Abstract

Introduction

Growth hormone (GH)-secreting pituitary tumors (GHomas) are the most common acromegaly cause. At diagnosis, most of them are macroadenomas, and up to 56% display cavernous sinus invasion. Biomarker assessment associated with tumor growth and invasion is important to optimize their management.

Objectives

The study aims to identify clinical/hormonal/molecular biomarkers associated with tumor size and invasiveness in GHomas and to analyze the influence of pre-treatment with somatostatin analogs (SSAs) or dopamine agonists (DAs) in key molecular biomarker expression.

Methods

Clinical/analytical/radiological variables were evaluated in 192 patients from the REMAH study (ambispective multicenter post-surgery study of the Spanish Society of Endocrinology and Nutrition). The expression of somatostatin/ghrelin/dopamine system components and key pituitary/proliferation markers was evaluated in GHomas after the first surgery. Univariate/multivariate regression studies were performed to identify association between variables.

Results

Eighty percent of patients harbor macroadenomas (63.8% with extrasellar growth). Associations between larger and more invasive GHomas with younger age, visual abnormalities, higher IGF1 levels, extrasellar/suprasellar growth, and/or cavernous sinus invasion were found. Higher GH1 and lower PRL/POMC/CGA/AVPR1B/DRD2T/DRD2L expression levels (P < .05) were associated with tumor invasiveness. Least Absolute Shrinkage and Selection Operator’s penalized regression identified combinations of clinical and molecular features with areas under the curve between 0.67 and 0.82. Pre-operative therapy with DA or SSAs did not alter the expression of any of the markers analyzed except for DRD1/AVPR1B (up-regulated with DA) and FSHB/CRHR1 (down-regulated with SSAs).

Conclusions

A specific combination of clinical/analytical/molecular variables was found to be associated with tumor invasiveness and growth capacity in GHomas. Pre-treatment with first-line drugs for acromegaly did not significantly modify the expression of the most relevant biomarkers in our association model. These findings provide valuable insights for risk stratification and personalized management of GHomas.