ArticleCancer informatics2023
Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model.
Article in Cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Nucleolar proteomics identifies S100A16 as a key nucleolar protein driving breast cancer metastasis.Cell death & disease · 2025Article
- Role of ABCC5 in cancer drug resistance and its potential as a therapeutic target.Frontiers in cell and developmental biology · 2024Review
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models. Methods: A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net were used to identify metastasis related genes. Results: Five regression models with penalties were applied in the additive hazards model and jointly found 9 genes including Conclusion: This study showed that
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