机构:[1]Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, People’s Republic of China.研究所北京市神经外科研究所首都医科大学附属天坛医院[2]Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, No. 27, Taiping Road, Haidian District, Beijing 100850, People’s Republic of China.[3]Neurosurgery, Beijing Neurosurgical Institute, Beijing Tiantan Hospital Affiliated To Capital Medical University, Beijing Institute for Brain Disorders Brain Tumour Center, China National Clinical Research Center for Neurological Diseases, Key Laboratory of Central Nervous System Injury Research, Beijing 100070, People’s Republic of China.重点科室诊疗科室研究所神经病学中心神经病学中心北京市神经外科研究所首都医科大学附属天坛医院
BackgroundCompared 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.MethodTumour 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.ResultsThree 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-), 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.ConclusionThe prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions.
基金:
National High Technology Research and Development Program of China (863 Program)National High Technology Research and Development Program of China [2014AA020610]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [81771489]; Beijing Municipal Science & Technology CommissionBeijing Municipal Science & Technology Commission [Z171100000117002]; China National Key Research and Development Program [2017YFC0908300]
第一作者机构:[1]Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, People’s Republic of China.
共同第一作者:
通讯作者:
通讯机构:[2]Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, No. 27, Taiping Road, Haidian District, Beijing 100850, People’s Republic of China.[3]Neurosurgery, Beijing Neurosurgical Institute, Beijing Tiantan Hospital Affiliated To Capital Medical University, Beijing Institute for Brain Disorders Brain Tumour Center, China National Clinical Research Center for Neurological Diseases, Key Laboratory of Central Nervous System Injury Research, Beijing 100070, People’s Republic of China.
推荐引用方式(GB/T 7714):
Cheng Sen,Wu Jiaqi,Li Chuzhong,et al.Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model[J].JOURNAL OF TRANSLATIONAL MEDICINE.2019,17(1):-.doi:10.1186/s12967-019-1915-2.
APA:
Cheng, Sen,Wu, Jiaqi,Li, Chuzhong,Li, Yangfang,Liu, Chunhui...&Zhang, Yazhuo.(2019).Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model.JOURNAL OF TRANSLATIONAL MEDICINE,17,(1)
MLA:
Cheng, Sen,et al."Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model".JOURNAL OF TRANSLATIONAL MEDICINE 17..1(2019):-