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Bioinformatic Analysis Identifies Biomarkers and Treatment Targets in Primary Sjogren's Syndrome Patients with Fatigue.

ABSTRACT: We aim to identify the common genes, biological pathways, and treatment targets for primary Sjögren's syndrome patients with varying degrees of fatigue features. We select datasets about transcriptomic analyses of primary Sjögren's syndrome (pSS) patients with different degrees of fatigue features and normal controls in peripheral blood. We identify common differentially expressed genes (DEGs) to find shared pathways and treatment targets for pSS patients with fatigue and design a protein-protein interaction (PPI) network by some practical bioinformatic tools. And hub genes are detected based on the PPI network. We perform biological pathway analysis of common genes by Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Lastly, potential treatment targets for pSS patients with fatigue are found by the Enrichr platform. We discovered that 27 DEGs are identified in pSS patients with fatigue features and the severe fatigued pSS-specific gene is RTP4. DEGs are mainly localized in the mitochondria, endosomes, endoplasmic reticulum, and cytoplasm and are involved in the biological process by which interferon acts on cells and cells defend themselves against viruses. Molecular functions mainly involve the process of RNA synthesis. The DEGs of pSS are involved in the signaling pathways of viruses such as hepatitis C, influenza A, measles, and EBV. Acetohexamide PC3 UP, suloctidil HL60 UP, prenylamine HL60 UP, and chlorophyllin CTD 00000324 are the four most polygenic drug molecules. PSS patients with fatigue features have specific gene regulation, and chlorophyllin may alleviate fatigue symptoms in pSS patients.

SUBMITTER: Chen G

PROVIDER: S-EPMC8783724 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease, where patients often suffer from fatigue. Biological pathways underlying fatigue are unknown. In this study aptamer-based SOMAscan technology is used to identify potential biomarkers and treatment targets for fatigue in pSS. Methods: SOMAscan® Assay 1.3k was performed on serum samples of healthy controls (HCs) and pSS patients characterized for interferon upregulation and fatigue. Differentially expressed proteins (DEPs) between pSS patients and HC or fatigued and non-fatigued pSS patients were validated and discriminatory capacity of markers was tested using independent technology. Results: Serum concentrations of over 1,300 proteins were compared between 63 pSS patients and 20 HCs resulting in 58 upregulated and 46 downregulated proteins. Additionally, serum concentrations of 30 interferon positive (IFNpos) and 30 interferon negative (IFNneg) pSS patients were compared resulting in 25 upregulated and 13 downregulated proteins. ELISAs were performed for several DEPs between pSS patients and HCs or IFNpos and IFNneg all showing a good correlation between protein levels measured by ELISA and relative fluorescence units (RFU) measured by the SOMAscan. Comparing 22 fatigued and 23 non-fatigued pSS patients, 16 serum proteins were differentially expressed, of which 14 were upregulated and 2 were downregulated. Top upregulated DEPs included neuroactive synaptosomal-associated protein 25 (SNAP-25), alpha-enolase (ENO1) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1). Furthermore, the proinflammatory mediator IL36a and several complement factors were upregulated in fatigued compared to non-fatigued pSS patients. ROC analysis indicated that DEPs showed good capacity to discriminate fatigued and non-fatigued pSS patients. Conclusion: In this study we validated the use of aptamer-based proteomics and identified a novel set of proteins which were able to distinguish fatigued from non-fatigued pSS patients and identified a so-called "fatigue signature."

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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Bioinformatic Analysis Identifies Biomarkers and Treatment Targets in Primary Sjogren's Syndrome Patients with Fatigue.

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