ArticlePloS one2020
Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 12 citations in OpenAlex.
- Spatiotemporal regulation of DNA repair proteins between Golgi and nucleus maintains genome stability.The Journal of cell biology · 2026Article
- Machine learning-based prediction of survival in breast cancer patients with lung metastasis: a SEER-based study.Translational cancer research · 2026Article
- Clinical and Metabolic Factors Associated with Comorbidities of Thyroid and Breast Diseases in Women Aged 25-55 Years.International journal of women's health · 2026Article
- Nomogram for predicting the risk and prognosis of lung metastasis of four subtypes of breast cancer: A population-based study from SEER.Cancer pathogenesis and therapy · 2025Article
- Alternative splicing factor RAB3IP as a novel risk signature to predict the prognosis of colorectal cancer.Journal of Cancer · 2025Article
- Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.BMC women's health · 2024Article
- AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter.Scientific reports · 2024Article
- Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study.Frontiers in oncology · 2024Article
- Anterior Gradient 2 is a Significant Prognostic Biomarker in Bone Metastasis of Breast Cancer.Pathology oncology research : POR · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes. The best genes for modeling were selected using a robust likelihood-based survival modeling approach: GOLGB1, TMEM158, CXCL8, MCM5, HIF1AN, and TSPAN31. A prognostic nomogram for predicting lung metastasis in breast cancer was developed based on these six genes. The effectiveness of the nomogram was evaluated in the training set GSE5327 and the validation set GSE2603. Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients. On the other hand, in the validation set GSE2603, we found that neither the six genes in the nomogram nor the risk predicted by the nomogram were associated with bone metastasis of breast cancer, preliminarily suggesting that these genes and nomogram were specifically associated with lung metastasis of breast cancer. What's more, five genes in the nomogram were significantly differentially expressed between breast cancer and normal breast tissues in the TIMER database. In conclusion, we constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients. In addition, some of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer. The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs.
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