Recent studies have shown that miR-31 could play a potential role as diagnostic and prognostic biomarkers of several cancers including lung cancer. The aim of this study is to globally summarize the predicting targets of miR-31 and their potential function, pathways and networks, which are involved in the biological behavior of lung cancer.

We have conducted the natural language processing (NLP) analysis to identify lung cancer-related molecules in our previous work. In this study, miR-31 targets predicted by combinational computational methods. All target genes were characterized by gene ontology (GO), pathway and network analysis. In addition, miR-31 targets analysis were integrated with the results from NLP analysis, followed by hub genes interaction analysis.

We identified 27 hub genes by the final integrative analysis and suggested that miR-31 may be involved in the initiation, progression and treatment response of lung cancer through cell cycle, cytochrome P450 pathway, metabolic pathways, apoptosis, chemokine signaling pathway, MAPK signaling pathway, as well as others.

Our data may help researchers to predict the molecular mechanisms of miR-31 in the molecular mechanism of lung cancer comprehensively. Moreover, the present data indicate that the interaction of miR-31 targets may be promising candidates as biomarkers for the diagnosis, prognosis and personalized therapy of lung cancer.