Disease Markers最新文献

[This retracts the article DOI: 10.1155/2022/2959846.].

[This retracts the article DOI: 10.1155/2022/7115181.].

[This retracts the article DOI: 10.1155/2022/1195875.].

[This retracts the article DOI: 10.1155/2022/9041466.].

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease with strong heterogeneity, leading to variable clinical symptoms, which makes diagnosis and activity evaluation difficult.

Methods: The original dataset of GSE88884 was analyzed to screen differentially expressed genes (DEGs) of SLE and the correlation between DEGs and clinical parameters (SLEDAI, anti-dsDNA, C3, and C4). The result was validated by microarray GSE121239 and SLE patients with RT-qPCR. Next, receiver operator characteristic (ROC) analysis, correlation analysis, and ordinal logistic regression were applied, respectively, to evaluate the capability of diagnosis and prediction of the candidate biomarker. Subsequently, the biological functions of the candidate biomarker were investigated through KEGG and GO enrichment, protein-protein interaction network, and the correlation matrix.

Results: A total of 283 DEGs were screened, and seven of them were overlapped with SLE-related genes. DDX60 was identified as the candidate biomarker. Analyses of GSE88884, GSE121239, and SLE patients with RT-qPCR indicated that DDX60 expression level is significantly higher in patients with high disease activity. ROC analysis and the area under the ROC curve (AUC = 0.8818) suggested that DDX60 has good diagnostic performance. DDX60 expression level was positively correlated with SLEDAI scores (r = 0.24). For every 1-unit increase in DDX60 expression value, the odds of a higher stage of activity of SLE disease are multiplied by 1.47. The function of DDX60 mainly focuses on IFN-I-induced antiviral activities, RIG-I signaling, and innate immune. Moreover, DDX60 plays a synergistic role with DDX58, IFIH1, OASL, IFIT1, and other related genes in the SLE pathogenesis. Conclusions. DDX60 is differently expressed in SLE, and it is significantly related to both serological indicators and the disease activity of SLE. We suggested that DDX60 might be a potential biomarker for SLE diagnosis and management.

The chemokine (C-X-C motif) ligand (CXCL) family in tumor tissue is closely related to tumor growth, metastasis, and survival. However, the differential expression profile and prognostic value of the CXCLs in ovarian cancer (OC) have not been elucidated. Therefore, we studied the expression levels and mutations of CXCLs in OC patient in TCGA and various public databases. The expression differences of CXCLs in OC cancer tissues and normal tissues were compared through the Gene Expression Profiling Interactive Analysis (GEPIA) database. The effect of CXCLs on OC prognosis was analyzed using the Kaplan-Meier curves in GEPIA database. The impact of CXCLs on immune infiltration and clinicopathological outcomes in OC was assessed using the TIMER algorithm. Compared with normal tissues, we found that eight CXCLs were significantly differentially expressed in OC. The expression levels of CXCL9 (P = 0.0201), CXCL11 (P = 0.0385), and CXCL13 (P = 0.0288) were significantly associated with tumor stage. CXCL13 was the only gene that significantly affected both disease-free survival (DFS) and overall survival (OS) in OC, and higher CXCL13 transcript levels implied longer DFS and OS. Although there was no significant impact on DFS, CXCL10 (P = 0.0079) and CXCL11 (P = 0.0011) expression levels had a significant effect on OS in OC. At the same time, CXCLs were significantly associated with several immune-infiltrating cells in OC tissues. The CXCLs were significantly associated with one or more immune-infiltrating cells in OC tissue. CXCL13 was differentially expressed in OC and significantly affected the prognosis of patients and was a potential marker of OC prognosis.

Objective: Numerus studies present that remnant cholesterol (RC) as a risk factor participates in the progression of multiple diseases. The aim of this study was to assess the relationship between cholesterol and periodontitis in the US population to find a reliable lipid predictor for periodontitis.

Materials and methods: Clinical data was retrieved from the National Health and Nutrition Examination Survey (NHANES) database between 2009 and 2014. The logistic regression was conducted to examine the corelationship between RC and various clinical features. Meanwhile, the dose-response relationship was measured through restricted cubic spline analysis. And the propensity score matching (PSM) was established to further investigate the potential relationship between RC and periodontitis.

Results: A number of 4,829 eligible participants were included in this study. It was found that the increased RC is associated with the higher risk of periodontitis after adjusting the potential confounding factors with the adjusted odds ratios (aOR) of 1.403 (95% confidence intervals (CI): 1.171-1.681, P < 0.001, univariate analysis) and 1.341 (95% CI: 1.105-1.629, P = 0.003, multivariate analysis) in the highest grade. There were significant differences in the relationship between RC and various clinical features including age, gender, body mass index (BMI), race, hypertension, and diabetes mellitus (all P < 0.001). Besides, the calculated thresholds for predicting periodontitis were 19.99 (before propensity score matching (PSM)) and 20.91 (after PSM) mg/dL.

Conclusions: In this study, RC was identified to be positively associated with the occurrence of periodontitis, which suggests that RC can be considered as a predictor for periodontitis.

[This retracts the article DOI: 10.1155/2022/4782361.].

[This retracts the article DOI: 10.1155/2022/3682741.].

[This retracts the article DOI: 10.1155/2022/4709019.].