An autophagic gene-based signature to predict the survival of patients with low-grade gliomas.

Since autophagy remains an important topic of investigation, the RNA-sequence profiles of autophagy-related genes (ARGs) can provide insights into predicting low-grade gliomas (LGG) prognosis. The RNA-seq profiles of autophagic genes and prognosis data of LGG were integrated from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model were carried out to identify the differentially expressed prognostic autophagy-related genes. Then, the autophagic-gene signature was formed and verified in TCGA test set and external CGGA cohorts. Time-dependent receiver operating characteristic (ROC) was examined to test the accuracy of this signature feature. A nomogram was conducted to meet the needs of clinicians. Sankey diagrams were performed to visualize the relationship between the multigene signatures and clinic-pathological features. Twenty-four ARGs were finally identified most relevant to LGG prognosis. According to the specific prediction index formula, the patients were classified into low-risk or high-risk groups. Prognostic accuracy was proved by time-dependent ROC analysis, with AUC 0.9, 0.93, and 0.876 at the survival time of 2-, 3-, and 5-year, respectively, which was superior to the AUC of the isocitrate dehydrogenase (IDH) mutation. The result was confirmed while validated in the TCGA test set and external validation CGGA cohort. A nomogram was constructed to meet individual needs. With a visualization approach, Sankey diagrams show the relationship of the histological type, IDH status, and predict index. In TCGA and CGGA cohorts, both low-risk groups displayed better survival rate in LGG while histological type and IDH status did not show consistency results. 24-ARGs may play crucial roles in the progression of LGG, and LGG patients were effectively divided into low-risk and high-risk groups according to prognostic prediction. Overall, our study will provide novel strategies for clinical applications.

Chen J ，Li Y ，Han X ，Pan Y ，Qian X ... -  《Cancer Medicine》

Identification and validation of a novel eight mutant-derived long non-coding RNAs signature as a prognostic biomarker for genome instability in low-grade glioma.

Maimaiti A ，Wang X ，Pei Y ，Nuermaimaiti N ，Tuersunniyazi A ，Abula Y ，Feng Z ，Jiang L ，Shi X ，Kasimu M ... -  《Aging-US》

An Immune-Related Signature for Predicting the Prognosis of Lower-Grade Gliomas.

Lower-grade gliomas (LGGs) have more favorable outcomes than glioblastomas; however, LGGs often progress to process glioblastomas within a few years. Numerous studies have proven that the tumor microenvironment (TME) is correlated with the prognosis of glioma. LGG RNA-Sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were extracted and then divided into training and testing cohorts, respectively. Immune-related differentially expressed genes (DEGs) were screened to establish a prognostic signature by a multivariate Cox proportional hazards regression model. The immune-related risk score and clinical information, such as age, sex, World Health Organization (WHO) grade, and isocitrate dehydrogenase 1 (IDH1) mutation, were used to independently validate and develop a prognostic nomogram. GO and KEGG pathway analyses to DEGs between immune-related high-risk and low-risk groups were performed. Sixteen immune-related genes were screened for establishing a prognostic signature. The risk score had a negative correlation with prognosis, with an area under the receiver operating characteristic (ROC) curve of 0.941. The risk score, age, grade, and IDH1 mutation were identified as independent prognostic factors in patients with LGGs. The hazard ratios (HRs) of the high-risk score were 5.247 [95% confidence interval (CI) = 3.060-8.996] in the multivariate analysis. A prognostic nomogram of 1-, 3-, and 5-year survival was established and validated internally and externally. Go and KEGG pathway analyses implied that immune-related biological function and pathways were involved in the TME. The immune-related prognostic signature and the prognostic nomogram could accurately predict survival.

Zhang H ，Li X ，Li Y ，Chen B ，Zong Z ，Shen L ... -  《Frontiers in Immunology》

A Risk Score Signature Consisting of Six Immune Genes Predicts Overall Survival in Patients with Lower-Grade Gliomas.

Lower-grade gliomas (LGGs) are less aggressive with a long overall survival (OS) time span. Because of individualized genomic features, a prognostic system incorporating molecular signatures can more accurately predict OS. Differential expression analysis between LGGs and normal tissues was performed using the Gene Expression Omnibus (GEO) datasets (GSE4290 and GSE12657). Immune-related differentially expressed genes (ImmPort-DEGs) were analyzed for functional enrichment. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an immune risk score signature (IRSS). We extracted information from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to establish and validate the model. The relationship of model gene sets with immune infiltration was analyzed based on gene set variation analysis (GSVA) scores. Patients were divided into low- and high-risk groups based on the median score. The time-dependent receiver-operating characteristic (ROC) curve and the Kaplan-Meier curve were used to evaluate the model. Then, a precise prognostic nomogram was established, and its efficacy was verified. A total of 18 related immune genes were identified, building a 6-gene IRSS (BMP2, F2R, FGF13, PCSK1, PRKCB, and PTGER3). DEGs were enriched in T cell and NK cell regulatory pathways. Immune infiltration analysis confirmed that the gene signature correlated with a decrease in innate immune cells. In terms of model evaluation, ROC curves at 1, 3, and 5 years showed moderate predictive ability of IRSS (AUC = 0.930, 0.797, and 0.728). The Cox regression analysis revealed that IRSS was an independent prognostic factor, and the nomogram model had good predictive ability (C - index = 0.828). Meanwhile, the predictive power of IRSS was also confirmed in the training cohort. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly worse in all cohorts. IRSS may serve as a novel survival prediction tool in the classification of LGG patients.

Wu Y ，Peng Z ，Gu S ，Wang H ，Xiang W ... -  《-》

A five-microRNA signature for individualized prognosis evaluation and radiotherapy guidance in patients with diffuse lower-grade glioma.

Despite the prognostic value of IDH and other gene mutations found in diffuse glioma, markers that judge individual prognosis of patients with diffuse lower-grade glioma (LGG) are still lacking. This study aims to develop an expression-based microRNA signature to provide survival and radiotherapeutic response prediction for LGG patients. MicroRNA expression profiles and relevant clinical information of LGG patients were downloaded from The Cancer Genome Atlas (TCGA; the training group) and the Chinese Glioma Genome Atlas (CGGA; the test group). Cox regression analysis, random survival forests-variable hunting (RSFVH) screening and receiver operating characteristic (ROC) were used to identify the prognostic microRNA signature. ROC and TimeROC curves were plotted to compare the predictive ability of IDH mutation and the signature. Stratification analysis was conducted in patients with radiotherapy information. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to explore the biological function of the signature. We identified a five-microRNA signature that can classify patients into low-risk or high-risk group with significantly different survival in the training and test datasets (P < 0.001). The five-microRNA signature was proved to be superior to IDH mutation in survival prediction (AUCtraining = 0.688 vs 0.607). Stratification analysis found the signature could further divide patients after radiotherapy into two risk groups. GO and KEGG analyses revealed that microRNAs from the prognostic signature were mainly enriched in cancer-associated pathways. The newly discovered five-microRNA signature could predict survival and radiotherapeutic response of LGG patients based on individual microRNA expression.

Zhang JH ，Hou R ，Pan Y ，Gao Y ，Yang Y ，Tian W ，Zhu YB ... -  《-》