Some cells within a diffuse large B-cell lymphoma (DLBCL) have the genotype of a stem cell, the proportion of which is termed degree of stemness. We interrogated correlations between the degree of stemness with immune and stromal cell scores and clinical outcomes in persons with DLBCL. We evaluated gene expression data on 1,398 subjects from Gene Expression Omnibus to calculate the degree of stemness. Subjects were classified into low- and high-stemness cohorts based on restricted cubic spline plots. Weighted gene co-expression network analysis (WGCNA) was used to screen for stemness-related genes. Immune and stromal scores correlated with the degree of stemness (both P < 0.001). A high degree of stemness correlated with a shorter progression-free survival (PFS; Hazard Ratio [HR; 95% Confidence Interval [CI] =1.90 (1.37, 2.64; P < 0.001) and a shorter survival (HR = 2.29 (1.53, 3.44; P < 0.001). CDC7 expression correlated with the degree of stemness, and CDC7-inhibitors significantly increased apoptosis (P < 0.01), the proportion of cells in G1 phase (P < 0.01), and inhibited lymphoma growth in a mice xenograft model (P = 0.04). Our data indicate correlations between the degree of stemness, immune and stromal scores, PFS, and survival. These data will improve the prediction of therapy outcomes in DLBCL and suggest potential new therapies.
Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male's risk prediction model logistic regression equation: logit(P)=BMI × 0.735+ vegetables × (-0.671) + age × 0.838+ diastolic pressure × 0.296+ physical activity× (-2.287) + sleep ×(-0.009) +smoking ×0.214; Female's risk prediction model logistic regression equation: logit(P)=BMI ×1.979+ vegetables× (-0.292) + age × 1.355+ diastolic pressure× 0.522+ physical activity × (-2.287) + sleep × (-0.010).The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.
Objective: To investigate the FPG associated with HbA1c diagnosis of T2DM, to clear the best point of tangency HbA1c diagnosis of T2DM. Methods: Using simple random sampling method, from this group of T2DM field epidemiological investigation data in the database we randomly selected 300 cases of T2DM patients, 300 cases of healthy people. The sensitivity and specificity of FPG, HbA1c and HbA1c/FPG combination were calculated by diagnostic screening test; Using ROC curve evaluation FPG, HbA1c and HbA1c in combination with FPG diagnosis value of T2DM. Results: The cutoff point of FPG for diagnosing T2DM was 6.19 mmol/L, the sensitivity and specificity were respectively: 83.00%, 93.67%, area under the curve (AUC) was 0.928; the cutoff point of HbA1c for diagnosing T2DM was 6.40%, the sensitivity and specificity were respectively: 87.00%, 90.33%, area under the curve (AUC) is 0.935;Combined use of HbA1c and FPG in the diagnosis of T2DM, simultaneous detection of HbA1c and FPG, when HbA1c≥ 6.40% or FPG≥6.19 mmol/L diagnosed with T2DM, the sensitivity and specificity were respectively: 97.79%, 84.61%, area under the curve (AUC) is 0.960. Conclusions: The cutoff point of HbA1c for diagnosing T2DM was 6.40% , Combined HbA1c and FPG in the T2DM diagnosis, which can greatly improve the sensitivity, the combined application is more conducive to the early screening of T2DM.
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