lncRNA RGMB-AS1在脑胶质瘤预后评估中的价值

目的 探讨长链非编码RNA（lncRNA）RGMB-AS1在脑胶质瘤病人预后评估中的作用。方法 选取2014年9月~2017年6月经术后病理检查证实的脑胶质瘤140例（低级别64例，高级别76例），另选取颅脑损伤内减压术中切取正常脑组织25例为对照。实时荧光定量PCR法检测lncRNA RGMB-AS1的表达水平，以RGMB-AS1表达量的中位数为截断值，分为高表达组和低表达组。用Kaplan-Meier法比较总体生存期（OS）和无进展生存期（PFS），用Cox比例回归风险模型分析影响胶质瘤病人预后的因素。结果 140例胶质瘤中，lncRNA RGMB-AS1高表达70例，低表达70例。胶质瘤组织lncRNA RGMB-AS1的相对表达量明显高于对照组（P<0.05）。多因素Cox比例回归风险模型分析结果显示年龄≥50岁、术前KPS评分<80分、WHO分级Ⅲ~Ⅳ级、lncRNA RGMB-AS1高表达是胶质瘤病人PFS和OS的独立影响因素（P<0.05）。lncRNA RGMB-AS1高表达胶质瘤病人PFS和OS较低表达病人均明显缩短（P<0.05）。无论是高级别胶质瘤，还是低级别胶质瘤，lncRNA RGMB-AS1高表达病人PFS和OS较低表达病人均明显缩短（P<0.05）。结论 lncRNA RGMB-AS1表达水平与脑胶质瘤病例级别呈正相关，lncRNA RGMB-AS1高表达脑胶质瘤病人生存期较低表达病人明显缩短。这提示lncRNA RGMB-AS1表达水平可作为脑胶质瘤病人预后评估指标。     

Hierarchical non‐negative matrix factorization to characterize brain tumor heterogeneity using multi‐parametric MRI

Tissue characterization in brain tumors and, in particular, in high‐grade gliomas is challenging as a result of the co‐existence of several intra‐tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow‐up of individual patients. Twenty‐four patients with glioma [10 low‐grade gliomas (LGGs), 14 high‐grade gliomas (HGGs)] underwent a multi‐parametric MRI (MP‐MRI) scheme, including conventional MRI (cMRI), perfusion‐weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short‐TE ¹H MRSI. MP‐MRI parameters were derived: T₂, T₁ + contrast, fluid‐attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N‐acetyl‐aspartate (NAA), total choline (Cho), etc. Hierarchical non‐negative matrix factorization (hNMF) was applied to the MP‐MRI parameters, providing tissue characterization on a patient‐by‐patient and voxel‐by‐voxel basis. Tissue‐specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was also applied to reduced MRI datasets, showing the added value of individual MRI modalities. Copyright © 2015 John Wiley & Sons, Ltd.