Department of Urology, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China; Department of Urology, Changzheng Hospital, Second Military Medical University, Shanghai, China.
RNA Information Center, Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou, China.
Eur Urol. 2018 Dec;74(6):756-763. doi: 10.1016/j.eururo.2018.07.032. Epub 2018 Aug 22.
Long non-coding RNAs (lncRNAs) can be used as prognostic biomarkers in many types of cancer.
We sought to establish an lncRNA signature to improve postoperative risk stratification for patients with localized clear cell renal cell carcinoma (ccRCC).
DESIGN, SETTING, AND PARTICIPANTS: Based on the RNA-seq data of 444 stage I-III ccRCC tumours from The Cancer Genome Atlas project, we built a four-lncRNA-based classifier using the least absolute shrinkage and selection operation (LASSO) Cox regression model in 222 randomly selected samples (training set) and validated the classifier in the remaining 222 samples (internal validation set). We confirmed this classifier in an external validation set of 88 patients with stage I-III ccRCC from a Japan cohort and using quantitative reverse transcription polymerase chain reaction (RT-PCR) in another three independent sets that included 1869 patients from China with stage I-III ccRCC.
Univariable and multivariable Cox regression, Harrell's concordance index (c-index), and time-dependent receiver operating characteristic curves were used to evaluate the association of the classifier with overall survival, disease-specific survival, and disease-free survival.
Using the LASSO Cox regression model, we built a classifier named RCClnc4 based on four lncRNAs: ENSG00000255774, ENSG00000248323, ENSG00000260911, and ENSG00000231666. In the RNA-seq and RT-PCR data sets, the RCClnc4 signature significantly stratified patients into high-risk versus low-risk groups in terms of clinical outcome across and within subpopulations and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.34 [95% confidence interval {CI}: 1.03-1.75; p=0.028] to 1.89 [95% CI, 1.55-2.31; p<0.001]) after adjusting for clinical and pathologic factors. The RCClnc4 signature achieved a higher accuracy (mean c-index, 0.72) than clinical staging systems such as TNM (mean c-index, 0.62) and the stage, size, grade, and necrosis (SSIGN) score (mean c-index, 0.64), currently reported prognostic signatures and biomarkers for the estimation of survival. When integrated with clinical characteristics, the composite clinical and lncRNA signature showed improved prognostic accuracy in all data sets (TNM + RCClnc4 mean c-index, 0.75; SSIGN + RCClnc4 score mean c-index, 0.75). The RCClnc4 classifier was able to identify a clinically significant number of both high-risk stage I and low-risk stage II-III patients.
The RCClnc4 classifier is a promising and potential prognostic tool in predicting the survival of patients with stage I-III ccRCC. Combining the lncRNA classifier with clinical and pathological parameters allows for accurate risk assessment in guiding clinical management.
The RCClnc4 classifier could facilitate patient management and treatment decisions.
长链非编码 RNA（lncRNA）可作为许多类型癌症的预后生物标志物。
我们旨在建立 lncRNA 特征，以改善局限性透明细胞肾细胞癌（ccRCC）患者的术后风险分层。
设计、地点和参与者：基于来自癌症基因组图谱项目的 444 例 I-III 期 ccRCC 肿瘤的 RNA-seq 数据，我们使用最小绝对收缩和选择操作（LASSO）Cox 回归模型在 222 个随机选择的样本（训练集）中构建了一个基于四个 lncRNA 的分类器，并在其余 222 个样本（内部验证集）中验证了该分类器。我们在来自日本队列的 88 例 I-III 期 ccRCC 患者的外部验证集中证实了这一分类器，并在另外三个独立的包含来自中国的 1869 例 I-III 期 ccRCC 患者的数据集（包括 1869 例患者）中使用定量逆转录聚合酶链反应（RT-PCR）进行了验证。
单变量和多变量 Cox 回归、Harrell 一致性指数（c-index）和时间依赖性接收者操作特征曲线用于评估分类器与总生存、疾病特异性生存和无病生存的相关性。
使用 LASSO Cox 回归模型，我们构建了一个名为 RCClnc4 的分类器，该分类器基于四个 lncRNA：ENSG00000255774、ENSG00000248323、ENSG00000260911 和 ENSG00000231666。在 RNA-seq 和 RT-PCR 数据集中，RCClnc4 特征在跨亚群和在亚群内显著将患者分层为高风险与低风险组，并在多变量分析中保持为独立的预后因素（风险比范围为 1.34 [95%置信区间 {CI}：1.03-1.75；p=0.028]至 1.89 [95% CI，1.55-2.31；p<0.001]），调整了临床和病理因素后。RCClnc4 特征的准确性（平均 c-index，0.72）高于临床分期系统，如 TNM（平均 c-index，0.62）和分期、大小、分级和坏死（SSIGN）评分（平均 c-index，0.64），目前报道的用于估计生存的预后标志物和生物标志物。当与临床特征相结合时，复合临床和 lncRNA 特征在所有数据集中均提高了预后准确性（TNM+RCClnc4 平均 c-index，0.75；SSIGN+RCClnc4 评分平均 c-index，0.75）。RCClnc4 分类器能够识别出具有临床意义的高风险 I 期和低风险 II-III 期患者。
RCClnc4 分类器是预测 I-III 期 ccRCC 患者生存的一种很有前途和有潜力的预后工具。将 lncRNA 分类器与临床和病理参数相结合，可在指导临床管理中进行准确的风险评估。
RCClnc4 分类器有助于患者管理和治疗决策。
