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.