Exploring microRNA-mediated alteration of EGFR signaling pathway in non-small cell lung cancer using an mRNA:miRNA regression model supported by target prediction databases

•We construct mRNA:miRNA regression models exploring mRNA and miRNA relationship.•R2 and LOOCV demonstrate good performances of these models.•The model is suitable for predicting the expression level of mRNA using miRNA(s).•The model is able to identify miRNA roles as tumor suppressor mirs or oncomirs.•The mode can also be used to explore the underlying mechanisms of NSCLC.

EGFR signaling pathway and microRNAs (miRNAs) are two important factors for development and treatment in non-small cell lung cancer (NSCLC). Microarray analysis enables the genome-wide expression profiling. However, the information from microarray data may not be fully deciphered through the existing approaches. In this study we present an mRNA:miRNA stepwise regression model supported by miRNA target prediction databases. This model is applied to explore the roles of miRNAs in the EGFR signaling pathway. The results show that miR-145 is positively associated with epidermal growth factor (EGF) in the pre-surgery NSCLC group and miR-199a-5p is positively associated with EGF in the post-surgery NSCLC group. Surprisingly, miR-495 is positively associated with protein tyrosine kinase 2 (PTK2) in both groups. The coefficient of determination (R2) and leave-one-out cross-validation (LOOCV) demonstrate good performance of our regression model, indicating that it can identify the miRNA roles as oncomirs and tumor suppressor mirs in NSCLC.