Screening of biomarkers for the diagnosis and prognosis of adrenocortical carcinoma based on bioinformatics analysis

Objective:To explore the gene biomarkers related to the diagnosis and prognosis of adrenocortical carcinoma(ACC)by bioinformatics.Methods:GEPIA online analysis tool was used to screen differentially expressed genes for sequencing data from patients with ACC and normal adrenal cortex.The overall survival rate and disease-free survival method were used to conduct batch survival analysis on differentially expressed genes,and the top 100 genes with HR values calculated by the two methods were obtained respectively.The intersection method is used to obtain core genes that play a key role in both overall survival and disease-free survival.GEPIA online analysis tool was used again to explore the relationship between the above-mentioned survival-related genes and the pathological stage of ACC.Use UALCAN online analysis tool to verify the survival-related genes again and draw the Kaplan-Meier survival curve.Finally,GSE33371 chip dataset of the GEO database was used to evaluate the diagnostic value of the above-mentioned survival-related genes.Results:514 differentially expressed genes were obtained by limma method.Batch analysis of differential genes was performed to obtain the top 100 genes most related to overall survival and disease-free survival,of which 13 genes were closely related to overall survival and disease-free survival.9 hub genes including TP73,SNHG1,PDE6D,GPC2,SUV39H2,HELLS,CLK2,COPS7B and CEP164 were finally obtained by exploring the relationship between their expression levels and pathological stage and resurvival analysis.At the same time,the results of ROC analysis suggest that the above hub genes have high diagnostic value for patients with adrenocortical carcinoma.Conclusion:By using GEPIA,UALCAN and the gene chip retrieved from GEO database,combined with the bioinformatics method,we analyzed and verified the new biomarkers that can be used to evaluate the prognosis of patients with ACC and to differential diagnosis of ACC,and provided the theoretical support of bioinformatics for exploring the occurrence,development of molecular mechanism and potential target of treatment of ACC.

A nomogram of 5-year risk of type 2 diabetes in Chinese population

Objective:The aim of this study was to analyze the risk factors of type 2 diabetes in 5 years in Chinese population,and to construct the prediction model of nomogram and verify its validity.Methods:The physical examination and follow-up data of the participants who received physical examination at 32 sites in 11 cities in China from 2010 to 2016 were collected from the Dryad digital repository database.Randomly divided into modeling group(n=22936)and validation group(n=9830).In the modeling group,the independent risk factors were determined by single factor and multi factor analysis based on Cox regression model,and the nomogram prediction model was constructed by R software.The accuracy and performance of the model were evaluated by AUC value,C-index and calibration curve.Results:The multivariate regression model suggested that fasting blood glucose,triglyceride,smoking history and drinking history were independent risk predictors of 5-year risk of type 2 diabetes in Chinese population.In the modeling group,AUC was 0.776(95%CI:0.699-0.849),and C-index was 0.783(95%CI:0.706-0.856).Similarly,in the validation group,the AUC value was 0.743(95%CI:0.665-0.824),and the C-index was 0.764(95%CI:0.667-0.846),suggesting that the model had a good discrimination ability.The 5-year adjusted risk curve of type 2 diabetes in Chinese population suggests a good consistency between the predicted value and the actual value.Conclusion:The nomogram model can predict the 5-year risk of type 2 diabetes in Chinese population intuitively and accurately.