Digital pathology and bioinformatics analysis of PIT1 expression in pituitary macroadenomas

Introduction: Pituitary macroadenomas pose a challenge in clinical endocrinology due to their impact on hormonal balance and subsequent clinical complications. Traditional diagnostic methods often suffer from subjectivity, highlighting the need for a more objective approach. Materials and Methods: This study was conducted as a retrospective secondary analysis of publicly available, de-identified data. Digital histopathological images were obtained from a digital pathology repository, while RNA-seq data, including PIT1 gene expression, were retrieved from the NCBI GEO database. Convolutional neural networks (CNN) were applied for tumor tissue segmentation and classification, while differential expression analysis was performed using DESeq2. Results: The model achieved an accuracy of 92.3% in identifying tumor regions, while bioinformatics analysis revealed a significant upregulation of PIT1 expression in adenomas with more pronounced clinical symptoms (log2FC = 1.8, p < 0.01). Integrated analysis confirmed a strong correlation between morphological patterns and PIT1 expression levels, while regression analysis indicated that this gene is an independent predictor of clinical outcomes. Discussion and Conclusion: The integration of digital pathology and bioinformatics analysis has shown promise in improving the diagnosis and classification of pituitary macroadenomas, paving the way for personalized therapy. Further studies on more heterogeneous samples could further validate the utility of this multidisciplinary approach.