A bioinformatics study of autophagy biomarkers associated with the prognosis of endometrial carcinoma

Abstract

Background: Autophagy plays a critical role in endometrial carcinoma (EC), but prognosis studies of differentially expressed autophagy-related genes (DEARGs) in EC are lacking. This study aimed to access the prognostic value of autophagy-related genes (ARGs) in EC and to identify the potential characteristics of ARGs in predicting patient survival and guiding treatment. Methods:The RNA-Seq data and clinical data were obtained from TCGA databases. The DEARGs were identified by "limma" package in R software. Clusterprofler was used for functional analysis.Using STRING databases to construct a protein-protein interaction (PPI) network and plug-in MCODE to screen hub modules in Cytoscape. The degrees method of cytoHubba was used to select important hub genes. Co-expression analysis was performed using "limma" package and Metascape for functional analysis. Univariate and multivariate Cox proportional hazards regression analyses were performed to construct a prognostic signature. Kaplan-Meier curve analysis, ROC curve analysis and Gene set enrichment analysis (GSEA) were also performed. Validation was executed by UALCAN, CCLE and HPA databases. Results:In total, 45 DEARGs were identified. Functional analysis showed that DEARGs were strikingly enriched in autophagy pathway. PPI network showed that CASP3 was the most central protein. CASP3 co-expression analysis revealed that co-expressed genes were mostly enriched in cell cycle. We identified a novel prognostic signature consisting of 3 genes (CDKN2A, PTK6 and GRID2). The risk score based on the prognostic signature was able to classify patients into high-risk and low-risk groups with significantly different overall survival. Furthermore, the prognostic signature is an independent prognostic predictor of survival and demonstrates superior prognostic performance compared with the clinicopathologic features for predicting 5-year survival. CDKN2A and PTK6 were further validated in UALCAN. GSEA suggested that the two genes played crucial roles in drug metabolism. Conclusion:Using bioinformatics analyses, we identified DEARGs and determined CDKN2A, PTK6 and DRID2 are tumor-associated genes and can be utilized as possible biomarkers in EC treatment.3 Background Endometrial cancer (EC) is the most common gynecologic malignancy and it accounts for 20%-30% of female reproductive system malignancies. Over 300,000 new cases are diagnosed annually, accounting for approximately 8.2% of women's cancer incidence worldwide [1]. An investigation from the American Cancer Society (ACS) reveals that the death rate for endometrial cancer has doubled during the past 20 years [2]. Currently, there are still no validated biological markers to screen EC, early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease [1,[3][4][5]. Therefore, it is urgent to develop prognostic or predictive biomarkers for risk assessment to identify high-risk or low-risk patients and to develop appropriate treatment options for them.Autophag...