Identification of biomarkers associated with integrated stress response in pituitary adenomas based on bioinformatics

Background: Integrated stress response (ISR) participates in the development of tumor, however, the roles of ISR-related genes (ISR-RGs) in pituitary adenoma (PA) are unclear. The goal of this study was to identify the biomarkers related to ISR-RGs in PA and explore their action mechanism. Methods: In the present study, the public datasets from Gene Expression Omnibus database (GEO) database and ISR-RGs from literature were analyzed to obtain biomarkers for PA using differential expression analysis, machine learning algorithm, Boruta analysis and expression validation. Results: The results demonstrated that BCL2, ERN1, PMAIP1 and TWIST1, which all possessed significantly lower expression in PA samples in training and testing sets, were selected as biomarkers for PA. Subsequently, the nomogram showed good performance in predicting PA risk based on these biomarkers. BCL2 and PMAIP1 were both validated to locate in Chromosome 18, and BCL2 mainly functioned in cytoplasm and PMAIP1 worked in extracellular region. Additionally, the regulatory networks revealed that biomarkers might work through interacting with BBC3, AC004687, hsa-miR-142-5p, TP53 and STAT3, which provided significant insights into mechanism of biomarkers. Strong correlations were found to exist between activated dendritic cells and TWIST1, as well as between PMAIP1 and T follicular helper cells, which contributed to a more comprehensive understanding of the mechanism. Finally, we totally obtained 36 potential drugs interacting with biomarkers, including sodium arsenite, cyclosporine and valproic acid. Conclusions: This study identified BCL2, ERN1, PMAIP1 and TWIST1 as biomarkers for PA and revealed their action mechanism, which provided important references for treatment and study of PA.