Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model

• Published in: Journal of translational medicine Vol. 17; no. 1; p. 164
• Main Authors: Cheng, Sen, Wu, Jiaqi, Li, Chuzhong, Li, Yangfang, Liu, Chunhui, Li, Guilin, Li, Wuju, Hu, Shuofeng, Ying, Xiaomin, Zhang, Yazhuo
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.05.2019; BioMed Central; BMC
• Subjects: Adenoma; Adenoma - pathology; Adult; Algorithms; Bone morphogenetic proteins; Cancer; Cell cycle; Clinically non-functioning pituitary adenoma; Cyclin-dependent kinase; Cyclin-dependent kinases; Diagnosis; Discriminant Analysis; DNA microarrays; Enzyme inhibitors; Female; Health aspects; Hormones; Humans; Kaplan-Meier Estimate; Male; Mathematical models; Medical prognosis; Middle Aged; Models, Statistical; Neoplasm Proteins - metabolism; NMR; Nuclear magnetic resonance; Pituitary; Pituitary Neoplasms - pathology; Pituitary tumors; Predicting model; Prediction models; Prognosis; Protein expression; Proteins; Radiation therapy; Regrowth; Signatures; Statistical models; Surgery; Tissue analysis; Transforming growth factors; Tumor markers; Tumors; Wnt protein; China; Predicting model; Clinically non-functioning pituitary adenoma; Regrowth
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-019-1915-2

Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We aim to investigate whether the expression of selected tumour-related proteins and clinical features could be used as tumour markers to effectively predict the regrowth of NFPA. Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set. Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-β), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively. The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions.