Expression of STAT3 and hypoxia markers in long-term surviving malignant glioma patients.

Background: Glioblastoma is a malignant and aggressive type of central nevous system malignancy characterized by many distinct biological features including extensive hypoxia. Hypoxia in glioblatoma associates with complex signaling patterns including activation of several pathways such as MAPK, PI3K-AKT/mTOR and IL-6/JAK/STAT3 with the master regulator HIF-1, which in turn drive particular tumor behaviors determining, in the end, treatment outcomes and patients fate. Thus, the present study was designed to investigate the expression of selected hypoxia related factors including STAT3 in a small set of long-term surviving glioma patients.
Methods: The expression of selected hypoxia related factors including STAT3 was evaluated in a time series of formalin fixed paraffin embedded and cryopreserved glioma samples from repeatedly resected patients. In addition, comparative studies were also conducted on primary glioma cells derived from original patient samples, stabilized glioma cell lines and tumor-xenograft mice model. Obtained data were correlated with clinical findings too.
Results: Glioblastoma samples of the analyzed patients displayed heterogeneity in the expression of hypoxia- related and EMT markers with most interesting trend being observed in pSTAT3. This heterogeneity was subsequently confirmed in other employed models (primocultures derived from glioblastoma tissue resections, cryopreserved tumor specimens, stabilized glioblastoma cell line in vitro and in vivo) and concerned, in particular, STAT3 expression which remained stable. In addition, subsequent studies on the role of STAT3 in the context of glioblastoma hypoxia demonstrated opposing effects of its deletion on cell viability as well as the expression of hypoxia and EMT markers.
Conclusions: Our results suport the importance of STAT3 expression and activity in the context of hypoxia in malignant glioblastoma long-term surviving glioma patients while emphasizing heterogeneity of biological outcomes in varying employed tumor models.
(© 2024. The Author(s).)