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A transcriptomics-based meta-analysis identifies a cross-tissue signature for sarcoidosis.

ABSTRACT: Sarcoidosis is a granulomatous disease of unknown etiology, immunologically characterized by a Th1 immune response. Transcriptome-wide expression studies in various types of sarcoid tissues contributed to better understanding of disease mechanisms. We performed a systematic database search on Gene Expression Omnibus (GEO) and utilized transcriptomic data from blood and sarcoidosis-affected tissues in a meta-analysis to identify a cross-tissue, cross-platform signature. Datasets were further separated into training and testing sets for development of a diagnostic classifier for sarcoidosis. A total of 690 differentially expressed genes were identified in the analysis among various tissues. 29 of the genes were robustly associated with sarcoidosis in the meta-analysis both in blood and in lung-associated tissues. Top genes included LINC01278 (P = 3.11 × 10^-13), GBP5 (P = 5.56 × 10^-07), and PSMB9 (P = 1.11 × 10^-06). Pathway enrichment analysis revealed activated IFN-γ, IL-1, and IL-18, autophagy, and viral infection response. IL-17 was observed to be enriched in peripheral blood specific signature genes. A 16-gene classifier achieved excellent performance in the independent validation data (AUC 0.711-0.964). This study provides a cross-tissue meta-analysis for expression profiles of sarcoidosis and identifies a diagnostic classifier that potentially can complement more invasive procedures.

SUBMITTER: Jiang Y

PROVIDER: S-EPMC9530451 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A transcriptomics-based meta-analysis identifies a cross-tissue signature for sarcoidosis.

Jiang Yale Y Jiang Dingyuan D Costabel Ulrich U Dai Huaping H Wang Chen C

Frontiers in medicine 20220920

Sarcoidosis is a granulomatous disease of unknown etiology, immunologically characterized by a Th1 immune response. Transcriptome-wide expression studies in various types of sarcoid tissues contributed to better understanding of disease mechanisms. We performed a systematic database search on Gene Expression Omnibus (GEO) and utilized transcriptomic data from blood and sarcoidosis-affected tissues in a meta-analysis to identify a cross-tissue, cross-platform signature. Datasets were further sepa ...[more]

PMID: 36203777

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Project description:BackgroundSarcoidosis is a multisystem granulomatous disease of unknown origin with a variable and often unpredictable course and pattern of organ involvement. In this study we sought to identify specific bronchoalveolar lavage (BAL) cell gene expression patterns indicative of distinct disease phenotypic traits.MethodsRNA sequencing by Ion Torrent Proton was performed on BAL cells obtained from 215 well-characterised patients with pulmonary sarcoidosis enrolled in the multicentre Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Weighted gene co-expression network analysis and nonparametric statistics were used to analyse genome-wide BAL transcriptome. Validation of results was performed using a microarray expression dataset of an independent sarcoidosis cohort (Freiburg, Germany; n=50).ResultsOur supervised analysis found associations between distinct transcriptional programmes and major pulmonary phenotypic manifestations of sarcoidosis including T-helper type 1 (Th1) and Th17 pathways associated with hilar lymphadenopathy, transforming growth factor-β1 (TGFB1) and mechanistic target of rapamycin (MTOR) signalling with parenchymal involvement, and interleukin (IL)-7 and IL-2 with airway involvement. Our unsupervised analysis revealed gene modules that uncovered four potential sarcoidosis endotypes including hilar lymphadenopathy with increased acute T-cell immune response; extraocular organ involvement with PI3K activation pathways; chronic and multiorgan disease with increased immune response pathways; and multiorgan involvement, with increased IL-1 and IL-18 immune and inflammatory responses. We validated the occurrence of these endotypes using gene expression, pulmonary function tests and cell differentials from Freiburg.ConclusionTaken together, our results identify BAL gene expression programmes that characterise major pulmonary sarcoidosis phenotypes and suggest the presence of distinct disease molecular endotypes.

Project description:Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer's Disease (AD) and greater apolipoprotein E4-related (APOE ε4) AD risk compared to males, molecular signatures underlying these differences remain elusive. Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer's disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD. Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the APOE ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD patients. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. Conclusion: These results help identify sex and APOE ε4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

Project description:AimThis study aimed to identify key genes, non-coding RNAs, and their possible regulatory interactions during gallbladder cancer (GBC).BackgroundThe early detection of GBC, i.e. before metastasis, is restricted by our limited knowledge of molecular markers and mechanism(s) involved during carcinogenesis. Therefore, identifying important disease-associated transcriptome-level alterations can be of clinical importance.MethodsIn this study, six NCBI-GEO microarray dataseries of GBC and control tissue samples were analyzed to identify differentially expressed genes (DEGs) and non-coding RNAs {microRNAs (DEmiRNAs) and long non-coding RNAs (DElncRNAs)} with a computational meta-analysis approach. A series of bioinformatic methods were applied to enrich functional pathways, create protein-protein interaction networks, identify hub genes, and screen potential targets of DEmiRNAs and DElncRNAs. Expression and interaction data were consolidated to reveal putative DElncRNAs:DEmiRNAs:DEGs interactions.ResultsIn total, 351 DEGs (185 downregulated, 166 upregulated), 787 DEmiRNAs (299 downregulated, 488 upregulated), and 7436 DElncRNAs (3127 downregulated, 4309 upregulated) were identified. Eight genes (FGF, CDK1, RPN2, SEC61A1, SOX2, CALR, NGFR, and NCAM) were identified as hub genes. Genes associated with ubiquitin ligase activity, N-linked glycosylation, and blood coagulation were upregulated, while those for cell-cell adhesion, cell differentiation, and surface receptor-linked signaling were downregulated. DEGs-DEmiRNAs-DElncRNAs interaction network identified 46 DElncRNAs to be associated with 28 DEmiRNAs, consecutively regulating 27 DEGs. DEmiRNAs-hsa-miR-26b-5p and hsa-miR-335-5p; and DElnRNAs-LINC00657 and CTB-89H12.4 regulated the highest number of DEGs and DEmiRNAs, respectively.ConclusionThe current study has identified meaningful transcriptome-level changes and gene-miRNA-lncRNA interactions during GBC and laid a platform for future studies on novel prognostic and diagnostic markers in GBC.

Dataset Information

A transcriptomics-based meta-analysis identifies a cross-tissue signature for sarcoidosis.

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A transcriptomics-based meta-analysis identifies a cross-tissue signature for sarcoidosis.

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