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Long non-coding RNA-based signatures to improve prognostic prediction of breast cancer.

ABSTRACT: Breast cancer (BC) is a disease of high mortality rate because of high malignant, while early diagnosis and personal management may make a better prognosis possible. This study aimed to establish and validate lncRNAs signatures to improve the prognostic prediction for BC.RNA sequencing data along with the corresponding clinical information of patients with BC were gained from The Cancer Genome Atlas (TCGA). Prognostic differentially expressed lncRNAs were obtained using differentially expressed lncRNAs analysis (P value <.01 and |fold change|?>?2) and univariate cox regression (P value <.05). By applying least absolute shrinkage and selection operation (LASSO) Cox regression analysis along with 10-fold cross-validation, 2 lncRNA-based signatures were constructed in the training, test and whole set.A 14-lncRNAs signature and a 10-lncRNAs signature were built for overall survival (OS) and relapse-free survival (RFS) respectively in the 3 sets. BC patients were divided into high-risk groups and low-risk groups depended on median risk score value. Significant differences were found for OS and RFS between 2 groups in the 3 sets. The time-dependent receiver operating characteristic (ROC) curves analysis demonstrated that our lncRNAs signatures had better predictive capacities of survival and recurrence for BC patients as well as enhancing the predictive ability of the tumor node metastasis (TNM) stage system.These results indicate that the 2 lncRNAs signatures with the potential to be biomarkers to predict the prognosis of BC for OS and RFS.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7535645 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Long non-coding RNA-based signatures to improve prognostic prediction of breast cancer.

Zhang Yi Y Wang Yuzhi Y Tian Gang G Jiang Tianhua T

Medicine 20201001 40

Breast cancer (BC) is a disease of high mortality rate because of high malignant, while early diagnosis and personal management may make a better prognosis possible. This study aimed to establish and validate lncRNAs signatures to improve the prognostic prediction for BC.RNA sequencing data along with the corresponding clinical information of patients with BC were gained from The Cancer Genome Atlas (TCGA). Prognostic differentially expressed lncRNAs were obtained using differentially expressed ...[more]

PMID: 33019395

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Project description:BackgroundDysregulated long non-coding RNAs (lncRNAs) may serve as potential biomarkers of cancers including breast cancer (BRCA). This study aimed to identify lncRNAs with strong prognostic value for BRCA.MethodsLncRNA expression profiles of 929 tissue samples were downloaded from TANRIC database. We performed differential expression analysis between paired BRCA and adjacent normal tissues. Survival analysis was used to identify lncRNAs with prognostic value. Univariate and multivariate Cox regression analyses were performed to confirm the independent prognostic value of potential lncRNAs. Dysregulated signaling pathways associated with lncRNA expression were evaluated using gene set enrichment analysis.ResultsWe found that a total of 398 lncRNAs were significantly differentially expressed between BRCA and adjacent normal tissues (adjusted P value <= 0.0001 and |logFC| >= 1). Additionally, 381 potential lncRNAs were correlated Overall Survival (OS) (P value < 0.05). A total of 48 lncRNAs remained when differentially expressed lncRNAs overlapped with lncRNAs that had prognostic value. Among the 48 lncRNAs, one lncRNA (LINC01614) had stronger prognostic value and was highly expressed in BRCA tissues. LINC01614 expression was validated as an independent prognostic factor using univariate and multivariate analyses. Higher LINC01614 expression was observed in several molecular subgroups including estrogen receptors+, progesterone receptors+ and human epidermal growth factor receptor 2 (HER2)+ subgroup, respectively. Also, BRCA carrying one of four gene mutations had higher expression of LINC01614 including AOAH, CIT, HER2 and ODZ1. Higher expression of LINC01614 was positively correlated with several gene sets including TGF-β1 response, CDH1 signals and cell adhesion pathways.ConclusionsA novel lncRNA LINC01614 was identified as a potential biomarker for prognosis prediction of BRCA. This study emphasized the importance of LINC01614 and further research should be focused on it.

Dataset Information

Long non-coding RNA-based signatures to improve prognostic prediction of breast cancer.

Publications

Long non-coding RNA-based signatures to improve prognostic prediction of breast cancer.

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