Machine learning-integrated multi-omics risk prediction for pulmonary fungal infection in COPD and lung cancer: a transcriptomic and immune profiling study.

Machine learning combined with multi-omics data predicts pulmonary fungal infection risk in COPD and lung cancer with an AUC of 0.988.

• Why it matters: Early identification of invasive pulmonary fungal infection (IPFI) is critical due to its high mortality rate and current diagnostic limitations, especially in patients with COPD and lung cancer.
• What they did: The study integrated single-cell and bulk RNA sequencing data from COPD and lung adenocarcinoma tissues, constructing five machine learning models to identify key immune-related genes predictive of IPFI.
• The result: The random forest model demonstrated outstanding accuracy, highlighting Treg infiltration, TLR4, and MMP9 as top predictors, with in vitro validation confirming the biological relevance of five antifungal immune genes.