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Introducing a panel for early detection of lung adenocarcinoma by using data integration of genomics, epigenomics, transcriptomics and proteomics.

Abstract：

: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.

journal_name

Exp Mol Pathol

journal_title

Experimental and molecular pathology

authors

Haghjoo N,Moeini A,Masoudi-Nejad A

doi

10.1016/j.yexmp.2019.104360

subject

Has Abstract

pub_date

2020-02-01 00:00:00

pages

104360

eissn

0014-4800

issn

1096-0945

pii

S0014-4800(19)30177-7

journal_volume

112

pub_type

杂志文章

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