Le Qu1, Ze-Lin Wang2, Qi Chen3, Yao-Ming Li4, Hao-Wei He5, James J Hsieh6, Song Xue5, Zhen-Jie Wu7, Bing Liu7, Hao Tang5, Xiao-Feng Xu5, Feng Xu5, Jie Wang7, Yi Bao7, An-Bang Wang7, Dong Wang5, Xiao-Ming Yi5, Zhong-Kui Zhou5, Chang-Jie Shi5, Ke Zhong5, Zheng-Cheng Sheng5, Yu-Lin Zhou5, Jun Jiang4, Xiao-Yuan Chu8, Jia He3, Jing-Ping Ge5, Zheng-Yu Zhang5, Wen-Quan Zhou9, Cheng Chen10, Jian-Hua Yang11, Ying-Hao Sun12, Lin-Hui Wang13. 1. 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. Electronic address: septsoul@hotmail.com. 2. RNA Information Center, Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou, China. 3. Department of Health Statistics, Second Military Medical University, Shanghai, China. 4. Department of Urology, Daping Hospital, Third Military Medical University, Chongqing, China. 5. Department of Urology, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China. 6. Molecular Oncology, Department of Medicine, Siteman Cancer Center, Washington University, St. Louis, MO, USA. 7. Department of Urology, Changzheng Hospital, Second Military Medical University, Shanghai, China. 8. Department of Medical Oncology, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China. 9. Department of Urology, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China. Electronic address: shzwqzsl@163.com. 10. Department of Medical Oncology, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China. Electronic address: chencheng1289@126.com. 11. RNA Information Center, Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou, China. Electronic address: yangjh7@mail.sysu.edu.cn. 12. Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai, China. Electronic address: sunyhsmmu@126.com. 13. Department of Urology, Changzheng Hospital, Second Military Medical University, Shanghai, China. Electronic address: wanglinhui@smmu.edu.cn.
Abstract
BACKGROUND: Long non-coding RNAs (lncRNAs) can be used as prognostic biomarkers in many types of cancer. OBJECTIVE: 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. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: 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. RESULTS AND LIMITATIONS: 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. CONCLUSIONS: 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. PATIENT SUMMARY: The RCClnc4 classifier could facilitate patient management and treatment decisions.
BACKGROUND: Long non-coding RNAs (lncRNAs) can be used as prognostic biomarkers in many types of cancer. OBJECTIVE: 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. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: 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. RESULTS AND LIMITATIONS: 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. CONCLUSIONS: 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. PATIENT SUMMARY: The RCClnc4 classifier could facilitate patient management and treatment decisions.
Authors: Miaoyan Wei; Jin Xu; Jie Hua; Qingcai Meng; Chen Liang; Jiang Liu; Bo Zhang; Wei Wang; Xianjun Yu; Si Shi Journal: Front Immunol Date: 2021-04-23 Impact factor: 7.561