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. 2019 Oct;23(10):6775-6784.
doi: 10.1111/jcmm.14556. Epub 2019 Aug 20.

A prognostic 10-lncRNA expression signature for predicting the risk of tumour recurrence in breast cancer patients

Jianing Tang  1 , Jiangbo Ren  2 , Qiuxia Cui  1 , Dan Zhang  3 , Deguang Kong  4 , Xing Liao  1 , Mengxin Lu  1 , Yan Gong  2 , Gaosong Wu  1
Affiliations

A prognostic 10-lncRNA expression signature for predicting the risk of tumour recurrence in breast cancer patients

Jianing Tang et al. J Cell Mol Med. 2019 Oct.

Abstract

Breast cancer is one of the most frequently diagnosed malignancies and a leading cause of cancer death among females. Multiple molecular alterations are observed in breast cancer. LncRNA transcripts were proved to play important roles in the biology of tumorigenesis. In this study, we aimed to identify lncRNA expression signature that can predict breast cancer patient survival. We developed a 10-lncRNA signature-based risk score which was used to separate patients into high-risk and low-risk groups. Patients in the low-risk group had significantly better survival than those in the high-risk group. Receiver operating characteristic analysis indicated that this signature exhibited excellent diagnostic efficiency for 1-, 3- and 5-year disease-relapse events. Moreover, multivariate Cox regression analysis demonstrated that this 10-lncRNA signature was an independent risk factor when adjusting for several clinical signatures such as age, tumour size and lymph node status. The prognostic value of risk scores was validated in the validation set. In addition, a nomogram was established and the calibration plots analysis indicated the good performance and clinical utility of the nomogram. In conclusion, our results demonstrated that this 10-lncRNA signature effectively grouped patients at low and high risk of disease recurrence.

Keywords: GEO; breast cancer; lncRNA; nomogram; prognosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart and 10‐time cross‐validation for tuning parameter selection. A, Flow chart indicating the process used to select target genes included in the analysis. B, Ten‐time cross‐validation for tuning parameter selection in the lasso model. C, LASSO coefficient profiles of the 19 prognostic lncRNAs. A vertical line is drawn at the value chosen by 10‐fold cross‐validation
Figure 2
Figure 2
Univariate Cox regression analysis of the ten prognostic lncRNAs in the signature. A, HAGLR. B, MIR210HG. C, RGMB‐AS1. D, TMEM161B‐AS1. E, CADM3‐AS1. F, LINC00293. G, LINC00910. H, LINC01187. I, PDZRN3‐AS1. J, ZBED5‐AS1
Figure 3
Figure 3
Validation of prognostic risk score model in training set. A, Time‐dependent receiver operating characteristic curves of the 10‐lncRNA signature. B, Kaplan‐Meier survival analysis of the 10‐lncRNA signature
Figure 4
Figure 4
Kaplan‐Meier survival analysis for patients according to the 10‐lncRNA‐based signature stratified by clinicopathological risk factors. A, B, Tumour size. C, D, Lymph node status. E, F, Tumour grade. G, H, Age
Figure 5
Figure 5
Validation of 10‐lncRNA signature in validation sets. A, Time‐dependent receiver operating characteristic (ROC) curves of the 10‐lncRNA signature in GSE19615. B, Kaplan‐Meier survival analysis of the 10‐lncRNA signature in GSE19615. C, Time‐dependent ROC curves of the 10‐lncRNA signature in GSE20685. D, Kaplan‐Meier survival analysis of the 10‐lncRNA signature in GSE20685
Figure 6
Figure 6
Nomogram to predict risk of cancer recurrence. A, Nomograms to predict risk of cancer recurrence. B, 3‐y nomogram calibration curves of training set. C, 5‐y nomogram calibration curves of training set. D, 3‐y nomogram calibration curves of validation set GSE19615. E, 5‐y nomogram calibration curves of validation set GSE19615. F, 3‐y nomogram calibration curves of validation set GSE20685. G 5‐y nomogram calibration curves of validation set GSE20685
Figure 7
Figure 7
Gene set enrichment analysis
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