Identification of an autophagy-related 10-lncRNA-mRNA signature for distinguishing glioblastoma multiforme from lower‑grade glioma and prognosis prediction

Abstract

Autophagy may provide the source of nutrients for tumor cells. We aim to develop an autophagy-related signature to predict the progression from lower-grade gliomas (LGG) to glioblastoma multiforme (GBM) and prognosis. Totally, 686 differentially expressed genes (DEGs) and 73 long non-coding RNAs (DELs) were identified between GBM and LGG samples from the Chinese Glioma Genome Atlas (CGGA). Of them, 131 DEGs were intersected with autophagy genes from the Human Autophagy Database; while 54 DELs co-expressed with autophagy-related DEGs. Ten autophagy-related genes were associated with overall survival and could distinguish GBM from LGG, with the accuracy of 0.891 using CGGA dataset and 0.790 using The Cancer Genome Atlas (TCGA) dataset. The risk score was established based on these 10 genes. Patients with higher risk score were at an increased risk of developing GBM (49.7% vs. 21.3%; p < 0.001) and worse prognosis than those in low risk group. The prognostic accuracy was 0.840 and 0.744 for CGGA and TCGA dataset, respectively. Age, recurrence, isocitrate dehydrogenase mutation and risk score were independent prognostic factors and thus they were used to build a nomogram which showed the highest prognostic power. This established nomogram may aid the clinical decision making of personalized treatment.

m6A Regulatory Gene-Mediated Methylation Modification in Glioma Survival Prediction

“…Using univariate and multivariate Cox analyses, we found that older age and m6A risk score were independent risk factors for predicting the prognosis of patients with glioma. A previous study found that age was an independent risk factor for GBM, and aging resulted in a poorer prognosis ( Wei et al, 2020 ). Our results are consistent with those of previous studies.…”

“…Additionally, the prognosis of individual patients with glioma is difficult to predict because few clinical biomarkers or parameters are available to reflect disease progression and neurological outcomes. Although a series of functional gene sets have been identified, the exact roles of these clusters remain to be elucidated ( He et al, 2020 ; Wei et al, 2020 ; Zhang et al, 2021 ). Thus, a better understanding of the molecular mechanisms of glioma, including its genetic background and prognosis-related factors, is essential for the diagnosis and treatment of this malignant disease.…”

Identification of a Nomogram with an Autophagy-Related Risk Signature for Survival Prediction in Patients with Glioma

“…The signature could distinguish GBM from LGG and predict OS in glioma patients, which also validated using TCGA dataset. 48 Five-ATG signature identified by Guo et al using two datasets was validated that it had good predictive performance. 49 Xu et al used 15ARGs to construct a risk model that could predict the prognosis of glioma patients.…”

Immune-Related lncRNA Pairs Clinical Prognosis Model Construction for Hepatocellular Carcinoma

“…10 Long non-coding RNA (lncRNA) is an RNA molecule composed of more than 200 nucleotides, 11 which is closely related to the occurrence and development of tumors and can be used as a potential marker for tumor diagnosis and prognosis. 12 At the same time, a large amount of evidence shows that lncRNA is an active participant in all stages of tumor immunity. 13 In recent years, with the continuous development of gene sequencing technology, bioinformatics databases have been widely used in the field of medical research.…”