Objective: Increasing evidence emphasizes the clinical implications of RNA binding proteins (RBPs) in cancers. This study aimed to develop a RBP signature for predicting prognosis in glioma. Methods: Two glioma datasets as training (n = 693) and validation (n = 325) sets were retrieved from the CGGA database. In the training set, univariate Cox regression analysis was conducted to screen prognosis-related RBPs based on differentially expressed RBPs between WHO grade II and IV. A ten-RBP signature was then established. The predictive efficacy was evaluated by ROCs. The applicability was verified in the validation set. The pathways involving the risk scores were analyzed by ssGSEA. scRNA-seq was utilized for evaluating their expression in different glioma cell types. Moreover, their expression was externally validated between glioma and control samples. Results: Based on 39 prognosis-related RBPs, a ten RBP signature was constructed. High risk score distinctly indicated a poorer prognosis than low risk score. AUCs were separately 0.838 and 0.822 in the training and validation sets, suggesting its well performance for prognosis prediction. Following adjustment of other clinicopathological characteristics, the signature was an independent risk factor. Various cancer-related pathways were significantly activated in samples with high risk score. The scRNA-seq identified that risk RBPs were mainly expressed in glioma malignant cells. Their high expression was also found in glioma than control samples. Conclusion: This study developed a novel RBP signature for robustly predicting prognosis of glioma following multi-data set verification. These RBPs may affect the progression of glioma.
Objective: Increasing evidence emphasizes the clinical implications of RNA binding proteins (RBPs) in cancers. This study aimed to develop a RBP signature for predicting prognosis in glioma. Methods: Two glioma datasets as training (n = 693) and validation (n = 325) sets were retrieved from the CGGA database. In the training set, univariate Cox regression analysis was conducted to screen prognosis-related RBPs based on differentially expressed RBPs between WHO grade II and IV. A ten-RBP signature was then established. The predictive efficacy was evaluated by ROCs. The applicability was verified in the validation set. The pathways involving the risk scores were analyzed by ssGSEA. scRNA-seq was utilized for evaluating their expression in different glioma cell types. Moreover, their expression was externally validated between glioma and control samples. Results: Based on 39 prognosis-related RBPs, a ten RBP signature was constructed. High risk score distinctly indicated a poorer prognosis than low risk score. AUCs were separately 0.838 and 0.822 in the training and validation sets, suggesting its well performance for prognosis prediction. Following adjustment of other clinicopathological characteristics, the signature was an independent risk factor. Various cancer-related pathways were significantly activated in samples with high risk score. The scRNA-seq identified that risk RBPs were mainly expressed in glioma malignant cells. Their high expression was also found in glioma than control samples. Conclusion: This study developed a novel RBP signature for robustly predicting prognosis of glioma following multi-data set verification. These RBPs may affect the progression of glioma.
Authors: Tathiane M Malta; Camila F de Souza; Thais S Sabedot; Tiago C Silva; Maritza S Mosella; Steven N Kalkanis; James Snyder; Ana Valeria B Castro; Houtan Noushmehr Journal: Neuro Oncol Date: 2018-04-09 Impact factor: 12.300