ArticleJournal of healthcare engineering2022
Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- TGFBI in tumors and the tumor immune microenvironment: functional roles, mechanisms, and therapeutic targeting.Frontiers in immunology · 2026Review
- BNIPL is a promising biomarker of laryngeal cancer: novel insights from bioinformatics analysis and experimental validation.BMC medical genomics · 2024Article
- Establishment and verification of a predictive model for bone metastasis in patients with non-small cell lung cancer based on peripheral blood CX3CL and CCL28.American journal of cancer research · 2024Article
- Predictive and prognostic biomarkers of bone metastasis in breast cancer: current status and future directions.Cell & bioscience · 2023Review
- Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis.Journal of healthcare engineering · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study is to design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods: Dataset GSE124647 was used as a training set, while GSE16446, GSE45255, and GSE14020 were taken as validation sets. In the training cohort, the limma package in Results: A total of 1858 DEGs were obtained. The WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival (OS). While GJA1, IGFBP6, MDFI, TGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival (PFS). Kaplan-Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Cox regression analysis further revealed that GESBN models were independent prognostic predictors for OS and PFS in BC patients. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion: Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.
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