Hormonal signaling in glioblastoma: emerging roles in stemness and tumor plasticity.

Abstract

Glioblastoma (GBM) is characterized by pronounced cell-state plasticity and functional heterogeneity, which drive therapeutic resistance, tumor recurrence, and disease evolution. Although these processes have traditionally been viewed through a predominantly genomic perspective, emerging evidence identifies endocrine signaling as a critical regulator of glioblastoma cell states. In this review, we propose that hormones function not as independent signaling pathways or merely as promoters of tumor growth, but as systemic regulators that integrate physiological and microenvironmental signals to modulate transitions among stem-like, proliferative, invasive, and treatment-resistant cellular states. We analyzed the current evidence on the roles of sex steroids, glucocorticoids, the IGF/insulin axis, melatonin, thyroid hormones, and prolactin, highlighting how these endocrine signals converge on common molecular nodes, including PI3K/AKT/mTOR, MAPK/ERK, JAK/STAT3, and epigenetic regulators such as EZH2, to coordinate stemness-associated programs, adaptive cell-state responses, and therapeutic adaptation. We also discussed the dual nature of various hormonal axes, including differential activity of estrogen receptor isoforms, the role of the androgen receptor in maintaining self-renewal, the impact of glucocorticoids on adaptive cellular reprogramming, and the ability of melatonin to destabilize stem-like cellular states. Finally, we discuss how sex-based differences in GBM incidence and outcome -driven by both biological and non-biological factors- influence diagnosis, access to treatment, and current interpretations of the global epidemiology of glioblastoma. Collectively, we propose a conceptual framework that redefines GBM as an endocrine-modulated disease, in which hormonal signaling may modulate tumor cell state adaptation. This perspective provides a basis for developing therapeutic strategies that target shared molecular convergence nodes while integrating the biological complexity of GBM with its broader clinical and socioeconomic context.