Abstract
BACKGROUND: This study aimed to infer a causal gene network associated with bone metastasis in lung cancer and to validate its reliability through experimental gene expression analysis. METHODS: Using DNA microarray data from the Gene Expression Omnibus, we analyzed samples from primary lung cancer and those with bone metastasis. Commonly expressed genes in both groups were identified, and a causal network was inferred using Bayesian network inference with Java Objects based on the Bayesian Dirichlet score. To evaluate the network, we predicted the expression changes of downstream genes following knockdown of a key upstream gene and compared these predictions with mRNA expression levels in fatty acid desaturase 1 (FADS1)-knockdown lung cancer cells. RESULTS: The genes FADS1, cardiotrophin-like cytokine factor 1 (CLCF1), chromosome 4 open reading frame 48, sushi, nidogen and EGF like domains 1, FK506-binding protein 15, and coenzyme Q10A (COQ10A) were identified as directly associated with lung cancer bone metastasis. Among them, FADS1 appeared to have a regulatory role, influencing downstream targets. Notably, CLCF1 and COQ10A showed significantly increased expression in FADS1-knockdown cells, consistent with the network's predictions. CONCLUSIONS: These findings suggest that Bayesian network analysis is a reliable machine learning approach for uncovering causal gene relationships in cancer metastasis. Furthermore, FADS1 may serve as a potential therapeutic target in lung cancer bone metastasis. The validity of this network was supported by in vitro experiments using a lung cancer cell line.