Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, P.R. China.
Beijing Neurosurgical Institute, Beijing Tiantan Hospital Affiliated to Capital Medical University, Beijing Institute for Brain Disorders Brain Tumor Center, China National Clinical Research Center for Neurological Diseases, Key Laboratory of Central Nervous System Injury Research, Beijing 100070, P.R. China.
Int J Oncol. 2020 Sep;57(3):804-812. doi: 10.3892/ijo.2020.5087. Epub 2020 Jun 23.
Clinically non‑functioning pituitary adenoma (NFPA) represents approximately one third of all pituitary adenomas. Tumor regrowth is an important feature of NFPA; however, the effective methods with which to predict this are limited. The present study analyzed the expression of protein‑coding genes and long non‑coding RNA in 66 patients with NFPA. Cox regression analysis was performed to identify genes associated with regrowth or progression‑free survival (PFS). Kaplan‑Meier, random survival forest analysis and receiver operating characteristic curve (ROC) analyses were performed to generate a multi‑protein‑coding gene (PCG) and long non‑coding RNA (lncRNA) signature with a maximum area under the ROC curve (AUC). In total, 1 PCG (CHST12) and 2 lncRNAs (COA6‑AS1 and RP11‑23N2.4) were identified that were significantly associated with tumor regrowth. The multi‑transcriptome signature exhibited a high predictive accuracy for tumor regrowth, with an AUC of 0.869/0.726 in the training/testing set, and the discriminative power of this signature was better than that of age. On the whole, the present study indicates that the combined PCG and lncRNA signature may be beneficial as a marker for the prediction of the prognosis of patients with NFPA.
临床上无功能垂体腺瘤(NFPA)约占所有垂体腺瘤的三分之一。肿瘤复发是 NFPA 的一个重要特征;然而,预测这种情况的有效方法有限。本研究分析了 66 例 NFPA 患者的蛋白质编码基因和长非编码 RNA 的表达。采用 Cox 回归分析鉴定与复发或无进展生存(PFS)相关的基因。进行 Kaplan-Meier、随机生存森林分析和接受者操作特征曲线(ROC)分析,以生成具有最大 ROC 曲线下面积(AUC)的多蛋白编码基因(PCG)和长非编码 RNA(lncRNA)特征。总共鉴定出 1 个 PCG(CHST12)和 2 个 lncRNAs(COA6-AS1 和 RP11-23N2.4)与肿瘤复发显著相关。多转录组特征对肿瘤复发具有较高的预测准确性,在训练/测试集中的 AUC 为 0.869/0.726,该特征的判别能力优于年龄。总的来说,本研究表明,联合 PCG 和 lncRNA 特征可能有助于预测 NFPA 患者的预后。