Ruyue Zhang1,2, Qingwen Zhu3, Detao Yin4, Zhe Yang2, Jinxiu Guo2, Jingmin Zhang2, Yubing Zhou2, Jane J Yu1,5. 1. Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. 2. Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. 3. Department of Otorhinolaryngology Head and Neck surgery, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China. 4. Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. 5. Department of Internal Medicine, Pulmonary, Critical Care and Sleep Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
Abstract
BACKGROUND: Autophagy is a "self-feeding" phenomenon of cells, which is crucial in mammalian development. Long non-coding RNA (lncRNA) is a new regulatory factor for cell autophagy, which can regulate the process of autophagy to affect tumor progression. However, poor attention has been paid to the roles of autophagy-related lncRNAs in breast cancer. OBJECTIVE: This study aimed to construct an autophagy-related lncRNA signature that can effectively predict the prognosis of breast cancer patients and explore the potential functions of these lncRNAs. METHODS: The RNA sequencing (RNA-Seq) data of breast cancer patients was collected from The Cancer Genome Atlas (TCGA) database and the GSE20685 database. Multivariate Cox analysis was implemented to produce an autophagy-related lncRNA signature in the TCGA cohort. The signature was then validated in the GSE20685 cohort. The receiver operator characteristic (ROC) curve was performed to evaluate the predictive ability of the signature. Gene set enrichment analysis (GSEA) was used to explore the potential functions based on the signature. Finally, the study developed a nomogram and internal verification based on the autophagy-related lncRNAs. RESULTS: A signature composed of 9 autophagy-related lncRNAs was determined as a prognostic model, and 1,109 breast cancer patients were divided into high-risk group and low-risk group based on median risk score of the signature. Further analysis demonstrated that the over survival (OS) of breast cancer patients in the high-risk group was poorer than that in the low-risk group based on the prognostic signature. The area under the curve (AUC) of ROC curve verified the sensitivity and specificity of this signature. Additionally, we confirmed the signature is an independent factor and found it may be correlated to the progression of breast cancer. GSEA showed gene sets were notably enriched in carcinogenic activation pathways and autophagy-related pathways. The qRT-PCR identified 5 lncRNAs with significantly differential expression in breast cancer cells based on the 9 lncRNAs of the prognostic model, and the results were consistent with the tissues. CONCLUSION: In summary, our signature has potential predictive value in the prognosis of breast cancer and these autophagy-related lncRNAs may play significant roles in the diagnosis and treatment of breast cancer.
BACKGROUND: Autophagy is a "self-feeding" phenomenon of cells, which is crucial in mammalian development. Long non-coding RNA (lncRNA) is a new regulatory factor for cell autophagy, which can regulate the process of autophagy to affect tumor progression. However, poor attention has been paid to the roles of autophagy-related lncRNAs in breast cancer. OBJECTIVE: This study aimed to construct an autophagy-related lncRNA signature that can effectively predict the prognosis of breast cancer patients and explore the potential functions of these lncRNAs. METHODS: The RNA sequencing (RNA-Seq) data of breast cancer patients was collected from The Cancer Genome Atlas (TCGA) database and the GSE20685 database. Multivariate Cox analysis was implemented to produce an autophagy-related lncRNA signature in the TCGA cohort. The signature was then validated in the GSE20685 cohort. The receiver operator characteristic (ROC) curve was performed to evaluate the predictive ability of the signature. Gene set enrichment analysis (GSEA) was used to explore the potential functions based on the signature. Finally, the study developed a nomogram and internal verification based on the autophagy-related lncRNAs. RESULTS: A signature composed of 9 autophagy-related lncRNAs was determined as a prognostic model, and 1,109 breast cancer patients were divided into high-risk group and low-risk group based on median risk score of the signature. Further analysis demonstrated that the over survival (OS) of breast cancer patients in the high-risk group was poorer than that in the low-risk group based on the prognostic signature. The area under the curve (AUC) of ROC curve verified the sensitivity and specificity of this signature. Additionally, we confirmed the signature is an independent factor and found it may be correlated to the progression of breast cancer. GSEA showed gene sets were notably enriched in carcinogenic activation pathways and autophagy-related pathways. The qRT-PCR identified 5 lncRNAs with significantly differential expression in breast cancer cells based on the 9 lncRNAs of the prognostic model, and the results were consistent with the tissues. CONCLUSION: In summary, our signature has potential predictive value in the prognosis of breast cancer and these autophagy-related lncRNAs may play significant roles in the diagnosis and treatment of breast cancer.
Authors: Imke Tiessen; Marie H Abildgaard; Michal Lubas; Helene M Gylling; Cornelia Steinhauer; Elin J Pietras; Sven Diederichs; Lisa B Frankel; Anders H Lund Journal: Oncogene Date: 2019-03-14 Impact factor: 9.867
Authors: José A Peña-Flores; Mercedes Bermúdez; Rosalío Ramos-Payán; Carlos E Villegas-Mercado; Uriel Soto-Barreras; Daniela Muela-Campos; Alexis Álvarez-Ramírez; Brenda Pérez-Aguirre; Ana D Larrinua-Pacheco; César López-Camarillo; Jorge A López-Gutiérrez; Julio Garnica-Palazuelos; Marvin E Estrada-Macías; Juan L Cota-Quintero; Andrés A Barraza-Gómez Journal: Front Oncol Date: 2022-08-01 Impact factor: 5.738