Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Molecular biology reports
Metabolic signatures of triple-negative breast cancer
Triple-negative breast cancer (TNBC), being one of the most aggressive subtypes of breast malignancies, is characterized by poor prognosis and limited treatment options. As a leading cause of cancer-related mortality among women, TNBC poses unique clinical challenges due to the absence of effective targeted therapies. Conventional treatment strategies, including chemotherapy, often suffer from significant drawbacks such as drug resistance and intolerable side effects, underscoring an urgent need for innovative approaches to improve therapeutic outcomes. A defining hallmark of TNBC is its markedly altered cellular metabolism, which not only supports tumor growth and survival but also contributes to therapy resistance. Elucidating these metabolic alterations could provide critical insights into potential vulnerabilities that may be exploited for therapeutic intervention. This review provides a comprehensive overview of the major metabolic pathways that are dysregulated in TNBC and their relevance to disease progression and therapeutic intervention. Finally, we explore recent advances in metabolomics-driven precision medicine, highlighting the integration of artificial intelligence and machine learning approaches, covering aspects such as data processing, feature selection, and model construction that can accelerate the advancement of personalized treatment strategies for TNBC.
18 July 2026
Read appraisal →Analytical chemistry
Large Language Model-Generated Dietary Metabolite Biomarker Database Drives Deep Annotation of the Human Diet Metabolome
The annotation of dietary biomarkers is crucial for nutritional epidemiology. While untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical approach, the annotation of dietary biomarkers is hampered by the low specificity of existing public databases, which limits annotation coverage and accuracy. To address this limitation, we developed a novel database construction strategy and a dual-annotation workflow. We first employed an automated, large language model (LLM)-based text-mining pipeline to parse 7339 scientific articles and supplementary materials, creating the Dietary Metabolite Biomarker Database (DMBDB), which contains 4983 nonredundant biomarkers. The LLMs workflow demonstrated high performance, achieving an F1 score of 0.9269 for biomarker name recognition. Subsequently, two complementary annotation strategies were designed: (i) a specialized LC-MS database derived from DMBDB, incorporating predicted retention times and experimental MS/MS spectra for high-confidence matching, and (ii) a structure-guided molecular networking strategy (SGMNS) that uses DMBDB as background knowledge to annotate dietary biomarkers and their metabolites lacking spectral evidence. The framework was validated using untargeted LC-HRMS analysis of urine samples. LC-MS database directly annotated 566 metabolites, and the integration with SGMNS expanded the total number of annotations to 2078. The LLM-driven database construction combined with the dual-strategy annotation framework provides a powerful paradigm for achieving high-coverage and high-accuracy dietary metabolomics.
15 July 2026
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