Dataset Information

Identification and Validation of an Autophagy-Related lncRNA Signature for Patients With Breast Cancer.

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.

SUBMITTER: Zhang R

PROVIDER: S-EPMC7892971 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Identification and Validation of an Autophagy-Related lncRNA Signature for Patients With Breast Cancer.

Zhang Ruyue R Zhu Qingwen Q Yin Detao D Yang Zhe Z Guo Jinxiu J Zhang Jingmin J Zhou Yubing Y Yu Jane J JJ

Frontiers in oncology 20210205

<h4>Background</h4>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.<h4>Objective</h4>This study aimed to construct an autophagy-related lncRNA signature that can effectively predict the prognosis of breast ca ...[more]

PMID: 33614483

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Project description:BackgroundCuproptosis is a novel form of cell death referred to as copper-dependent cytotoxicity. The regulation of proptosis is becoming an increasingly popular cancer treatment modality. To date, few studies have attempted to identify the cuproptosis-related long non-coding RNAs (CRLs). In this study, we sought to investigate the CRLs and construct a novel prognostic model for colorectal cancer (CRC).MethodsThe RNA-sequencing data of CRC patients were obtained from The Cancer Genome Atlas database. An analysis was conducted to identify the differentially expressed long non-coding RNAs, and a correlation analysis was performed to identify the CRLs. A univariate Cox analysis was conducted to select the prognostic CRLs. Based on a least absolute shrinkage and selection operator regression analysis, a prognostic signature comprising the 22 identified CRLs was constructed. A survival receiver operating characteristic curve analysis was conducted to evaluate the performance of the signature. Finally, an in vitro analysis was performed to investigate the function of lncRNA AC090116.1 in the CRC cells.ResultsA signature comprising 22 CRLs was developed. The patients in the training and validation sets were divided into the low- and high-risk groups and had significantly different survival probabilities. This signature had outstanding prognostic accuracy in predicting the 5-year overall survival of patients [training set, area under the curve (AUC) =0.820; validation set, AUC =0.810]. The pathway enrichment analysis showed that the differential genes between low and high groups were enriched in several important oncogenic- and metastatic-associated processes and pathways. Finally, the in vitro experiments showed that AC090116.1 silencing promoted the cuproptosis processes and suppressed cell proliferation.ConclusionsOur findings provided promising insights into the CRLs involved in CRC. The signature based on CRLs has been successfully devised to prognosticate the clinical outcomes and treatment responses in patients.

Dataset Information

Identification and Validation of an Autophagy-Related lncRNA Signature for Patients With Breast Cancer.

Background

Objective

Methods

Results

Conclusion

Publications

Identification and Validation of an Autophagy-Related lncRNA Signature for Patients With Breast Cancer.

Similar Datasets

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