An integrated mRNA‐lncRNA signature for relapse prediction in laryngeal cancer

Abstract

Patients with laryngeal cancer with early relapse usually have a poor prognosis. In this study, we aimed to identify a multi‐gene signature to improve the relapse prediction in laryngeal cancer. One microarray data set GSE27020 (training set, N = 109) and one RNA‐sequencing data set (validation set, N = 85) were included into the analysis. In the training set, the microarray expression profile was re‐annotated into an mRNA‐long noncoding RNA (lncRNA) biphasic profile. Then, LASSO Cox regression model identified nine relapse‐related RNA (eight mRNA and one lncRNA), and a risk score was calculated for each sample according to the model coefficients. Patients with high‐risk showed poorer relapse‐free survival than patients with low risk (hazard ratios (HR): 6.189, 95% confidence interval (CI): 3.075‐12.460, P < 0.0001). The risk score demonstrated good accuracy in predicting the relapse (area under time‐dependent receiver‐operating characteristic (AUC): 0.859 at 1 year, 0.822 at 3 years, and 0.815 at 5 years). The results were validated in the validation set (HR: 3.762, 95% CI: 1.594‐8.877, P = 0.011; AUC: 0.770 at 1 year, 0.769 at 3 years, and 0.728 at 5 years). The multivariate analysis reached consistent results after adjustment by multiple confounders. When compared with a 27‐gene signature, a 2‐lncRNA signature, and Tumor‐Node‐Metastasis stage, the risk score also showed better performance (P < 0.05). In conclusion, we successfully developed a robust mRNA‐lncRNA signature that can accurately predict the relapse in laryngeal cancer.

Identification of a Seven-lncRNA-mRNA Signature for Recurrence and Prognostic Prediction in Relapsed Acute Lymphoblastic Leukemia Based on WGCNA and LASSO Analyses

“…ROC curve analysis demonstrated that this prognostic signature exhibited good performance for predicting 1-, 3-, and 5-year OS of childhood ALL patients of both the training and two validation datasets, with the AUC ranging from 0.796 to 0.958 for 1-year OS, 0.775 to 0.879 for 3-year OS, and 0.700 to 0.846 for 5-year OS, respectively. In line with the study of Xiang et al [28], the prognostic accuracy of our integrated lncRNA-mRNA signature screened from the training dataset also seemed to be higher than that of the study performed by Chang et al (AUC: 0.846 vs. 0.7984, only including 15 apoptosis pathway genes) [29]. Likewise, the predictive power of this lncRNA-mRNA signature for recurrence outcomes was also higher than that of the mRNA signature identified by Cleaver et al [13] (five-mRNA classifier: accuracy: 93.3% vs. 82%, PPV: 94.7% vs. 81%, and sensitivity: 97.8% vs. 77%; 7-NF-κB pathway genes: accuracy: 93.3% vs. 76%, PPV: 94.7% vs. 71%, and sensitivity: 97.8% vs. 77%; 12-Wnt pathway genes: accuracy: 93.3% vs. 76%, PPV: 94.7% vs. 75%, NPV: 80.0% vs. 77%, and sensitivity: 97.8% vs. 68%; and 14-cell adhesion pathway genes: accuracy: 93.3% vs. 82%, PPV: 94.7% vs. 84%, and sensitivity: 97.8% vs. 73%).…”

“…Although there are studies that attempt to develop a prognostic risk scoring system of lncRNAs for leukemia patients [ 25 – 27 ], all of them focused on the type of acute myeloid leukemia (AML) and did not specifically investigate the childhood population. Also, a previous study indicated that the predictive accuracy seemed to be better using the integrated mRNA-lncRNA signature (AUC = 0.791) than the mRNA (AUC: 0.584) or lncRNA alone (AUC: 0.527) [ 28 ]. Therefore, in this study, we aimed to, for the first time, identify an lncRNA-mRNA prognostic signature for relapsed childhood ALL patients.…”

Prediction of lncRNA–Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network

“…Therefore, it is imperative to identify novel lncRNAs for early diagnosis, prognosis, and treatment of laryngeal neoplasms. Accumulating evidence has demonstrated that lncRNAs have played critical roles in the development and progression of laryngeal neoplasms ( Xiang et al, 2019 ; Zhang G et al, 2019 ; Li et al, 2020 ). LDA-EAGCN was further implemented to identify lncRNAs associated with laryngeal neoplasms.…”

Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients

“…Although TNM stage has been extensively believed as an important predictor to evaluate patients’ survival time, univariate, and multivariate Cox regression analyses in this study did not find a statistical significance with OS, including whole TNM stage and individual pathologic M, T, and N stage, implying the limited prognostic power of TNM staging, which was in accordance with the previous studies. [ 10 19 ]…”

“…Furthermore, some scholars attempted to integrate the lncRNAs and mRNAs as a prognostic signature for other cancers[ 7 8 ] and the results implicated the multi-mRNA/lncRNA-based classifier may be more effective than the risk score model constructed only by multi-lncRNA or multi-mRNA in prediction of OS. [ 9 10 ] Therefore, it may be more clinically valuable to develop an integrated prognostic signature for HBV-HCC.…”