Identification and verification of feature biomarkers associated in heart failure by bioinformatics analysis

Yu, Yi-Ding; Xue, Yi-Tao; Li, Yan

doi:10.1038/s41598-023-30666-0

Cited by 6 publications

(5 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a type 2 diabetic rat model, PHB overexpression alleviated insulin resistance, left ventricular dysfunction, and fibrosis, suggesting its promise in treating human DCM 27 . In addition, FOSB (significant in multiple tissues) has been identified as a potential diagnostic biomarker and therapeutic target for heart failure in a recent study 28 . We also notice that ERP29 ( P = 7.12 × 10 −9 in artery tibial), involved in Connexin43 (Cx43) hemichannel assembly, plays a crucial role in Cx43 stability.…”

Section: Resultsmentioning

confidence: 99%

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations

Song,

Wang,

Hou

et al. 2024

Nat Commun

View full text Add to dashboard Cite

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

show abstract

Section: Resultsmentioning

confidence: 99%

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations

Song,

Wang,

Hou

et al. 2024

Nat Commun

View full text Add to dashboard Cite

show abstract

“…Through the use of large-scale data, bioinformatics techniques offer a thorough knowledge of numerous illnesses at the molecular level [ 14 , 15 ]. Moreover, it is also particularly important for identifying potential biomarkers for the diagnosis and prognosis of human diseases [ 16 , 17 ]. Nevertheless, there were few reports on their utilization for screening potential biomarkers in patients with periodontitis combined with MS.…”

Section: Discussionmentioning

confidence: 99%

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis

Wu,

Cheng,

Zhang

et al. 2024

BMC Oral Health

View full text Add to dashboard Cite

Background Although periodontitis has previously been reported to be linked with multiple sclerosis (MS), but the molecular mechanisms and pathological interactions between the two remain unclear. This study aims to explore potential crosstalk genes and pathways between periodontitis and MS. Methods Periodontitis and MS data were obtained from the Gene Expression Omnibus (GEO) database. Shared genes were identified by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Then, enrichment analysis for the shared genes was carried out by multiple methods. The least absolute shrinkage and selection operator (LASSO) regression was used to obtain potential shared diagnostic genes. Furthermore, the expression profile of 28 immune cells in periodontitis and MS was examined using single-sample GSEA (ssGSEA). Finally, real-time quantitative fluorescent PCR (qRT-PCR) and immune histochemical staining were employed to validate Hub gene expressions in periodontitis and MS samples. Results FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ genes were the shared genes between periodontitis, and MS. GO analysis revealed that the shared genes exhibited the greatest enrichment in response to molecules of bacterial origin. LASSO analysis indicated that CFI, DDIT4L, and FAM46C were the most effective shared diagnostic biomarkers for periodontitis and MS, which were further validated by qPCR and immunohistochemical staining. ssGSEA analysis revealed that T and B cells significantly influence the development of MS and periodontitis. Conclusions FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ were the most important crosstalk genes between periodontitis, and MS. Further studies found that CFI, DDIT4L, and FAM46C were potential biomarkers in periodontitis and MS.

show abstract

“…The process of data preprocessing and identifying differentially expressed genes (DEGs) began with the use of a Perl script to convert the probe matrix into a gene matrix, according to the annotation file. We excluded probes associated with multiple genes and calculated the average of multiple probes representing the same gene to establish the gene's final expression level ( 28 ). Next, we employed the “limma” package along with the combat function from the “sva” package to adjust for batch effects in the two datasets and to merge them ( 29 , 30 ), revealing a total of 22,877 expressed genes.…”

Section: Methodsmentioning

confidence: 99%

Identification of atrial fibrillation-related genes through transcriptome data analysis and Mendelian randomization

Zhang,

Lian,

Nie

et al. 2024

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

BackgroundAtrial fibrillation (AF) is a common persistent arrhythmia characterized by rapid and chaotic atrial electrical activity, potentially leading to severe complications such as thromboembolism, heart failure, and stroke, significantly affecting patient quality of life and safety. As the global population ages, the prevalence of AF is on the rise, placing considerable strains on individuals and healthcare systems. This study utilizes bioinformatics and Mendelian Randomization (MR) to analyze transcriptome data and genome-wide association study (GWAS) summary statistics, aiming to identify biomarkers causally associated with AF and explore their potential pathogenic pathways.MethodsWe obtained AF microarray datasets GSE41177 and GSE79768 from the Gene Expression Omnibus (GEO) database, merged them, and corrected for batch effects to pinpoint differentially expressed genes (DEGs). We gathered exposure data from expression quantitative trait loci (eQTL) and outcome data from AF GWAS through the IEU Open GWAS database. We employed inverse variance weighting (IVW), MR-Egger, weighted median, and weighted model approaches for MR analysis to assess exposure-outcome causality. IVW was the primary method, supplemented by other techniques. The robustness of our results was evaluated using Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out sensitivity analysis. A “Veen” diagram visualized the overlap of DEGs with significant eQTL genes from MR analysis, referred to as common genes (CGs). Additional analyses, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and immune cell infiltration studies, were conducted on these intersecting genes to reveal their roles in AF pathogenesis.ResultsThe combined dataset revealed 355 differentially expressed genes (DEGs), with 228 showing significant upregulation and 127 downregulated. Mendelian randomization (MR) analysis identified that the autocrine motility factor receptor (AMFR) [IVW: OR = 0.977; 95% CI, 0.956–0.998; P = 0.030], leucine aminopeptidase 3 (LAP3) [IVW: OR = 0.967; 95% CI, 0.934–0.997; P = 0.048], Rab acceptor 1 (RABAC1) [IVW: OR = 0.928; 95% CI, 0.875–0.985; P = 0.015], and tryptase beta 2 (TPSB2) [IVW: OR = 0.971; 95% CI, 0.943–0.999; P = 0.049] are associated with a reduced risk of atrial fibrillation (AF). Conversely, GTPase-activating SH3 domain-binding protein 2 (G3BP2) [IVW: OR = 1.030; 95% CI, 1.004–1.056; P = 0.024], integrin subunit beta 2 (ITGB2) [IVW: OR = 1.050; 95% CI, 1.017–1.084; P = 0.003], glutaminyl-peptide cyclotransferase (QPCT) [IVW: OR = 1.080; 95% CI, 1.010–0.997; P = 1.154], and tripartite motif containing 22 (TRIM22) [IVW: OR = 1.048; 95% CI, 1.003–1.095; P = 0.035] are positively associated with AF risk. Sensitivity analyses indicated a lack of heterogeneity or horizontal pleiotropy (P > 0.05), and leave-one-out analysis did not reveal any single nucleotide polymorphisms (SNPs) impacting the MR results significantly. GO and KEGG analyses showed that CG is involved in processes such as protein polyubiquitination, neutrophil degranulation, specific and tertiary granule formation, protein-macromolecule adaptor activity, molecular adaptor activity, and the SREBP signaling pathway, all significantly enriched. The analysis of immune cell infiltration demonstrated associations of CG with various immune cells, including plasma cells, CD8T cells, resting memory CD4T cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells, activated mast cells, and neutrophils.ConclusionBy integrating bioinformatics and MR approaches, genes such as AMFR, G3BP2, ITGB2, LAP3, QPCT, RABAC1, TPSB2, and TRIM22 are identified as causally linked to AF, enhancing our understanding of its molecular foundations. This strategy may facilitate the development of more precise biomarkers and therapeutic targets for AF diagnosis and treatment.

show abstract

Identification and verification of feature biomarkers associated in heart failure by bioinformatics analysis

Cited by 6 publications

References 56 publications

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis

Identification of atrial fibrillation-related genes through transcriptome data analysis and Mendelian randomization

Contact Info

Product

Resources

About