Multi-omics differential gene regulatory network inference for lung adenocarcinoma tumor progression biomarker discovery

A systematic method was proposed to infer differential gene regulatory networks (GRNs) among lung adenocarcinoma (LUAD) samples at different stages by integrating multi-omics data to uncover significant network features and to identify tumor progression (TP) biomarker genes. The mRNA expressions, copy number variations, and DNA methylations of two independent LUAD cohorts (TCGA and SPORE) at stages I, II, and III were used, respectively. As results, the transition from normal to early onset was showed to be critical to reveal the pathogenesis of LUAD; 61 genes were identified as TP-related biomarkers, including two types of microRNAs of ABLIM2 and ZFAS1. These identified biomarkers may set light on the underlying mechanism of LUAD TP and may serve as potential drug targets for new treatments. Moreover, our study provides a general framework for TP biomarker identification for other types of cancer, which may improve the mechanism research for cancer development.

Automated summary

A systematic method was proposed to infer differential gene regulatory networks among different stages of lung adenocarcinoma samples, revealing potential mechanisms and biomarkers for tumor progression.