Lung Adenocarcinoma is one of the most leading causes of death worldwide. Early detection of this cancer could enhance the survival chance of patients and even lead to better and more effective treatment. One of the approaches to find out more about biological malfunctions is using "omics" data. Among diverse computational procedures, data integration is becoming a striking tool to deal with complicated diseases such as cancer, considering the defective and informative nature of each kind of "omics" data. Data integration as relates to lung adenocarcinoma can lead to finding molecular biomarkers that could solve early-stage detection and progression prediction alongside other screening technologies like low-dose spiral computed tomography. In the present study, we hypothesized that genes with multiple variations are essential to provoke lung adenocarcinoma and one may use them to predict tumor formation or even cancer development. We integrated the genomic, epigenomic, transcriptomic and proteomic data. Consequently, five genes were introduced and validated by different analyses including classification of patients and survival analysis. Furthermore, we constructed a bipartite mRNA-miRNA network to identify a set of miRNAs for further experimental analyses. Finally, a sensitive and specific diagnostic panel comprising CDKN2A, CX3CR1, COX4I2, SLC15A2 and TFRC genes were identified for early detection of Lung Adenocarcinoma.
Keywords: Bipartite mRNA-miRNA bipartite network; Diagnostic panel; Integrative analysis; Lung adenocarcinoma; Multi-omics data; Prognostic panel.
Copyright © 2019 Elsevier Inc. All rights reserved.