Abstract
Ovarian cancer (OC) is one of the most lethal gynecologic malignant tumors. The interaction between autophagy and the tumor immune microenvironment has clinical importance. Hence, it is necessary to explore reliable biomarkers associated with autophagy-related genes (ARGs) for risk stratification in OC. Here, we obtained ARGs from the MSigDB database and downloaded the expression profile of OC from TCGA database. The k-means unsupervised clustering method was used for clustering, and two subclasses of OC (cluster A and cluster B) were identified. 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. We found that patients in cluster A were significantly associated with higher immune infiltration and immune-associated signaling pathways. Then, we established a risk model by LASSO Cox regression. ROC analysis and Kaplan-Meier analysis were applied for evaluating the efficiency of the risk signature, patients with low-risk got better outcomes than those with high-risk in overall survival. Finally, ULK2 and GABARAPL1 expression was further validated in clinical samples. In conclusion, Our study constructed an autophagy-related prognostic indicator, and identified two promising targets in OC.
Highlights
Ovarian cancer (OC) is one of the most lethal gynecologic malignant tumors [1]
The clinical data and corresponding gene expression profiles of OC patients were downloaded from the The cancer genome atlas (TCGA) database
The biological functions, metabolic pathways and signal transduction pathways with significant enrichment of differential genes were analyzed by the Metascape database, and the signaling pathways enriched in cluster A and cluster B were analyzed by Gene set enrichment analysis (GSEA)
Summary
Ovarian cancer (OC) is one of the most lethal gynecologic malignant tumors [1]. Due to the nonspecific symptoms in the early stage and the lack of effective screening techniques of the disease, a large number of patients are diagnosed at an advanced stage, of which the 5-year survival rate was less than 30%. Because of the heterogeneity of OC, there are obvious stratifications into histological or molecular subtypes, and the results may be significantly different even for patients with similar clinical features and treatment regimens. These observations showed that the clinicopathological features and current classification are not sufficient for prediction and risk stratification. It is difficult to meet the needs of clinicians [5, 6] It is of great significance for improving the prognosis of OC to search for specific prognostic biomarkers and therapeutic targets with higher predictive value
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