Tags

Type your tag names separated by a space and hit enter

An eight-lncRNA signature predicts survival of breast cancer patients: a comprehensive study based on weighted gene co-expression network analysis and competing endogenous RNA network.
Breast Cancer Res Treat. 2019 May; 175(1):59-75.BC

Abstract

PURPOSE

To identify a lncRNA signature to predict survival of breast cancer (BRCA) patients.

METHODS

A total of 1222 BRCA case and control datasets were downloaded from the TCGA database. The weighted gene co-expression network analysis of differentially expressed mRNAs was performed to generate the modules associated with BRCA overall survival status and further construct a hub on competing endogenous RNA (ceRNA) network. LncRNA signatures for predicting survival of BRCA patients were generated using univariate survival analyses and a multivariate Cox hazard model analysis and validated and characterized for prognostic performance measured using receiver operating characteristic (ROC) curves.

RESULTS

A prognostic score model of eight lncRNAs signature was identified as Prognostic score = (0.121 × EXPAC007731.1) + (0.108 × EXPAL513123.1) + (0.105 × EXPC10orf126) + (0.065 × EXPWT1-AS) + (- 0.126 × EXPADAMTS9-AS1) + (- 0.130 × EXPSRGAP3-AS2) + (0.116 × EXPTLR8-AS1) + (0.060 × EXPHOTAIR) with median score 1.088. Higher scores predicted higher risk. The lncRNAs signature was an independent prognostic factor associated with overall survival. The area under the ROC curves (AUC) of the signature was 0.979, 0.844, 0.99 and 0.997 by logistic regression, support vector machine, decision tree and random forest models, respectively, and the AUCs in predicting 1- to 10-year survival were between 0.656 and 0.748 in the test dataset from TCGA database.

CONCLUSIONS

The eight-lncRNA signature could serve as an independent biomarker for prediction of overall survival of BRCA. The lncRNA-miRNA-mRNA ceRNA network is a good tool to identify lncRNAs that is correlated with overall survival of BRCA.

Authors+Show Affiliations

Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China. Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.The First Clinical School, Hubei University of Medicine, Shiyan, 442000, China.Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.College of Basic Medical Sciences, Hubei University of Medicine, Shiyan, 442000, China. gu.xinsheng@gmail.com.Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China. hd_814@sohu.com.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30715658

Citation

Sun, Min, et al. "An eight-lncRNA Signature Predicts Survival of Breast Cancer Patients: a Comprehensive Study Based On Weighted Gene Co-expression Network Analysis and Competing Endogenous RNA Network." Breast Cancer Research and Treatment, vol. 175, no. 1, 2019, pp. 59-75.
Sun M, Wu D, Zhou K, et al. An eight-lncRNA signature predicts survival of breast cancer patients: a comprehensive study based on weighted gene co-expression network analysis and competing endogenous RNA network. Breast Cancer Res Treat. 2019;175(1):59-75.
Sun, M., Wu, D., Zhou, K., Li, H., Gong, X., Wei, Q., Du, M., Lei, P., Zha, J., Zhu, H., Gu, X., & Huang, D. (2019). An eight-lncRNA signature predicts survival of breast cancer patients: a comprehensive study based on weighted gene co-expression network analysis and competing endogenous RNA network. Breast Cancer Research and Treatment, 175(1), 59-75. https://doi.org/10.1007/s10549-019-05147-6
Sun M, et al. An eight-lncRNA Signature Predicts Survival of Breast Cancer Patients: a Comprehensive Study Based On Weighted Gene Co-expression Network Analysis and Competing Endogenous RNA Network. Breast Cancer Res Treat. 2019;175(1):59-75. PubMed PMID: 30715658.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - An eight-lncRNA signature predicts survival of breast cancer patients: a comprehensive study based on weighted gene co-expression network analysis and competing endogenous RNA network. AU - Sun,Min, AU - Wu,Di, AU - Zhou,Ke, AU - Li,Heng, AU - Gong,Xingrui, AU - Wei,Qiong, AU - Du,Mengyu, AU - Lei,Peijie, AU - Zha,Jin, AU - Zhu,Hongrui, AU - Gu,Xinsheng, AU - Huang,Dong, Y1 - 2019/02/04/ PY - 2018/10/01/received PY - 2019/01/22/accepted PY - 2019/2/5/pubmed PY - 2019/8/21/medline PY - 2019/2/5/entrez KW - Breast cancer KW - Competing endogenous RNA network KW - Prognostic signature KW - The cancer genome atlas KW - Weighted gene co-expression network analysis SP - 59 EP - 75 JF - Breast cancer research and treatment JO - Breast Cancer Res. Treat. VL - 175 IS - 1 N2 - PURPOSE: To identify a lncRNA signature to predict survival of breast cancer (BRCA) patients. METHODS: A total of 1222 BRCA case and control datasets were downloaded from the TCGA database. The weighted gene co-expression network analysis of differentially expressed mRNAs was performed to generate the modules associated with BRCA overall survival status and further construct a hub on competing endogenous RNA (ceRNA) network. LncRNA signatures for predicting survival of BRCA patients were generated using univariate survival analyses and a multivariate Cox hazard model analysis and validated and characterized for prognostic performance measured using receiver operating characteristic (ROC) curves. RESULTS: A prognostic score model of eight lncRNAs signature was identified as Prognostic score = (0.121 × EXPAC007731.1) + (0.108 × EXPAL513123.1) + (0.105 × EXPC10orf126) + (0.065 × EXPWT1-AS) + (- 0.126 × EXPADAMTS9-AS1) + (- 0.130 × EXPSRGAP3-AS2) + (0.116 × EXPTLR8-AS1) + (0.060 × EXPHOTAIR) with median score 1.088. Higher scores predicted higher risk. The lncRNAs signature was an independent prognostic factor associated with overall survival. The area under the ROC curves (AUC) of the signature was 0.979, 0.844, 0.99 and 0.997 by logistic regression, support vector machine, decision tree and random forest models, respectively, and the AUCs in predicting 1- to 10-year survival were between 0.656 and 0.748 in the test dataset from TCGA database. CONCLUSIONS: The eight-lncRNA signature could serve as an independent biomarker for prediction of overall survival of BRCA. The lncRNA-miRNA-mRNA ceRNA network is a good tool to identify lncRNAs that is correlated with overall survival of BRCA. SN - 1573-7217 UR - https://www.unboundmedicine.com/medline/citation/30715658/An_eight_lncRNA_signature_predicts_survival_of_breast_cancer_patients:_a_comprehensive_study_based_on_weighted_gene_co_expression_network_analysis_and_competing_endogenous_RNA_network_ L2 - https://doi.org/10.1007/s10549-019-05147-6 DB - PRIME DP - Unbound Medicine ER -