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Primary research
Wenru Tang
Kunming University of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0769-6448
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Background：Triple-negative breast cancer (TNBC) is an essential type of breast cancer (BC). Compared with other molecular subtypes of BC, TNBC has the features of fast tumor increase, quick recurrence and natural metastasis. It is more urgent to establish a comprehensive evaluation system containing multiple biomarkers than single parameter.
Methods：We conduct a bioinformatics analysis on 13 BC expression datasets from the Gene Expression Omnibus (GEO), which covered 2950 samples. We took 3484 genes with a more significant difference between TNBC and normal-like candidate genes for weighted correlation network analysis (WGCNA). A total of 54 genes were chosen as hub genes with great connectivity with the TNBC significant module. Based on The Cancer Genome Atlas (TCGA) data, we identify the best prognostic three lncRNA. Multivariate Cox regression was used to construct a 3-lncRNA risk score model. We evaluated prognostic capacity using time-dependent subject operating characteristics (ROC) and Kaplan-Meier (KM) survival analysis. The predictive power of the model was demonstrated by the time-dependent ROC spline and Kaplan-Meier spline. At the same time, it also shows good predictive ability in the validation set. Ultimately, Functional enrichment analysis of hub genes and three lncRNAs were offered to advise the possible biological pathways.
Results：The construct LNC00337, DEPCE-AS1, DDX11-AS1 multi-factor risk scoring model was meaningfully associated with the prognosis of TNBC patients. Through survival analysis, the risk score efficiently divided the patients into high-risk groups with poor overall survival. The time-dependent ROC curve revealed that the model presented robust in predicting survival over the first 3 years. The validity of the model in the validation set is also verified. Finally, functional enrichment analysis proposed some biological pathways that may be correlated to the tumor.
Conclusions：In our study, we established a lncRNA-based model to prognosticate the prediction of TNBC, which might afford a strong prognosis estimate tool to help therapy policy-making in the clinic.
Keywords
Triple-negative breast cancer, Long non-coding RNA, Biomarker, WGCNA, GEO, TCGA, Survival analysis
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Primary research
Wenru Tang
Kunming University of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0769-6448
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 05 Aug, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background：Triple-negative breast cancer (TNBC) is an essential type of breast cancer (BC). Compared with other molecular subtypes of BC, TNBC has the features of fast tumor increase, quick recurrence and natural metastasis. It is more urgent to establish a comprehensive evaluation system containing multiple biomarkers than single parameter.
Methods：We conduct a bioinformatics analysis on 13 BC expression datasets from the Gene Expression Omnibus (GEO), which covered 2950 samples. We took 3484 genes with a more significant difference between TNBC and normal-like candidate genes for weighted correlation network analysis (WGCNA). A total of 54 genes were chosen as hub genes with great connectivity with the TNBC significant module. Based on The Cancer Genome Atlas (TCGA) data, we identify the best prognostic three lncRNA. Multivariate Cox regression was used to construct a 3-lncRNA risk score model. We evaluated prognostic capacity using time-dependent subject operating characteristics (ROC) and Kaplan-Meier (KM) survival analysis. The predictive power of the model was demonstrated by the time-dependent ROC spline and Kaplan-Meier spline. At the same time, it also shows good predictive ability in the validation set. Ultimately, Functional enrichment analysis of hub genes and three lncRNAs were offered to advise the possible biological pathways.
Results：The construct LNC00337, DEPCE-AS1, DDX11-AS1 multi-factor risk scoring model was meaningfully associated with the prognosis of TNBC patients. Through survival analysis, the risk score efficiently divided the patients into high-risk groups with poor overall survival. The time-dependent ROC curve revealed that the model presented robust in predicting survival over the first 3 years. The validity of the model in the validation set is also verified. Finally, functional enrichment analysis proposed some biological pathways that may be correlated to the tumor.
Conclusions：In our study, we established a lncRNA-based model to prognosticate the prediction of TNBC, which might afford a strong prognosis estimate tool to help therapy policy-making in the clinic.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
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