An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer

Jiafeng Chen; Xinrong Li; Shuixin Yan; Jiadi Li; Yuxin Zhou; Minhua Wu; Jinhua Ding; Jiahui Yang; Yijie Yuan; Ye Zhu; Weizhu Wu

doi:10.3389/fonc.2022.929240

An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer

Front Oncol. 2022 Dec 15:12:929240. doi: 10.3389/fonc.2022.929240. eCollection 2022.

Authors

Jiafeng Chen^{1

2}, Xinrong Li^{1

2}, Shuixin Yan^{1

2}, Jiadi Li^{1

2}, Yuxin Zhou^{1

2}, Minhua Wu¹, Jinhua Ding¹, Jiahui Yang¹, Yijie Yuan¹, Ye Zhu¹, Weizhu Wu¹

Affiliations

¹ Department of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China.
² School of Medicine, Ningbo University, Ningbo, China.

Abstract

Introduction: Breast cancer (BRCA) is the most common malignancy among women worldwide. It was widely accepted that autophagy and the tumor immune microenvironment play an important role in the biological process of BRCA. Long non-coding RNAs (lncRNAs), as vital regulatory molecules, are involved in the occurrence and development of BRCA. The aim of this study was to assess the prognosis of BRCA by constructing an autophagy-related lncRNA (ARlncRNA) prognostic model and to provide individualized guidance for the treatment of BRCA.

Methods: The clinical data and transcriptome data of patients with BRCA were acquired from the Cancer Genome Atlas database (TCGA), and autophagy-related genes were obtained from the human autophagy database (HADb). ARlncRNAs were identified by conducting co‑expression analysis. Univariate and multivariate Cox regression analysis were performed to construct an ARlncRNA prognostic model. The prognostic model was evaluated by Kaplan-Meier survival analysis, plotting risk curve, Independent prognostic analysis, clinical correlation analysis and plotting ROC curves. Finally, the tumor immune microenvironment of the prognostic model was studied.

Results: 10 ARlncRNAs(AC090912.1, LINC01871, AL358472.3, AL122010.1, SEMA3B-AS1, BAIAP2-DT, MAPT-AS1, DNAH10OS, AC015819.1, AC090198.1) were included in the model. Kaplan-Meier survival analysis of the prognostic model showed that the overall survival(OS) of the low-risk group was significantly better than that of the high-risk group (p< 0.001). Multivariate Cox regression analyses suggested that the prognostic model was an independent prognostic factor for BRCA (HR = 1.788, CI = 1.534-2.084, p < 0.001). ROCs of 1-, 3- and 5-year survival revealed that the AUC values of the prognostic model were all > 0.7, with values of 0.779, 0.746, and 0.731, respectively. In addition, Gene Set Enrichment Analysis (GSEA) suggested that several tumor-related pathways were enriched in the high-risk group, while several immune‑related pathways were enriched in the low-risk group. Patients in the low-risk group had higher immune scores and their immune cells and immune pathways were more active. Patients in the low-risk group had higher PD-1 and CTLA-4 levels and received more benefits from immune checkpoint inhibitors (ICIs) therapy.

Discussion: The ARlncRNA prognostic model showed good performance in predicting the prognosis of patients with BRCA and is of great significance to guide the individualized treatment of these patients.

Keywords: autophagy; breast cancer; long non-coding RNAs; prognostic model; survival; tumor immune microenvironment.