Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer

Abstract

Background: Ovarian cancer (OC) is the gynecologic malignant tumor with the highest mortality rate. The interaction between autophagy and the tumor immune microenvironment(TIM) has clinical importance. Hence, it is necessary to explore reliable biomarkers associated with autophagy-related genes (ARGs) for risk stratification in OC. Methods: We obtained ARGs from the MSigDB database and downloaded the expression profile of OC from the Cancer Genome Atlas (TCGA) database. The k-means unsupervised clustering method was applied to classify patients. The single-sample gene set enrichment analysis (ssGSEA) method was used to quantify the levels of infiltration of 24 subtypes of immune cells. Metascape and GSEA were performed to reveal the differential gene enrichment in signaling pathways and cellular processes of the subtypes. Next, we established a risk signature by least absolute shrinkage and selection operator (LASSO) Cox regression. Time-dependent receiver operating characteristic (ROC) curve analysis, Kaplan-Meier survival analysis and univariate Cox regression were used to evaluate the efficiency of the risk signature. Then, we analyzed the correlation between the risk score and 7 ARGs and immune checkpoints. Finally, we selected ULK2 and GABARAPL1 and verified their expression by qRT-PCR, WB and immunohistochemistry. Findings: We divided patients into the cluster A and cluster B subtypes. Our research confirmed that the patients in cluster A have a significantly longer survival time and a higher level of immune infiltration than the patients in cluster B. We established a signature for risk stratification, according to which we could divide the patients into high-or low-risk groups and predict their outcomes. Clinical correlation analysis found that the risk score is more reliable than other indicators for predicting disease-free survival (DFS). ULK2 and GABARAPL1 may play important roles in ovarian tumorigenesis and may be potential therapeutic targets in OC. Interpretation: The autophagy-related gene risk signature might be effective for risk stratification in OC. Moreover, our study provides a promising prognostic indicator that may predict the clinical efficacy of immunotherapy treatments. Funding Statement: This work was supported by the project of degree and postgraduate education and teaching reform in Hunan Province (JG2018A003),the National Science Foundation of China (81874137),the National Science Foundation of Hunan Province (2019JJ50308). the Outstanding Youth Foundation of Hunan Province (2018JJ1047), the Hunan Province Science and technology talent promotion project (2019TJ-Q10). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained at all participating sites, and all the participants provided signed, written, informed consent.