Molecular Basis of Aggressiveness in Pituitary Adenomas and Its Association With the Immune Microenvironment

Background: Pituitary adenomas (PAs) are common intracranial tumors, and their aggressive phenotype exhibits a poor prognosis. We aimed to explore the aggressive feature of PAs and discover novel diagnostic markers. Method: The datasets of GSE260487 and GSE169498, which contained invasive and noninvasive samples, were downloaded from the Gene Expression Omnibus (GEO) database. Aggressive phenotype-related gene modules were classified using the "WGCNA" package. Differentially expressed genes (DEGs) in each module were identified by the "limma" package. Next, a protein-protein interaction (PPI) network was used in the construction and identification process of key genes, and the CytoHubba tool was utilized to analyze the subnetwork and select the top 10 genes. Diagnostic markers were selected using two machine learning algorithms: support vector machine (SVM) and Lasso. Finally, the ESTIMATE and "GSVA" were applied for immune infiltration assessment. Results: WGCNA showed that the turquoise module was closely associated with the aggressive phenotype and enriched in neural differentiation and cell migration pathways. A total of 521 DEGs were intersected with the turquoise module genes to obtain 187 overlapping genes, from which 10 hub genes related to tumor proliferation were selected to develop a PPI network. Next, we determined MYH7 as an accurate diagnostic marker, and the immune infiltration analysis revealed that MYH7 expression was negatively correlated with stromal score and immune score but positively correlated with the infiltration of antitumor cells. Conclusion: We developed a novel marker with a strong diagnostic performance for PAs, providing novel insights for the detection and individualized treatment of PAs.