Genome-wide screening and cohorts validation identifying novel lncRNAs as prognostic biomarkers for clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the main subtype of renal cell carcinoma with varied prognosis. We aimed to identify and assess the possible prognostic long noncoding RNA (lncRNA) biomarkers. LncRNAs expression data and corresponding clinical information of 619 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Differentially expressed genes analysis, univariate Cox regression, the least absolute shrinkage and selection operator Cox regression model were utilized to identify hub lncRNAs. Multivariate Cox regression was used to establish the risk model. Statistical analysis was performed using R 3.5.3. The expression value of five lncRNAs and the risk-score levels were significantly associated with a survival prognosis of ccRCC patients (all P < .001). In the TCGA validation cohort, the area under the curve (AUC) for the integrated nomogram was 0.905 and 0.91 for 3-, 5-year prediction separately. The AUC reached up to 0.757 in an independent ICGC cohort. Besides, the calibration plots also illustrated well curve-fitting between observation values and predictive values. Weighted gene co-expression network analysis and subsequent pathway analysis revealed that the PI3K-Akt-mTOR and hypoxia-inducible factor signaling crosstalk might function as the most essential mechanisms related to the five-lncRNAs signature. Our study suggested that lncRNA AC009654.1, AC092490.2, LINC00524, LINC01234, and LINC01885 were significantly associated with ccRCC prognosis. The prognostic model based on this five lncRNA may predict the overall survival of ccRCC.

Zhang C ，Huang D ，Liu A ，Xu Y ，Na R ，Xu D ... -  《-》

Comprehensive analysis of a novel lncRNA profile reveals potential prognostic biomarkers in clear cell renal cell carcinoma.

Liu T ，Sui J ，Zhang Y ，Zhang XM ，Wu WJ ，Yang S ，Xu SY ，Hong WW ，Peng H ，Yin LH ，Pu YP ，Liang GY ... -  《-》

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma.

The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC). A coexpression analysis of glycolysis-related mRNAs-long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan-Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model. We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan-Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways. The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.

Ma W ，Zhong M ，Liu X 《-》

Prognosis of clear cell renal cell carcinoma (ccRCC) based on a six-lncRNA-based risk score: an investigation based on RNA-sequencing data.

Zeng JH ，Lu W ，Liang L ，Chen G ，Lan HH ，Liang XY ，Zhu X ... -  《Journal of Translational Medicine》

Prognostic Value of a Long Non-coding RNA Signature in Localized Clear Cell Renal Cell Carcinoma.

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). 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.

Qu L ，Wang ZL ，Chen Q ，Li YM ，He HW ，Hsieh JJ ，Xue S ，Wu ZJ ，Liu B ，Tang H ，Xu XF ，Xu F ，Wang J ，Bao Y ，Wang AB ，Wang D ，Yi XM ，Zhou ZK ，Shi CJ ，Zhong K ，Sheng ZC ，Zhou YL ，Jiang J ，Chu XY ，He J ，Ge JP ，Zhang ZY ，Zhou WQ ，Chen C ，Yang JH ，Sun YH ，Wang LH ... -  《-》