Am J Transl Res 2020;12(9):5320-5331 www.ajtr.org /ISSN:1943-8141/AJTR0113418 Original Article Screening of autophagy genes as prognostic indicators for glioma patients Shanqiang Qu1,2*, Shuhao Liu3*, Weiwen Qiu4, Jin Liu5, Huafu Wang6 1Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China; 2Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; 3De- partment of Gastrointestinal Surgery, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China; Departments of 4Neurology, 5Neurosurgery, 6Clinical Pharmacy, Lishui People’s Hospital (The Sixth Affili- ated Hospital of Wenzhou Medical University), Lishui 323000, China. *Equal contributors. Received April 27, 2020; Accepted July 31, 2020; Epub September 15, 2020; Published September 30, 2020 Abstract: Although autophagy is reported to be involved in tumorigenesis and cancer progression, its correlation with the prognosis of glioma patients remains unclear. Thus, the aim of this study was to identify prognostic au- tophagy-related genes, analyze their correlation with clinicopathological features of glioma, and further construct a prognostic model for glioma patients. After 139 autophagy-related genes were obtained from the GeneCards database, their expression data in glioma patients were extracted from the Chinese Glioma Genome Atlas data- base. Univariate and multivariate COX regression analyses were performed to identify prognostic autophagy-related genes. Ten hub autophagy-related genes associated with prognosis were identified. The autophagy risk score (ARS) was only positively correlated with histopathology (P = 0.000) and World Health Organization grade (P = 0.000). Kaplan-Meier analysis showed that the overall survival of patients with a high ARS was significantly worse than that of patients with a low ARS (hazard ratio = 1.59, 95% confidence interval = 1.25-2.03, P = 0.0001). In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed several common biological pro- cesses and signaling pathways related to the 10 hub genes in glioblastoma. A prediction model was developed for glioma patients, which demonstrated high prediction efficiency on calibration.Moreover, the area under the receiver operating characteristic curve values for 1-, 3- and 5-year survival probabilities were 0.790, 0.861, and 0.853, re- spectively. In conclusion, we identified 10 autophagy-related genes that can serve as novel prognostic biomarkers for glioma patients. Our prediction model accurately predicted patient outcomes, and thus, may be a valuable tool in clinical practice. Keywords: Neoplasms, survival, nomograms Introduction of gliomas has been improved to some extent, the prognosis of glioma patients continues to Glioma of the brain is a common human malig- not be satisfactory. Notably, overall survival nancy that is harmful to human health globally. (OS) varies greatly among patients with the With the development of imaging technologies, same pathological diagnosis [3], which shows the overall incidence of gliomas in the past that the traditional pathological diagnosis is decades has continued to increase [1]. Accor- insufficient for judging patients’ prognosis. In ding to an analysis of the Central Brain Tumor addition, the common clinical prognostic mark- Registry of the United States (CBTRUS) data, an ers (e.g., isocitrate dehydrogenase [IDH] muta- estimated 87,240 cases of primary central ner- tion 1p/19q co-deletion) only distinguish two vous system (CNS) tumors will be diagnosed in subgroups of patients with the same histopa- the United States in 2020, including 25,800 thology. These markers are still not sufficient malignant tumors and 61430 non-malignant for more subtle stratification and cannot reflect tumors [2]. At present, the main treatment str- the individual prognosis of each patient. There- ategy for gliomas worldwide remains surgery fore, there is an urgent need for a new predic- followed by postoperative adjuvant radiothera- tion model that offers greater accuracy for glio- py and chemotherapy. Although the treatment ma patient prognosis. Moreover, such a model Clinical value of autophagy-related genes would be valuable for guiding personalized me- ecards.org/). Genes with a relevance score >8 dicine for glioma patients [4]. were screened as autophagy-related genes in this study. Research in recent years has demonstrated that autophagy is a process of degradation and Patient selection reuse of cellular components that plays a key role in tumorigenesis, cancer development, First, we extracted the data for all patients in and metastasis [5]. Studies have also shown the dataset 4 cohort of the CGGA (http://cgga. that tumor cells, especially under stress condi- org.cn/index.jsp) [10], which is a database for tions, can obtain the energy and substances storing clinical and pathological information on necessary for survival through autophagy, and glioma patients in China. Second, patients with thus, autophagy is a type of survival mecha- missing data were excluded. Finally, we inclu- nism for tumor cells [6]. Thus, the inhibition of ded data for 269 glioma patients in our an- autophagy can reduce the tolerance of tumor alysis. cells to stress, increase the sensitivity of tu- mor cells to anti-tumor drugs, and improve the Identification of prognostic genes effect of anticancer therapy [7]. Notably, multi- First, univariate Cox regression analysis was ple preclinical studies have found that knock- performed to screen autophagy-related genes out of the key autophagy genes (e.g., beclin-1, significantly associated with the prognosis of Atg12, Atg5) can improve the killing ability of glioma patients. The autophagy-related genes antineoplastic drugs, which indicates that au- for which P<0.05 were further included in mul- tophagy can protect cancer cells and inhibit tivariate regression analysis, and independent the efficacy of antineoplastic drugs [8, 9]. prognostic autophagy-related genes were iden- These findings further suggest that autophagy- tified. related genes play a key role in tumorigenesis and development and have a significant impact Oncomine database and human protein atlas on the prognosis of cancer patients. As such, (HPA) analysis these genes may also provide prognostic mark- The Oncomine gene expression array database ers. However, autophagy is a complex biologi- (www.oncomine.org) was used to assess the cal process involving hundreds of genes that is mRNA expression levels of the identified prog- common in the life processes of eukaryotes. nostic genes. In addition, the expression levels The prognostic value of autophagy genes in gli- of the corresponding proteins in gliomas and oma patients remains unclear, and investi- normal tissues were reviewed in the HPA gation of which autophagy-related genes have (http://www.proteinatlas.org/) [11]. the potential to become prognostic markers is worthwhile. Pathway analysis Toward this end, we obtained the expression From the prognostic autophagy-related genes, profile data for autophagy-related genes and we determined the nine most relevant coex- the clinicopathological data of patients from pression genes in the TCGA-CNS/Brain data- the Chinese Glioma Genome Atlas (CGGA) da- base (http://www.cbioportal.org/). Gene Onto- taset in order to search for independent prog- logy (GO) analysis of all prognostic genes nostic factors by analyzing the correlations and coexpression genes was performed using between autophagy-related genes and patien- the DAVID [12] database (https://david.ncifcrf. ts’ prognosis. Additionally, from the identified gov/), including biological process, cellular genes, we constructed a new prediction model component, and molecular function. The Cyto- for glioma patients’ prognosis. scape tool [13] was used to implement the Materials and methods Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We used Screening of autophagy-related genes the String database [14, 15] to observe prote- in-protein interaction between the proteins en- The genes related to autophagy were searched coded by the prognostic genes (https://string- in the GeneCards database (https://www.gen- db.org/). 5321 Am J Transl Res 2020;12(9):5320-5331 Clinical value of autophagy-related genes Correlations between autophagy risk score patients are summarized in Table S1. The aver- (ARS) and clinicopathological features of age age of all patients was 42.58±11.83 years. patients The incidence of glioma was higher in males than in females, and the median survival time The ARS was constructed according to the of all patients was 31.2 months (interquartile expression of a prognostic gene and a correla- range, 0.7-138.1 months). The 1-, 3-, and 5- tion risk coefficient for the gene. The formula year survival rates for all glioma patients were for the ARS is as follows: ARS = gene1*β1 + 77.32%, 46.84%, and 34.94%, respectively. gene2*β2 + gene3*β3…….gene (n)*β (n). Chi- square test was performed to explore correla- Screening of prognostic autophagy-related tions between clinical parameters and the ARS. genes in 269 glioma patients The relationship of the ARS to the prognosis of glioma patients was analyzed by Kaplan-Meier To search for prognostic autophagy-related ge- survival curve analysis. nes of glioma patients, all candidate genes related to autophagy were searched through Development and validation of nomogram the GeneCards database. A total of 139 auto- phagy-related