Prognostic value and immune relevancy of a combined autophagy-, apoptosis- and necrosis-related gene signature in glioblastoma.

Glioblastoma (GBM) is considered the most malignant and devastating intracranial tumor without effective treatment. Autophagy, apoptosis, and necrosis, three classically known cell death pathways, can provide novel clinical and immunological insights, which may assist in designing personalized therapeutics. In this study, we developed and validated an effective signature based on autophagy-, apoptosis- and necrosis-related genes for prognostic implications in GBM patients. Variations in the expression of genes involved in autophagy, apoptosis and necrosis were explored in 518 GBM patients from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis were performed to construct a combined prognostic signature. Kaplan-Meier survival, receiver-operating characteristic (ROC) curves and Cox regression analyses based on overall survival (OS) and progression-free survival (PFS) were conducted to estimate the independent prognostic performance of the gene signature. The Chinese Glioma Genome Atlas (CGGA) dataset was used for external validation. Finally, we investigated the differences in the immune microenvironment between different prognostic groups and predicted potential compounds targeting each group. A 16-gene cell death index (CDI) was established. Patients were clustered into either the high risk or the low risk groups according to the CDI score, and those in the low risk group presented significantly longer OS and PFS than the high CDI group. ROC curves demonstrated outstanding performance of the gene signature in both the training and validation groups. Furthermore, immune cell analysis identified higher infiltration of neutrophils, macrophages, Treg, T helper cells, and aDCs, and lower infiltration of B cells in the high CDI group. Interestingly, this group also showed lower expression levels of immune checkpoint molecules PDCD1 and CD200, and higher expression levels of PDCD1LG2, CD86, CD48 and IDO1. Our study proposes that the CDI signature can be utilized as a prognostic predictor and may guide patients' selection for preferential use of immunotherapy in GBM.

Bi Y ，Wu ZH ，Cao F 《BMC CANCER》

Systematic identification, development, and validation of prognostic biomarkers involving the tumor-immune microenvironment for glioblastoma.

Gliomas are infiltrative neoplasms with a highly invasive nature. Due to its distinct genomic, genetic and epigenetic features, the immune prognostic signature (IPS) and immune microenvironment of glioblastoma (GBM) merit further research. We aimed to explore prognosis-related immune genes and develop an IPS model for predicting prognosis in GBM. RNA-sequencing data, as well as clinical information, from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) public cohorts were analyzed. To develop the IPS, least absolute shrinkage and selection operator (LASSO) Cox analysis was performed for immune-related genes that were differentially expressed between GBM and normal tissues. Then, interaction effects of the IPS on the immune microenvironment were systematically analyzed; the precise prognostic model was developed based on the IPS and clinical data and was then further validated. A total of 21 immune prognostic genes were identified based on GBM microenvironment status. An 8-gene IPS was established, and the GBM patients were effectively stratified into low- and high-risk groups in the TCGA cohort as a training set. Univariate and multivariate Cox analyses revealed that IPS was an independent prognostic factor, and the prognostic performance of individual IPS genes was systematically illustrated. In addition, a comprehensive and novel nomogram model was initially established to estimate overall survival in TCGA-GBM patients, and high-risk patients had higher levels of dendritic cell and neutrophil infiltration. Furthermore, the nomogram model was developed and validated in the CGGA validation set. The low-risk IPS was linked to a stronger response to anti-PD-L1 immunotherapy and clinical advantages in the IMvigor210 cohort. This novel IPS with promising biomarkers classifies GBM patients into subgroups with distinct clinical outcomes and immunophenotypes. Our findings and this resource may help to characterize the immune microenvironment, inform cancer immunotherapy and facilitate the development of precision immuno-oncology.

Zhao B ，Wang Y ，Wang Y ，Chen W ，Liu PH ，Kong Z ，Dai C ，Wang Y ，Ma W ... -  《-》

Identification of a novel autophagy-related prognostic signature and small molecule drugs for glioblastoma by bioinformatics.

To explore the autophagy-related prognostic signature (ARPs) via data mining in gene expression profiles for glioblastoma (GBM). Using the Cancer Genome Atlas (TCGA) database, we obtained 156 GBM samples and 5 adjacent normal samples, and denoted them as discovery cohort. Univariate Cox regression analysis was used to screen autophagy genes that related to GBM prognosis. Then, the least absolute shrinkage and selection operator Cox regression model was used to construct an autophagy-based ARPs, which was validated in an external cohort containing 80 GBM samples. The patients in the above-mentioned cohorts were divided into low-risk group and high-risk group according to the median prognostic risk score, and the diagnostic performance of the model was assessed by receiver operating characteristic curve analyses. The gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were performed between the high-risk and low-risk patients. Additionally, the genetic features of ARPs, such as genetic variation profiles, correlations with tumor-infiltrating lymphocytes (TILs), and potential drug sensitivity, were further assessed in the TCGA-GBM data set. A signature of ARPs including NDUFB9, BAK1, SUPT3H, GAPDH, CDKN1B, CHMP6, and EGFR were detected and validated. We identified a autophagy-related prognosis 7-gene signature correlated survival prognosis, immune infiltration, level of cytokines, and cytokine receptor in tumor microenvironment. Furthermore, the signature was tested in several pathways related to disorders of tumor microenvironment, as well as cancer-related pathways. Additionally, a range of small molecular drugs, shown to have a potential therapeutic effect on GBM. We constructed an autophagy-based 7-gene signature, which could serve as an independent prognostic indicator for cases of GBM and sheds light on the role of autophagy as a potential therapeutic target in GBM.

Wang D ，Jiang Y ，Wang T ，Wang Z ，Zou F ... -  《BMC Medical Genomics》

A novel risk signature with 6 RNA binding proteins for prognosis prediction in patients with glioblastoma.

Recent studies suggested that RNA binding proteins (RBPs) were related to the tumorigenesis and progression of glioma. This study was conducted to identify prognostic RBPs of glioblastoma (GBM) and construct an RBP signature to predict the prognosis of GBM.Univariate Cox regression analysis was carried out to identify the RBPs associated with overall survival of GBM in the The Cancer Genome Atlas (TCGA), GSE16011, and Repository for Molecular Brain Neoplasia data (Rembrandt) datasets, respectively. Overlapping RBPs from the TCGA, GSE16011, and Rembrandt datasets were selected. The biological role of prognostic RBPs was assessed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction analyses. Least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis were used to construct an RBP-related risk signature. The prognostic value of RBP signature was measured by Kaplan-Meier method and time-dependent receiver operating characteristic curve. A nomogram based on independent prognostic factors was established to predict survival for GBM. The CGGA cohort was used as the validation cohort for external validation.This study identified 27 RBPs associated with the prognosis of GBM and constructed a 6-RPBs signature. Kaplan-Meier curves suggested that high-risk score was associated with a poor prognosis. Area under the curve of 1-, 3-, and 5-year overall survival was 0.618, 0.728, and 0.833 for TCGA cohort, 0.655, 0.909, and 0.911 for GSE16011 cohort, and 0.665, 0.792, and 0.781 for Rembrandt cohort, respectively. A nomogram with 4 parameters (age, chemotherapy, O6-methylguanine-DNA methyltransferase promoter status, and risk score) was constructed. The calibration curve showed that the nomogram prediction was in good agreement with the actual observation.The 6-RBPs signature could effectively predict the prognosis of GBM, and our findings supplemented the prognostic index of GBM to a certain extent.

Huang QR ，Li JW ，Pan XB 《-》

A risk signature with four autophagy-related genes for predicting survival of glioblastoma multiforme.

Glioblastoma multiforme (GBM) is a devastating brain tumour without effective treatment. Recent studies have shown that autophagy is a promising therapeutic strategy for GBM. Therefore, it is necessary to identify novel biomarkers associated with autophagy in GBM. In this study, we downloaded autophagy-related genes from Human Autophagy Database (HADb) and Gene Set Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to identify genes for constructing a risk signature. A nomogram was developed by integrating the risk signature with clinicopathological factors. Time-dependent receiver operating characteristic (ROC) curve and calibration plot were used to evaluate the efficiency of the prognostic model. Finally, four autophagy-related genes (DIRAS3, LGALS8, MAPK8 and STAM) were identified and were used for constructing a risk signature, which proved to be an independent risk factor for GBM patients. Furthermore, a nomogram was developed based on the risk signature and clinicopathological factors (IDH1 status, age and history of radiotherapy or chemotherapy). ROC curve and calibration plot suggested the nomogram could accurately predict 1-, 3- and 5-year survival rate of GBM patients. For function analysis, the risk signature was associated with apoptosis, necrosis, immunity, inflammation response and MAPK signalling pathway. In conclusion, the risk signature with 4 autophagy-related genes could serve as an independent prognostic factor for GBM patients. Moreover, we developed a nomogram based on the risk signature and clinical traits which was validated to perform better for predicting 1-, 3- and 5-year survival rate of GBM.

Wang Y ，Zhao W ，Xiao Z ，Guan G ，Liu X ，Zhuang M ... -  《-》