Potential biomarkers for lung adenocarcinoma identified byintegrative transcriptomics analysis

Abstract

Lung cancer is one of the most occurring and death-causing cancers worldwide.Despite the progress, survival rate is still low due to the late diagnosis. The aim of thisstudy is to develop a computational framework to identify potential prognosticbiomarkers for lung adenocarcinoma (LUAD). Gene expression profiles obtained fromthree independent studies were analyzed to find differentially expressed genes (DEGs)in LUAD. Disease-specific protein-protein interaction (PPI) network was constructedamong common DEGs and hub proteins were identified. Gene expression data wasintegrated with the human transcriptional regulatory network (TRN) to identify keyregulatory elements and construct disease-specific TRN. Hub proteins that were alsopresent in TRN of LUAD were considered as potential biomarkers and assessed bysurvival analysis. AURKA, CAV1, CLU, ENO1, FHL1, FHL2, LMO2, MYH11, NME1 and SFNwere discovered as biomarkers for LUAD, and survival analysis not only indicated theirsignificant prognostic performance as a group, but also revealed their contribution tothe discrimination of risk groups. Our findings suggested that identified biomarkerscould be valuable in LUAD progression and they should be considered for furtherexperimentation.