Whole-genome bisulfite sequencing identified the key role of the Src family tyrosine kinases and related genes in systemic lupus erythematosus.

BACKGROUND: As a multisystemic autoimmune illness, the basic mechanisms behind the pathophysiology of systemic lupus erythematosus (SLE) remain poorly understood. OBJECTIVE: We aimed to investigate the possible significance of SLE's DNA methylation and gain further insight into potential SLE-related biomarkers and therapeutic targets. METHODS: We used whole genome bisulfite sequencing (WGBS) method to analyze DNA methylation in 4 SLE patients and 4 healthy people. RESULTS: 702 differentially methylated regions (DMRs) were identified, and 480 DMR-associated genes were annotated. We found the majority of the DMR-associated elements were enriched in repeat and gene bodies. The top 10 hub genes identified were LCK, FYB, PTK2B, LYN, CTNNB1, MAPK1, GNAQ, PRKCA, ABL1, and CD247. Compared to the control group, LCK and PTK2B had considerably decreased levels of mRNA expression in the SLE group. Receiver operating characteristic (ROC) curve suggested that LCK and PTK2B may be potential candidate biomarkers to predict SLE. CONCLUSIONS: Our study improved comprehension of the DNA methylation patterns of SLE and identified potential biomarkers and therapeutic targets for SLE.

Bioinformatics Analysis for Identification of Key Genes in Salivary Gland and the Potential of a Combination of Biomarkers for the Diagnosis of SS.

PURPOSE: Sjögren's syndrome (SS) is a systemic autoimmune disease mainly characterized by dysfunction of exocrine glands. Studies on diagnosis models specific for SS patients are very limited. We aimed to use gene expression datasets from salivary glands to identify aberrant differentially expressed genes (DEGs) and pathways by bioinformatics and validate candidate genes by clinical minor labial gland biopsy (MSGB) samples, and finally build a combined gene quantitative diagnosis model of SS. PATIENTS AND METHODS: Original datasets GSE23117, GSE7451, and GSE127952 were obtained from the Gene Expression Omnibus database (GEO) and integrated and analyzed for differentially expressed genes in SS salivary glands. ClueGO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of upregulated and downregulated DEGs were performed, and protein-protein interaction (PPI) networks were constructed using the STRING and Cytoscape database. H&E staining and immunohistochemistry were used to validate the expression levels of four hub genes in salivary glands. Finally, a receiver operating characteristic (ROC) curve of the combined diagnosis of four hub genes was analyzed in SS patients and non-SS patients in order to explore the diagnostic efficacy of these genes compared with conventional FS in SS. RESULTS: Fifty-three upregulated genes and fifteen downregulated genes were identified. We analyzed the expression and function of four hub genes via H&E, immunohistochemistry, and ROC analysis. We then evaluated and verified the diagnosis value of four hub genes, STAT1, MNDA, IL10RA, and CCR1 in MSGB of SS and non-SS. A combined diagnosis model of four indicators was established to identify patients' discrete data on the foci size (AUC=0.915). CONCLUSION: The expression of STAT1, MNDA, and IL10RA may be potential biological indicators for SS diagnosis. Compared with FS, a combined diagnosis model of quantitative gene expression could potentially contribute to improving the sensitivity and specificity of MSGB of SS.

An evidence-based approach to assess the accuracy of diffusion kurtosis imaging in characterization of gliomas.

OBJECTIVE: Accurate and noninvasive pathologic grading of glioma patients before surgery was crucial to guiding clinicians to select appropriate treatment and improve patient prognosis. This study was performed to investigate the potential diagnostic value of diffusion kurtosis imaging (DKI) to distinguish high-grade gliomas (HGGs) from low-grade gliomas (LGGs) based on an evidence-based approach. METHODS: Relevant articles that used DKI to distinguish HGG from LGG in Embase, PubMed, China Knowledge Resource Integrated database (CNKI), Web of Knowledge, and Cochrane Libraries databases were electronically searched to April 31, 2018 by 2 reviewers. All analysis was performed by using Meta-disc1.4 and Stata. Influence factors on the diagnostic accuracy were evaluated using meta-regression analysis. RESULTS: Five eligible studies were included in this meta-analysis. The pooled sensitivity (SEN) and specificity (SPE) was 91% (confidence interval [CI]: 0.78-0.96; P = .02) and 91% (CI: 0.80-0.97; P = .01). The pooled data showed that diagnostic odds ratio (DOR) of DKI was 79.75 (CI: 31.57-201.45). The area under the curve (AUC) of summary receiver operating characteristic curve was 0.96. There is no evidence that our research has a threshold effect (Spearman correlation coefficient: 0.300, P = .624) and publication bias. Meta regression analysis identified that country, language, field strength, and parameter of magnetic resonance imaging had no significant effect on diagnostic performance. CONCLUSION: The present meta-analysis shows that the mean kurtosis values derived from DKI may be useful in characterization of gliomas with high sensitivity and specificity. Taken into consideration the small sample of this study, we need to be cautious when interpreting the results of this study.