Abstract
Purpose:
To elucidate the role of copper (Cu) metabolism in the progression of colon adenoma (CA) to colorectal cancer (CRC) and to identify potential biomarkers and therapeutic targets through comprehensive bioinformatics analysis.
Patients and methods:
Datasets associated with colon adenoma were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between CA samples and normal controls (NC) were intersected with genes related to copper metabolism (CMRGs) and DEGs between CRC and CA. Five machine-learning algorithms were employed to identify biomarkers. The degree of immune infiltration was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), and the expression profiles of these biomarkers across various cell types were further characterized using single-cell RNA sequencing (scRNA-seq). The expression levels of the identified genes were validated using quantitative polymerase chain reaction (qPCR) and data from the Human Protein Atlas (HPA) database.
Results:
Five biomarkers were identified: ZEB1, ABCA1, SLC24A3, CAV1, and FLNA. Functional enrichment analysis revealed significant pathway alterations in the low-expression groups of CAV1 (eg, phagosome pathway) and FLNA (eg, ribosome pathway). Significant differences in the infiltration abundance of macrophages and mast cells were observed between CA and NC. scRNA-seq analysis demonstrated that these biomarkers were expressed in fibroblasts, lymphocytes, goblet cells, B cells, and macrophages. The consistency of gene expression between patient samples and public datasets was confirmed through qPCR and HPA data.
Conclusion:
This study explores the role of copper metabolism in colon adenoma progression using bioinformatics. Five genes (ZEB1,ABCA1, SLC24A3, CAV1, FLNA) were identified as potential biomarkers. These genes correlate with immune infiltration and may serve as diagnostic and therapeutic targets. Further clinical validation is needed.
Keywords:
biomarkers; colon adenoma; copper metabolism; machine learning; single-cell RNA sequencing.