Dataset Information

Identification of a four-long non-coding RNA signature in predicting breast cancer survival.

ABSTRACT: Long non-coding RNAs (lncRNAs) serve key roles in tumorigenesis and are differentially expressed in cancer. Using bioinformatics and statistical methods, the present study aimed to identify an lncRNA signature to predict breast cancer survival. The gene expression data of 768 patients with breast cancer were downloaded from The Cancer Genome Atlas database, and Cox regression, Kaplan-Meier and receiver operating characteristic (ROC) analyses were performed to construct and validate a predictive model. Gene Ontology term enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis were employed to predict the functions of the indicated lncRNAs. A signature consisting of four lncRNAs, including PVT1, MAPT-AS1, LINC00667 and LINC00938, was identified, and patients were subsequently divided into high- and low-risk groups according to the median risk score. Kaplan-Meier analysis confirmed that patients in the high-risk group exhibited significantly poorer overall survival rate in both the training (P=0.0151) and the validation set (P=0.0016); furthermore, ROC analysis confirmed that the model could predict patient survival with a certain sensitivity and specificity. In conclusion, the four-lncRNA signature presents a potential prognostic biomarker for breast cancer that may be relevant for clinical application.

SUBMITTER: Zhu M

PROVIDER: S-EPMC6924049 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Identification of a four-long non-coding RNA signature in predicting breast cancer survival.

Zhu Mingjie M Lv Qing Q Huang Hu H Sun Chunlei C Pang Da D Wu Junqiang J

Oncology letters 20191107 1

Long non-coding RNAs (lncRNAs) serve key roles in tumorigenesis and are differentially expressed in cancer. Using bioinformatics and statistical methods, the present study aimed to identify an lncRNA signature to predict breast cancer survival. The gene expression data of 768 patients with breast cancer were downloaded from The Cancer Genome Atlas database, and Cox regression, Kaplan-Meier and receiver operating characteristic (ROC) analyses were performed to construct and validate a predictive ...[more]

PMID: 31897133

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Project description:BACKGROUND Accumulating evidence suggests the involvement of long non-coding RNAs (lncRNAs) as oncogenic or tumor suppressive regulators in the development of various cancers. In the present study, we aimed to identify a lncRNA signature based on RNA sequencing (RNA-seq) data to predict survival in esophageal cancer. MATERIAL AND METHODS The RNA-seq lncRNA expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs were screened out between esophageal cancer and normal tissues. Univariate and multivariate Cox regression analysis were performed to establish a lncRNA-related prognostic model. Receiver operating characteristic (ROC) analysis was conducted to test the sensitivity and specificity of the model. GO (gene ontology) functional and KEGG pathway enrichment analyses were performed for mRNAs co-expressed with the lncRNAs to explore the potential functions of the prognostic lncRNAs. RESULTS A total of 265 differentially expressed lncRNAs were identified between esophageal cancer and normal tissues. After univariate and multivariate Cox regression analysis, eight lncRNAs (GS1-600G8.5, LINC00365, CTD-2357A8.3, RP11-705O24.1, LINC01554, RP1-90J4.1, RP11-327J17.1, and LINC00176) were finally screened out to establish a predictive model by which patients could be classified into high-risk and low-risk groups with significantly different overall survival. Further analysis indicated independent prognostic capability of the 8-lncRNA signature from other clinicopathological factors. ROC curve analysis demonstrated good performance of the 8-lncRNA signature. Functional enrichment analysis showed that the prognostic lncRNAs were mainly associated with esophageal cancer related biological processes such as regulation of glucose metabolic process and amino acid and lipids metabolism. CONCLUSIONS Our study developed a novel candidate model providing additional and more powerful prognostic information beyond conventional clinicopathological factors for survival prediction of esophageal cancer patients. Moreover, it also brings us new insights into the molecular mechanisms underlying esophageal cancer.

Dataset Information

Identification of a four-long non-coding RNA signature in predicting breast cancer survival.

Publications

Identification of a four-long non-coding RNA signature in predicting breast cancer survival.

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