Prognostic Value of an Autophagy-Related Five-Gene Signature for Lower-Grade Glioma Patients

Abstract

Background: Molecular characteristics can be good indicators of tumor prognosis and have been introduced into the classification of gliomas. The prognosis of patients with newly classified lower-grade gliomas (LGGs, including grade 2 and grade 3 gliomas) is highly heterogeneous, and new molecular markers are urgently needed.Methods: Autophagy related genes (ATGs) were obtained from Human Autophagy Database (HADb). From the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), gene expression profiles including ATG expression information and patient clinical data were downloaded. Cox regression analysis, receiver operating characteristic (ROC) analysis, Kaplan–Meier analysis, random survival forest algorithm (RSFVH) and stratification analysis were performed.Results: Through univariate Cox regression analysis, we found a total of 127 ATGs associated with the prognosis of LGG patients from TCGA dataset and a total of 131 survival-related ATGs from CGGA dataset. Using TCGA dataset as the training group (n = 524), we constructed a five-ATG signature (including BAG1, BID, MAP1LC3C, NRG3, PTK6), which could divide LGG patients into two risk groups with significantly different overall survival (Log Rank P < 0.001). Then we confirmed in the independent CGGA dataset that the five-ATG signature had the ability to predict prognosis (n = 431, Log Rank P < 0.001). We further discovered that the predictive ability of the five-ATG signature was better than the existing clinical indicators and IDH mutation status. In addition, the five-ATG signature could further classify patients after receiving radiotherapy or chemotherapy into groups with different prognosis.Conclusions: We identified a five-ATG signature that could be a reliable prognostic marker and might be therapeutic targets for autophagy therapy for LGG patients.

Immunotherapeutic Value of MAP1LC3C and Its Candidate FDA-Approved Drugs Identified by Pan-Cancer Analysis, Virtual Screening and Sensitivity Analysis

“…Consistent with previously reported results, differentially expressed MAP1LC3C has certain prognostic values in some cancers, especially COAD, LGG and LUAD. MAP1LC3C is a marker that indicates poor prognosis in patients with colon cancer, low-grade glioma and lung adenocarcinoma ( Xu et al, 2020 ; Guo et al, 2021 ; Wang et al, 2021 ).…”

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

“… 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. 50 Most studies only used two datasets for model construction and validation, and the sample size was not large enough.…”

Identification of autophagic target RAB13 with small‐molecule inhibitor in low‐grade glioma via integrated multi‐omics approaches coupled with virtual screening of traditional Chinese medicine databases

“…Some prognostic predictor, such as GRID2, FOXO1, MYC, PTK6, IKBKE, BIRC5 and TP73, has been screened based on the analysis of autophagy‐related genes (ATGs) by a novel prognostic model and nomogram 40 . In addition, several researches identified autophagic biomarkers of LGG according the data of TCGA and the Chinese Glioma Genome Atlas (CGGA) 41‐43 . However, these current studies may lead to incomplete exploitation of the cancer genome because of one‐dimensional analyses of somatic mutations, and all these studies as mentioned above only conducted bioinformatics analysis, lacking validations of these identified targets.…”

“… 40 In addition, several researches identified autophagic biomarkers of LGG according the data of TCGA and the Chinese Glioma Genome Atlas (CGGA). 41 , 42 , 43 However, these current studies may lead to incomplete exploitation of the cancer genome because of one‐dimensional analyses of somatic mutations, and all these studies as mentioned above only conducted bioinformatics analysis, lacking validations of these identified targets.…”