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Objective: To clarify the relationship between proteomic expression and clinical feature of pituitary adenoma. Methods: We have previously developed non-target proteomics analysis, which enables to detect and quantify approximately 7,000 to 9,000 kinds of protein weave, in parallel with RNA-seq analysis, and then subjected to 14 cases of pituitary adenoma surgically removed at Chiba University Hospital. Bioinformatic evaluation including DEGs, heatmap and PCA analyses was performed to reveal underlying their molecular pathogenesis. Results: We successfully identified 789 differentially expressed proteins and 593 DEGs in non-target proteomics and RNA-seq, respectively. Intriguingly, PCA analysis demonstrated that tumors were clearly divided into 3 groups based on protein expression profile; functional pituitary adenomas consisting of two subtypes depending on Pit1 and T-pit linage, and non-functional tumors consisting of two distinct subtypes, with properties close to functional tumors and unique characteristics of hard tumor difficult to remove by endoscopic surgery. To address the underlying molecular biological functions in each group clustering analysis and heat-map were performed and we found that 3 groups were separated clearly with their own both gene and protein expression profile. Indeed, for instance, GO term of plasma membrane part was significantly enriched in hard tumor group, pathways of GH receptor signaling, GH hormone synthesis as in GH-positive group. Conclusions: We herein demonstrate that pituitary adenoma can be uniquely separated into certain categories through our novel non-target proteomics with coupling to RNA-seq, particularly providing novel group of hard tumor characteristics with enriched expression of both protein and mRNA in plasma membrane part. Thus our method would be beneficial and useful to elucidate underlying molecular pathogenesis for pituitary tumors, while further analysis is required.

SAT-309 Integrated Analysis of Pituitary Adenoma Using Novel Approach of Non-Target Proteomics Along with RNA-Sequencing Analyses