Metabolites. 2025 Aug 1;15(8):514. doi: 10.3390/metabo15080514.

ABSTRACT

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies-including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks-are advancing cancer research. Specifically, we highlight three major applications of ML-metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies.

PMID:40863133 | PMC:PMC12388062 | DOI:10.3390/metabo15080514

Medicine (Baltimore). 2025 Aug 22;104(34):e44050. doi: 10.1097/MD.0000000000044050.

ABSTRACT

Combined pulmonary fibrosis and emphysema (CPFE) and lung cancer cast intertwined shadows, yet the molecular nexus binding them remains largely obscured. By integrating high-resolution transcriptomic landscapes, extensive genome-wide association resources, and a stratified Mendelian randomization (MR) framework, we distilled 809 differentially expressed genes and, in successive steps, confirmed their causal ties to squamous cell carcinoma, adenocarcinoma, and small cell lung cancer. The credibility of these associations was bolstered through 3 sequential validation tiers - eQTL-anchored MR, eQTL-anchored SMR, and pQTL-anchored MR analyses - each reinforcing the robustness of the signals. Within this constellation, CPPED1 emerged as a watchful sentinel that mitigates risk in squamous carcinoma, whereas CD300LF proved a formidable oncogenic catalyst in the small cell lineage. Collectively, these insights illuminate the heritable circuitry linking CPFE and lung cancer, chart avenues for proactive surveillance and precision therapeutics in vulnerable patients, and enrich the conceptual framework of the fibrosis-to-carcinoma transition, inviting deeper multi-omic synthesis and incisive mechanistic exploration.

PMID:40859573 | PMC:PMC12385043 | DOI:10.1097/MD.0000000000044050