Study on autophagy-related biomarkers in pancreatic cancer based on bioinformatics

Abstract

Pancreatic cancer (PC) is a common malignant tumor whose incidence is increasing yearly and the 5-year survival rate is only 4\%. Early diagnosis is crucial for a better prognosis of PC, but additional research is needed to enhance existing technology. Current research clearly demonstrates a significant correlation between autophagy and PC pathology, so the related biomarkers possess immense potential for the early diagnosis of PC pathology. This study aimed to explore autophagy-related biomarkers in PC by detecting differentially expressed autophagy genes (DEAGs), providing new insights into the pathological progression of PC. GSE15471 and GSE16515 gene expression profiles of PC were downloaded from the Gene Expression Omnibus (GEO) database. R language was used to standardize and differentially expressed genes (DEGs) of PC expression profiles. A total of 324 autophagy genes were obtained from HADb, ATdb, and ATD autophagy gene databases. The differential genes of PC and autophagy genes were integrated and analyzed, and 11 DEAGs were selected. Then the potential biological functions of DEAGs were predicted. The correlation analysis reveals the potential correlation among 11 DEAGs. Finally, a total of 10 significant DEAGs (APOL1, CASP1, CEP55, CXCR4 ITGA3, ITGB4, NPC1, SLC6A14, SMS, SMURF1) were obtained by difference examination and receiver operating characteristic (ROC) curve, the area under curve (AUC) was 0.811, 0.675, 0.797, 0.807, 0.730, 0.795, 0.783, 0.827, 0.784, and 0.802, respectively. These results indicate that these genes may have application value in the study of PC, and could potentially be utilized as new biomarkers for the early diagnosis of PC. This study revealed a strong connection between DEAGs and PC, and the identified DEAGs could serve as a potential biomarker for PC, which offers a novel approach to early detection of the disease.