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 this study is to develop a computational framework to identify potential prognostic biomarkers for lung adenocarcinoma (LUAD). Gene expression profiles obtained from three independent studies were analyzed to find differentially expressed genes (DEGs) in LUAD. Disease-specific protein-protein interaction (PPI) network was constructed among common DEGs and hub proteins were identified. Gene expression data was integrated with the human transcriptional regulatory network (TRN) to identify key regulatory elements and construct disease-specific TRN. Hub proteins that were also present in TRN of LUAD were considered as potential biomarkers and assessed by survival analysis. AURKA, CAV1, CLU, ENO1, FHL1, FHL2, LMO2, MYH11, NME1 and SFN were discovered as biomarkers for LUAD and survival analysis not only indicated their significant prognostic performance as a group but also revealed their contribution to the discrimination of risk groups. Our findings suggested that identified biomarkers could be valuable in LUAD progression and they should be considered for further experimentation.

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