ArticleNeuro-oncology2023
A novel risk signature for predicting brain metastasis in patients with lung adenocarcinoma.
Article in Neuro-oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Analysis of prognostic factors and risk prediction in brain metastases: a SEER population-based study.Frontiers in oncology · 2025Pooled it
- Focused ultrasound for brain metastases.Neuro-oncology advances · 2026Review
- Downregulated lysyl oxidase in plasma extracellular vesicles: a biomarker linked to brain metastasis risk in lung adenocarcinoma.BMC medicine · 2026Article
- Mitochondrial DNA methylation predicts immunotherapy response and prognosis in lung adenocarcinoma: evidence from scRNA-Seq and machine learning.BMC cancer · 2026Article
- Single-cell and spatial transcriptomics reveal a potential role of ATF3 in brain metastasis of lung adenocarcinoma.Translational lung cancer research · 2025Article
- Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma.BMC cancer · 2025Article
- Article
- Review
- ITGB4 is a prognostic biomarker and correlated with lung adenocarcinoma brain metastasis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Article
- The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung.BMC cancer · 2024Article
- Prognostic nomogram models for predicting survival probability in elderly glioblastoma patients.Journal of cancer research and clinical oncology · 2023Article
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundBrain metastasis (BM) are a devastating consequence of lung cancer. This study was aimed to screen risk factors for predicting BM.
methodsUsing an in vivo BM preclinical model, we established a series of lung adenocarcinoma (LUAD) cell subpopulations with different metastatic ability. Quantitative proteomics analysis was used to screen and identify the differential protein expressing map among subpopulation cells. Q-PCR and Western-blot were used to validate the differential proteins in vitro. The candidate proteins were measured in LUAD tissue samples (n = 81) and validated in an independent TMA cohort (n = 64). A nomogram establishment was undertaken by performing multivariate logistic regression analysis.
resultsThe quantitative proteomics analysis, qPCR and Western blot assay implied a five-gene signature that might be key proteins associated with BM. In multivariate analysis, the occurrence of BM was associated with age ≤ 65 years, high expressions of NES and ALDH6A1. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI, 0.881-0.988) in the training set. The validation set showed a good discrimination with an AUC of 0.719 (95% CI, 0.595-0.843).
conclusionsWe have established a tool that is able to predict occurrence of BM in LUAD patients. Our model based on both clinical information and protein biomarkers will help to screen patient in high-risk population of BM, so as to facilitate preventive intervention in this part of the population.
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