Association Between p-STAT3/STAT3 Expression and Knosp Grading in Growth Hormone Pituitary Tumors.
Where this comes from
- Record sourced from PubMed, PMID 40293240.
- Also identified by DOI 10.1227/neu.0000000000003470 and PMC identifier 12507324.
- Licence recorded as CC BY-NC-ND.
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Abstract
Growth hormone-secreting pituitary adenomas (GH-PAs) that invade the structures surrounding the pituitary gland are a big problem for clinical professionals working in the field. People have tried to find a reliable biomarker for this. The STAT3 and phosphorylated STAT3 (p-STAT3) proteins are increased in many cancers and help cells move and invade. This study looked at the link between p-STAT3/STAT3 levels and the Knosp grading system for GH-PAs. A total of 71 patient GH-PAs were obtained between 22 January 2016 and 23 January 2022. An immunohistochemical analysis was conducted to assess the expression of p-STAT3/STAT3 and keratin CAM5.2 in tumor samples. p-STAT3/STAT3 was also used to evaluate the predictive value of tumor invasion propensity and patient prognosis. Tumor invasion, maximum diameter, macroadenoma, high T2-weighted intensity signal, cystic degeneration, low remission, and resection rates were found to be associated with high p-STAT3 expression. A large adenoma, a high T2-weighted intensity signal, cystic change, high insulin-like growth factor 1 levels, sparsely granular adenomas, high p-STAT3 expression, and a low resection rate of postoperative tumor were all associated with increased tumor aggressiveness. However, there was no link between Ki-67 and STAT3 expression and tumor invasion. Using MRI to diagnose the invasiveness of GH-PAs, it was found that p-STAT3 was more effective than sparsely granulated. The findings of this study indicate that the elevated expression of p-STAT3 may serve as a potential biomarker for the prediction of the invasiveness and clinical outcome of GH-PAs.
Medical subject headings
- STAT3 Transcription Factor
- Growth Hormone-Secreting Pituitary Adenoma
- Adenoma
- Pituitary Neoplasms