ArticleJournal of translational medicine2022
XGBoost-based and tumor-immune characterized gene signature for the prediction of metastatic status in breast cancer.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed, 91 citations in OpenAlex.
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- Interpretable and integrative analysis of single-cell multiomics with scMKL.Communications biology · 2025Article
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- Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization.Scientific reports · 2025Article
- Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling.BMC gastroenterology · 2025Article
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- Diagnostic accuracy of automated breast volume scanning, hand-held ultrasound and molybdenum-target mammography for breast lesions: a systematic review and meta-analysis.Gland surgery · 2025Article
- Development and validation of an ultrasound-based interpretable machine learning model for the classification of ≤3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic study.EClinicalMedicine · 2025Article
- COMPREHENSIVE CHARACTERIZATION OF CYTOKINES IN PATIENTS UNDER EXTRACORPOREAL MEMBRANE OXYGENATION: EVIDENCE FROM INTEGRATED BULK AND SINGLE-CELL RNA SEQUENCING DATA USING MULTIPLE MACHINE LEARNING APPROACHES.Shock (Augusta, Ga.) · 2025Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor a long time, breast cancer has been a leading cancer diagnosed in women worldwide, and approximately 90% of cancer-related deaths are caused by metastasis. For this reason, finding new biomarkers related to metastasis is an urgent task to predict the metastatic status of breast cancer and provide new therapeutic targets.
methodsIn this research, an efficient model of eXtreme Gradient Boosting (XGBoost) optimized by a grid search algorithm is established to realize auxiliary identification of metastatic breast tumors based on gene expression. Estimated by ten-fold cross-validation, the optimized XGBoost classifier can achieve an overall higher mean AUC of 0.82 compared to other classifiers such as DT, SVM, KNN, LR, and RF.
resultsA novel 6-gene signature (SQSTM1, GDF9, LINC01125, PTGS2, GVINP1, and TMEM64) was selected by feature importance ranking and a series of in vitro experiments were conducted to verify the potential role of each biomarker. In general, the effects of SQSTM in tumor cells are assigned as a risk factor, while the effects of the other 5 genes (GDF9, LINC01125, PTGS2, GVINP1, and TMEM64) in immune cells are assigned as protective factors.
conclusionsOur findings will allow for a more accurate prediction of the metastatic status of breast cancer and will benefit the mining of breast cancer metastasis-related biomarkers.
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