Transcriptomic and single-cell sequencing identify lipotoxicity-related COPD biomarkers APRT and S100A8, validated by RT-qPCR and a nomogram

The study identifies APRT and S100A8 as candidate lipotoxicity-related biomarkers in COPD, with APRT notably lower and S100A8 notably higher in COPD samples. The precise involvement of lipotoxicity-related genes in COPD was previously unclear; this work links two specific genes to lipotoxicity in COPD with opposite directions of expression change. Based on differential expression analysis, machine learning, and gene expression analysis of COPD-related datasets from a public database, further supported by RT-qPCR analysis.

A nomogram based on the two biomarkers was developed and validated, with the calibration curve indicating its ability to predict COPD risk (P = 0.395 in the Hosmer-Lemeshow test). Moves the biomarkers from association toward a risk-prediction model form with a calibration assessment. The nomogram was evaluated with a calibration curve, with P = 0.395 in the Hosmer-Lemeshow test.

Both biomarkers were significantly co-enriched in the "focal adhesion" pathway and both showed a significant correlation with neutrophils; immune infiltration analysis identified 3 immune cell types with notably differential infiltration. Provides a shared pathway-level mechanistic clue for the biomarkers and links them to specific immune cell populations. Results of functional enrichment analysis and immune infiltration analysis based on the analyzed datasets.

Drug prediction suggested 27 drugs targeting APRT (such as alteplase and relaxin) and methotrexate targeting S100A8; scRNA-seq identified macrophages as a key cell type in COPD and showed dynamic expression patterns during macrophage differentiation, with APRT first increasing, then decreasing, and increasing again, while S100A8 initially rises and then declines. Extends the biomarkers from expression association to potential intervention drugs and to single-cell-level cell type and dynamic expression, offering leads for targeted therapeutic strategies. Based on drug prediction analysis and single-cell RNA sequencing analysis, representing computational prediction and single-cell expression observation.

This study addresses the research setting of COPD lipotoxicity mechanisms and biomarker discovery, suited to researchers seeking candidate biomarkers, pathways, and candidate drugs from public transcriptomic data. It provides testable candidates (APRT, S100A8), the shared "focal adhesion" pathway enrichment, the correlation with neutrophils, 3 immune cell types with differential infiltration, 27 candidate drugs targeting APRT and methotrexate targeting S100A8, and macrophages as a key cell type with dynamic expression patterns of both biomarkers during macrophage differentiation. These results can inform subsequent mechanistic validation, immune cell function studies, and drug screening directions.

The loaded text is an incomplete version, lacking figures, specific dataset information, sample sizes, machine learning model details, and statistical thresholds, so the specific parameters and robustness of each analysis step cannot be assessed. The conclusion that APRT and S100A8 are lipotoxicity-related COPD biomarkers, their relationships with neutrophils and macrophages, and the drug prediction results still require further validation in independent cohorts and experimental systems; the nomogram's predictive ability also needs testing in more populations.