Lead DEAD/H box helicase biomarkers with the therapeutic potential identified by integrated bioinformatic approaches in lung cancer

DEAD/H box helicases are implicated in lung cancer but have not been systematically investigated for their clinical significance and function. In this study, we aimed to evaluate the potential of DEAD/H box helicases as prognostic biomarkers and therapeutic targets in lung cancer by integrated bioinformatic analysis of multivariate large-scale databases. Survival and differential expression analysis of these helicases enabled us to identify four biomarkers with the most significant alterations. These were found to be the negative prognostic factors DDX11, DDX55 and DDX56, and positive prognostic factor DDX5. Pathway enrichment analysis indicates that MYC signalling is negatively associated with expression levels of the DDX5 gene while positively associated with that of DDX11, DDX55 and DDX56. High expression levels of the DDX5 gene is associated with low mutation levels of TP53 and MUC16, the two most frequently mutated genes in lung cancer. In contrast, high expression levels of DDX11, DDX55 and DDX56 genes are associated with high levels of TP53 and MUC16 mutation. The tumour-infiltrated CD8 + T and B cells positively correlate with levels of DDX5 gene expression, while negatively correlate with that of the other three DEAD box helicases, respectively. Moreover, the DDX5-associated miRNA profile is distinguished from the miRNA profiles of DDX11, DDX55 and DDX56, although each DDX has a different miRNA signature. The identification of these four DDX helicases as biomarkers will be valuable for prognostic prediction and targeted therapeutic development in lung cancer. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

Automated summary

This study evaluated the potential of DEAD/H box helicases as prognostic biomarkers and therapeutic targets in lung cancer through integrated bioinformatic analysis. Four biomarkers with significant alterations were identified, providing valuable insights for prognostic prediction and targeted therapeutic development in lung cancer.