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Transcriptome Sequencing Reveals Potential Roles of ICOS in Primary Sjogren's Syndrome.

ABSTRACT: Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease characterized by exocrine gland damage and extraglandular involvements. To identify potential biomarkers for the early detection of pSS and to further investigate the potential roles of the biomarkers in the progression of pSS, our previous RNA sequencing data and four microarray data of salivary glands (SGs) were combined for integrative transcriptome analysis between pSS and non-pSS. Differential gene expression analysis, gene co-expression network analysis, and pathway analysis were conducted to detect hub genes, which were subsequently investigated in peripheral blood mononuclear cell (PBMC) and plasma. Correlation analysis, single-gene Gene Set Enrichment Analysis, and receiver operating characteristic (ROC) curve were applied to investigate the potential function of the hub genes and their classification capacity for pSS. A total of 51 common up-regulated genes were identified among different pSS cohorts. A key module was found to be the most closely linked to pSS, which was significantly associated with inflammation-related pathways. Seven overlapped hub genes (ICOS, SELL, CR2, BANK1, MS4A1, ZC3H12D, and CCR7) were identified, among which ICOS was demonstrated to be involved in most crucial immune pathways. ICOS was up-regulated not only in SGs but also in PBMC and plasma in pSS, and the expression of ICOS was closely associated with lymphocytic infiltration in SGs and disease activity of pSS patients. It showed a strong classification capacity with classic clinical index in SGs (ROC curve 0.9821) and significant distinct discrimination in PBMC (ROC curve 0.9107). These findings are expected to gain a further insight into the pathogenesis of pSS and provide a promising candidate for the early detection of pSS.

SUBMITTER: Luo J

PROVIDER: S-EPMC7747463 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Transcriptome Sequencing Reveals Potential Roles of <i>ICOS</i> in Primary Sjögren's Syndrome.

Luo Jing J Liao Xin X Zhang Lihe L Xu Xin X Ying Senhong S Yu Mengjiao M Zhu Lixia L Lin Suxian S Wang Xiaobing X

Frontiers in cell and developmental biology 20201204

Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease characterized by exocrine gland damage and extraglandular involvements. To identify potential biomarkers for the early detection of pSS and to further investigate the potential roles of the biomarkers in the progression of pSS, our previous RNA sequencing data and four microarray data of salivary glands (SGs) were combined for integrative transcriptome analysis between pSS and non-pSS. Differential gene expression analysis ...[more]

PMID: 33344450

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Project description:Background: Using machine learning based on metabolomics, this study aimed to construct an effective primary Sjogren's syndrome (pSS) diagnostics model and reveal the potential targets and biomarkers of pSS. Methods: From a total of 39 patients with pSS and 38 healthy controls (HCs), serum specimens were collected. The samples were analyzed by ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry. Three machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost), were used to build the pSS diagnosis models. Afterward, four machine learning methods were used to reduce the dimensionality of the metabolomics data. Finally, metabolites with significant differences were screened and pathway analysis was conducted. Results: The area under the curve (AUC), sensitivity, and specificity of LASSO, RF and XGBoost test set all reached 1.00. Orthogonal partial least squares discriminant analysis was used to classify the metabolomics data. By combining the results of the univariate false discovery rate and the importance of the variable in projection, we identified 21 significantly different metabolites. Using these 21 metabolites for diagnostic modeling, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00. Metabolic pathway analysis revealed that these 21 metabolites are highly correlated with amino acid and lipid metabolisms. On the basis of 21 metabolites, we screened the important variables in the models. Further, five common variables were obtained by intersecting the important variables of three models. Based on these five common variables, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00.2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were found to be potential targets and specific biomarkers for pSS. Conclusion: The combination of machine learning and metabolomics can accurately distinguish between patients with pSS and HCs. 2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were potential targets and biomarkers for pSS.

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Transcriptome Sequencing Reveals Potential Roles of ICOS in Primary Sjogren's Syndrome.

Publications

Transcriptome Sequencing Reveals Potential Roles of <i>ICOS</i> in Primary Sjögren's Syndrome.

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