolog 7 (CBX7), Janus kinase 2 (JAK2) and cyclin-dependent kinase 5 (CDK5)) were selected as candidate biomarkers after validation using the external dataset and clinical samples. Furthermore, a nomogram with robust diagnostic value was established (AUC = 0.960). Conclusion: TP53, CBX7, JAK2, and CDK5 might serve as diagnostic DECRs for AD diagnosis. These DECRs were enriched predominantly in regulating epigenetic processes.

Dataset Information

A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.

Publications

A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets