Identification of a Five-Pseudogene Signature for Predicting Survival and Its ceRNA Network in Glioma

Abstract

Background: Glioma is the most common primary brain tumor with a dismal prognosis. It is urgent to develop novel molecular biomarkers and conform to individualized schemes.Methods: Differentially expressed pseudogenes between low grade glioma (LGG) and glioblastoma multiforme (GBM) were identified in the training cohort. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox proportional hazards regression analyses were used to select pseudogenes associated with prognosis of glioma. A risk signature was constructed based on the selected pseudogenes for predicting the survival of glioma patients. A pseudogene-miRNA-mRNA regulatory network was established and visualized using Cytoscape 3.5.1. Gene Oncology (GO) and signaling pathway analyses were performed on the targeted genes to investigate functional roles of the risk signature.Results: Five pseudogenes (ANXA2P2, EEF1A1P9, FER1L4, HILS1, and RAET1K) correlating with glioma survival were selected and used to establish a risk signature. Time-dependent receiver operating characteristic (ROC) curves revealed that the risk signature could accurately predict the 1, 3, and 5-year survival of glioma patients. GO and signaling pathway analyses showed that the risk signature was involved in regulation of proliferation, migration, angiogenesis, and apoptosis in glioma.Conclusions: In this study, a risk signature with five pseudogenes was constructed and shown to accurately predict 1-, 3-, and 5-year survival for glioma patient. The risk signature may serve as a potential target against glioma.

Identification of an Autophagy-Related 10-lncRNA-mRNA Signature for Distinguishing Glioblastoma Multiforme from Lower‑Grade Glioma and Prognosis Prediction

“…The prognostic accuracy was 0.840 and 0.744 using CGGA and TCGA dataset, respectively. These results were comparable to other risk classification systems established by the lipid metabolism [4], pseudogenes [7], necrosis [9] or module [10] related genes that were differentially expressed between GBM and LGG. Furthermore, previous studies indicated mRNA signature outperformed the lncRNA-based signature [23], while combined with lncRNAs [24,25] added the power in predicting prognosis.…”

“…These ndings suggested our new risk score may be a promising biomarker for GBM diagnosis and prognosis. In order to obtain better predictive effects in clinic, recent studies recommended to creating a nomogram that integrated the molecular signature with clinical indicators [7,10,17,18]. Similarly, a nomogram based on the risk signature, age, recurrence status and IDH mutation status was established in the training cohort.…”

Development of a nomogram for prognostic prediction of lower‐grade glioma based on alternative splicing signatures

“…26 New genes related to prognosis have been found based on database analysis, and a prognostic prediction model has been constructed. 27,28 With the development of radiology in recent years, research on establishing nomogram models in glioma based on radiomics data has also gradually increased, and this includes the preoperative prediction of tumor grade and the prediction of molecular biomarkers. 29,30 Based on clinical factors and AS events, an epigenetic factor closely related to the genesis, progression, metastasis, and drug resistance of tumors, this study established a nomogram of the prognostic prediction model for LGG patients, demonstrating the value of basic science research and the importance of translational medicine.…”

“…Similarly, Zhao et al also constructed a prognostic prediction model based on clinical data downloaded from the Surveillance, Epidemiology, and End Result (SEER) database 26 . New genes related to prognosis have been found based on database analysis, and a prognostic prediction model has been constructed 27,28 . With the development of radiology in recent years, research on establishing nomogram models in glioma based on radiomics data has also gradually increased, and this includes the preoperative prediction of tumor grade and the prediction of molecular biomarkers 29,30 .…”

Malignant Evaluation and Clinical Prognostic Values of m6A RNA Methylation Regulators in Glioblastoma

“…Even the combined surgical resection, radiation therapy, chemotherapy, and other therapies were broadly used, the recurrence of the patients with GBM is inevitable. Besides, the median survival of GBM patients is <15 months after a definite diagnosis (29)(30)(31). The m6A RNA methylation regulators were also reported to be associated with self-renewal, radio resistance, and tumorigenesis of GBM stem cells (32).…”