Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature
Abstract Background Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients. Methods We obtained IRL expression profiles in large BC...
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| Format: | Article |
| Language: | English |
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BMC
2020-11-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | http://link.springer.com/article/10.1186/s12967-020-02578-4 |
| _version_ | 1818217164141756416 |
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| author | Jianguo Lai Bo Chen Guochun Zhang Xuerui Li Hsiaopei Mok Ning Liao |
| author_facet | Jianguo Lai Bo Chen Guochun Zhang Xuerui Li Hsiaopei Mok Ning Liao |
| author_sort | Jianguo Lai |
| collection | DOAJ |
| description | Abstract Background Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients. Methods We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA). Results A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001). Conclusions A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies. |
| first_indexed | 2024-12-12T07:03:31Z |
| format | Article |
| id | doaj.art-b3e0a802bb984f429a67bead0c045998 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-12-12T07:03:31Z |
| publishDate | 2020-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-b3e0a802bb984f429a67bead0c0459982022-12-22T00:33:48ZengBMCJournal of Translational Medicine1479-58762020-11-0118111010.1186/s12967-020-02578-4Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signatureJianguo Lai0Bo Chen1Guochun Zhang2Xuerui Li3Hsiaopei Mok4Ning Liao5Department of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesAbstract Background Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients. Methods We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA). Results A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001). Conclusions A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.http://link.springer.com/article/10.1186/s12967-020-02578-4Breast cancerImmunelncRNASignatureSurvival |
| spellingShingle | Jianguo Lai Bo Chen Guochun Zhang Xuerui Li Hsiaopei Mok Ning Liao Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature Journal of Translational Medicine Breast cancer Immune lncRNA Signature Survival |
| title | Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature |
| title_full | Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature |
| title_fullStr | Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature |
| title_full_unstemmed | Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature |
| title_short | Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature |
| title_sort | molecular characterization of breast cancer a potential novel immune related lncrnas signature |
| topic | Breast cancer Immune lncRNA Signature Survival |
| url | http://link.springer.com/article/10.1186/s12967-020-02578-4 |
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