To identify key genes associated with colorectal cancer lung metastasis and explore their potential molecular mechanisms, thereby providing references for the early diagnosis and targeted therapy of colorectal cancer lung metastasis.

The GSE41258 dataset was obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed between primary colorectal cancer samples and lung metastatic samples to identify differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to investigate the biological functions of DEGs, and candidate genes were screened using a Protein-Protein Interaction (PPI) network. Subsequently, Random Forest (RF), Logistic Regression (LR), and Light Gradient Boosting Machine (LightGBM) algorithms combined with Shapley Additive Explanations (SHAP) analysis were employed to identify key genes. The Human Protein Atlas (HPA) database and Gene Set Enrichment Analysis (GSEA) were further used for validation and functional characterization.

A total of 196 differentially expressed genes (DEGs) associated with colorectal cancer lung metastasis were identified. GO and KEGG enrichment analyses revealed that these genes were mainly enriched in biological processes and signaling pathways related to extracellular matrix (ECM) remodeling, cell adhesion regulation, and energy metabolism, suggesting their potential roles in colorectal cancer lung metastasis and metastatic microenvironment remodeling. The area under the curve (AUC) values of the RF, LR, and LightGBM models in the testing set were 0.830, 0.852, and 0.942, respectively, with LightGBM showing the best predictive performance. Integrated machine learning and SHAP analyses identified ATP citrate lyase (ACLY) and aggrecan (ACAN) as the key genes associated with colorectal cancer lung metastasis. Validation using the HPA database demonstrated that ACLY and ACAN proteins were expressed at higher levels in colorectal cancer tissues than in normal tissues, further supporting their potential roles in colorectal cancer progression. GSEA revealed that the ACAN high-expression group was mainly enriched in extracellular matrix (ECM) receptor interaction and integrin-related pathways, whereas the ACLY high-expression group was primarily enriched in complement and coagulation cascades, ribosome biogenesis, and spliceosome-related pathways, suggesting potential roles for these genes in colorectal cancer lung metastasis through the regulation of tumor metabolic reprogramming and extracellular matrix remodeling.

In summary, based on transcriptomic data from the GEO database, this study identified ACLY and ACAN as candidate genes associated with colorectal cancer lung metastasis through differential expression analysis, functional enrichment analysis, PPI network analysis, and machine learning screening. The expression features of ACLY and ACAN may reflect the molecular phenotype of colorectal cancer lung metastasis and may potentially serve as auxiliary markers for histological diagnosis of lung metastasis or as candidate molecules for targeted intervention.