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Research article
Jiani Guo
Affiliated Huai'an No1 People's Hospital of Nanjing Medical University: Huaian First People's Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9899-0924
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Triple negative breast cancer (TNBC) remains the most incurable subtype of breast cancer owing to high heterogeneity, aggressive nature, and lack of treatment options. It is generally acknowledged that epithelial-mesenchymal transition (EMT) is the key step in tumor metastasis.
Methods: With the application of TCGA and GEO database, we identified EMT-related lncRNAs by Cox univariate regression analysis. Optimum risk scores were calculated and used to divide TNBC patients into high/low-risk subgroups by the median value using lasso regression analysis. Kaplan-Meier and ROC curve analyses were applied for model validation. Then we assessed the risk model from multi-omic aspects including immune infiltration, drug sensitivity, mutability spectrum, signaling pathways, and clinical indicators.
Results: The risk model was composed of 22 EMT-related long noncoding RNAs (lncRNAs), which seemed to be valuable in prognostic prediction of TNBC patients. The model could act as an independent prognostic factor of TNBC, and showed a robust prognostic ability in the stratification analysis.
Conclusions: Together, our study successfully established a risk model with great accuracy and efficacy in prognosis prediction of TNBC patients.
Keywords
lncRNA, epithelial-mesenchymal transition, prognostic signature, triple negative breast cancer
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This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryTable1.xlsx
Table S1. A total of 1033 LncRNAs highly associated with EMT.
- SupplementaryTable2.xlsx
Table S2. A total of 285 prognostic lncRNAs screened by Cox regression 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.
Research article
Jiani Guo
Affiliated Huai'an No1 People's Hospital of Nanjing Medical University: Huaian First People's Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9899-0924
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
View the published article at Journal of Oncology.
Version 1
Posted 31 Mar, 2021
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
View the published article at Journal of Oncology.
Background: Triple negative breast cancer (TNBC) remains the most incurable subtype of breast cancer owing to high heterogeneity, aggressive nature, and lack of treatment options. It is generally acknowledged that epithelial-mesenchymal transition (EMT) is the key step in tumor metastasis.
Methods: With the application of TCGA and GEO database, we identified EMT-related lncRNAs by Cox univariate regression analysis. Optimum risk scores were calculated and used to divide TNBC patients into high/low-risk subgroups by the median value using lasso regression analysis. Kaplan-Meier and ROC curve analyses were applied for model validation. Then we assessed the risk model from multi-omic aspects including immune infiltration, drug sensitivity, mutability spectrum, signaling pathways, and clinical indicators.
Results: The risk model was composed of 22 EMT-related long noncoding RNAs (lncRNAs), which seemed to be valuable in prognostic prediction of TNBC patients. The model could act as an independent prognostic factor of TNBC, and showed a robust prognostic ability in the stratification analysis.
Conclusions: Together, our study successfully established a risk model with great accuracy and efficacy in prognosis prediction of TNBC patients.
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.
- SupplementaryTable1.xlsx
Table S1. A total of 1033 LncRNAs highly associated with EMT.
- SupplementaryTable2.xlsx
Table S2. A total of 285 prognostic lncRNAs screened by Cox regression analysis.
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