Dataset Information

Identification and validation of stemness-related lncRNA prognostic signature for breast cancer.

ABSTRACT: BACKGROUND:Long noncoding RNAs (lncRNAs) are emerging as crucial contributors to the development of breast cancer and are involved in the stemness regulation of breast cancer stem cells (BCSCs). LncRNAs are closely associated with the prognosis of breast cancer patients. It is critical to identify BCSC-related lncRNAs with prognostic value in breast cancer. METHODS:A co-expression network of BCSC-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate and multivariate Cox proportional hazards analyses were used to identify a stemness risk model with prognostic value. Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation were conducted to analyze the risk model. RESULTS:In this study, BCSC-related lncRNAs in breast cancer were identified. We evaluated the prognostic value of these BCSC-related lncRNAs and eventually obtained a prognostic risk model consisting of 12 BCSC-related lncRNAs (Z68871.1, LINC00578, AC097639.1, AP003119.3, AP001207.3, LINC00668, AL122010.1, AC245297.3, LINC01871, AP000851.2, AC022509.2 and SEMA3B-AS1). The risk model was further verified as a novel independent prognostic factor for breast cancer patients based on the calculated risk score. Moreover, based on the risk model, the low- risk and high-risk groups displayed different stemness statuses. CONCLUSIONS:These findings suggested that the 12 BCSC-related lncRNA signature might be a promising prognostic factor for breast cancer and can promote the management of BCSC-related therapy in clinical practice.

SUBMITTER: Li X

PROVIDER: S-EPMC7461324 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identification and validation of stemness-related lncRNA prognostic signature for breast cancer.

Li Xiaoying X Li Yang Y Yu Xinmiao X Jin Feng F

Journal of translational medicine 20200831 1

<h4>Background</h4>Long noncoding RNAs (lncRNAs) are emerging as crucial contributors to the development of breast cancer and are involved in the stemness regulation of breast cancer stem cells (BCSCs). LncRNAs are closely associated with the prognosis of breast cancer patients. It is critical to identify BCSC-related lncRNAs with prognostic value in breast cancer.<h4>Methods</h4>A co-expression network of BCSC-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate ...[more]

PMID: 32867770

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Project description:BackgroundAutophagy 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.ObjectiveThis 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.MethodsThe 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.ResultsA 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.ConclusionIn 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.

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Dataset Information

Identification and validation of stemness-related lncRNA prognostic signature for breast cancer.

Publications

Identification and validation of stemness-related lncRNA prognostic signature for breast cancer.

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