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

摘要

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 diagnosisof ACC,and provided the theoretical support of bioinformatics for exploring the occurrence,development of molecular mechanism and potential target of treatment of ACC.