Development and Verification of an Autophagy-Related lncRNA Signature to Predict Clinical Outcomes and Therapeutic Responses in Ovarian Cancer.

Abstract

Objective: Long noncoding RNAs (lncRNAs) are key regulators during ovarian cancer initiation and progression and are involved in mediating autophagy. In this study, we aimed to develop a prognostic autophagy-related lncRNA signature for ovarian cancer. Methods: Autophagy-related abnormally expressed lncRNAs were screened in ovarian cancer with the criteria values of |correlation coefficient| > 0.4 and p < 0.001. Based on them, a prognostic lncRNA signature was established. The Kaplan-Meier overall survival analysis was conducted in high- and low-risk samples in the training, verification, and entire sets, followed by receiver operating characteristics (ROCs) of 7-year survival. Multivariate Cox regression analysis was used for assessing the predictive independency of this signature after adjusting other clinical features. The associations between the risk scores and immune cell infiltration, PD-L1 expression, and sensitivity of chemotherapy drugs were assessed in ovarian cancer. Results: A total of 66 autophagy-related abnormally expressed lncRNAs were identified in ovarian cancer. An autophagy-related lncRNA signature was constructed for ovarian cancer. High-risk scores were indicative of poorer prognosis compared with the low-risk scores in the training, verification, and entire sets. ROCs of 7-year survival confirmed the well-predictive efficacy of this model. Following multivariate Cox regression analysis, this model was an independent prognostic factor. There were distinct differences in infiltrations of immune cells, PD-L1 expression, and sensitivity of chemotherapy drugs between high- and low-risk samples. Conclusions: This study constructed an autophagy-related lncRNA signature that was capable of predicting clinical outcomes and also therapeutic responses for ovarian cancer.

• Authors: Yan Li, Juan Wang, Fang Wang, Chengzhen Gao, Yuanyuan Cao, Jianhua Wang
• Journal: Frontiers in medicine (Front Med (Lausanne)) Vol. 8 Pg. 715250 ( 2021) ISSN: 2296-858X [Print] Switzerland
• PMID: 34671615 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Li, Wang, Wang, Gao, Cao and Wang.