ArticleJournal of translational medicine2021
Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models.
Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 36 citations in OpenAlex.
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- Ovarian cancer recurrence prediction: comparing confirmatory to real-world predictors with machine learning.ESMO real world data and digital oncology · 2026Article
- Prospective Breast Cancer Biomarkers Identified Using miR-526b-Driven Metabolic Alterations.Cancer informatics · 2026Article
- Extracellular Matrix-Associated Biomarkers for Hepatocellular Carcinoma: Insights From Machine Learning and Single-Cell Analysis.International journal of genomics · 2026Article
- Integrative Machine Learning Model for Overall Survival Prediction in Breast Cancer Using Clinical and Transcriptomic Data.Biology · 2025Article
- BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.Journal of translational medicine · 2025Article
- A Weibull mixture cure frailty model for high-dimensional covariates.Statistical methods in medical research · 2025Article
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- Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization.BMC cancer · 2025Article
- GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.Journal of cellular and molecular medicine · 2024Article
- Epithelial cell-related prognostic risk model in breast cancer based on single-cell and bulk RNA sequencing.Heliyon · 2024Article
- Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia.Annals of hematology · 2024Article
- A transformer model for cause-specific hazard prediction.BMC bioinformatics · 2024Article
- Assessment of the albumin-bilirubin score in breast cancer patients with liver metastasis after surgery.Heliyon · 2023Article
- Advances in Electrochemical Biosensor Technologies for the Detection of Nucleic Acid Breast Cancer Biomarkers.Sensors (Basel, Switzerland) · 2023Review
- A deep attention LSTM embedded aggregation network for multiple histopathological images.PloS one · 2023Article
- Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified.PloS one · 2023Article
- The prognostic value of arachidonic acid metabolism in breast cancer by integrated bioinformatics.Lipids in health and disease · 2022Article
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- Identifying the critical states and dynamic network biomarkers of cancers based on network entropy.Journal of translational medicine · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group.
methodsRegularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts.
resultsThe nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations.
conclusionsThe proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.
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