ArticleBMC cancer2025
Identification of optimal biomarkers associated with distant metastasis in breast cancer using Boruta and Lasso machine learning algorithms.
Article in BMC cancer, 2025. 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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Who cites it
9 citing papers in PubMed.
- Development and internal validation of an explainable machine learning model for predicting functional disability trajectories in hospitalized older patients with chronic low back pain.BMC geriatrics · 2026Article
- Development and validation of a nomogram to assess recurrence risk in young patients with breast cancer based on preoperative serum tumor markers.Gland surgery · 2026Article
- VEGFC as a prognostic cytokine biomarker linking lymph node metastasis to immune suppression in breast cancer.European cytokine network · 2026Article
- Explainable ML for ACS culprit plaques: a multidimensional CCTA model highlighting hemodynamic increment.The international journal of cardiovascular imaging · 2026Article
- Development and validation of a simplified machine learning model based on T-SPOT.TB and routine clinical data for the diagnosis of tuberculous pleural effusion.Journal of thoracic disease · 2026Article
- Predicting sleep disorder trajectories in older patients with chronic pain: an explainable machine learning approach.BMC psychiatry · 2026Article
- An interpretable machine learning model for detecting vision-threatening diabetic retinopathy among patients with diabetic retinopathy: a web-based cross-sectional study.Frontiers in endocrinology · 2026Article
- Machine Learning and SHAP Value Interpretation for Predicting Hepatic Steatosis Using Vibration-Controlled Transient Elastography.International journal of endocrinology · 2026Article
- The role of TEAD4 gene in the Hippo signaling pathway in triple-negative breast cancer and targeted therapy strategies.BMC medical genomics · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
objectiveThe aim of this study was to identify optimal biomarkers associated with distant metastasis in patients with breast cancer from among nutritional and inflammatory indicators using the Boruta and Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithms, thereby improving the ability to identify distant metastasis.
methodsA total of 348 patients newly diagnosed with breast cancer were included, comprising 185 patients with nonmetastatic breast cancer and 163 patients with distant metastatic breast cancer. The variables were initially screened using the Boruta algorithm, followed by further optimization through LASSO regression. The selected key indicators were evaluated for their association with distant metastasis risk using multivariate logistic regression analysis and restricted cubic spline functions. Discriminative performance was assessed through ROC curve analysis.
resultsBoruta and LASSO analyses identified five important indicators: the advanced lung cancer inflammation index (ALI), systemic inflammation response index (SIRI), monocyte-to-lymphocyte ratio (MLR), albumin-to-globulin ratio (AGR), and geriatric nutritional risk index (GNRI). Multivariate logistic regression analysis revealed that an elevated SIRI and MLR were associated with an increased risk of distant metastasis in patients with breast cancer, whereas a higher ALI, AGR, and GNRI were associated with a reduced risk. ROC analysis indicated moderate predictive performance for these indicators, with AUC values of approximately 0.65.
conclusionThe ALI, SIRI, MLR, AGR, and GNRI are effective biomarkers for identifying the risk of distant metastasis in patients with breast cancer. These indicators could be incorporated into clinical practice to improve risk stratification, guide personalized treatment, and enhance patient outcomes.
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