Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma - Zhou, You; Xu, Bin; Zhou, Yi; Liu, Jian; Zheng, Xiao; Liu, Yingting; Deng, Haifeng; Liu, Ming; Ren, Xiubao; Xia, Jianchuan; Kong, Xiangyin; Huang, Tao; et al. (Frontiers in Cell and Developmental Biology, 2021)
Background: With the advent of large-scale molecular profiling, an increasing number of oncogenic drivers contributing to precise medicine and reshaping classification of lung adenocarcinoma (LUAD) has been identified. However, only a minority of patients archived improved outcome under current standard therapies because of dynamic mutational spectrum, which required expanding susceptible gene libraries. Accumulating evidence has witnessed that understanding gene regulatory networks as well as their changing processes were helpful in identifying core genes which acted as master regulators during carcinogenesis. The present study aimed at identifying key genes with differential correlations between normal and tumor status. Methods: Weighted gene co-expression network analysis (WGCNA) was employed to build gene interaction network using expression profile of LUAD from The Cancer Genome Atlas (TCGA). R package DiffCorr was implemented for the identification of differential correlations between tumor and adjacent normal tissues. STRING and Cytoscape was used for the construction and visualization of biological networks. Results: A total of 176 modules were detected in the network, among which yellow and mediumorchid modules showed the most significant associations with LUAD. Then genes in these two modules were further chosen to evaluate their differential correlations. Finally, dozens of novel genes with opposite correlations including ATP13A4-AS1, HIGD1B, DAP3 and ISG20L2, were identified. Further biological and survival analysis highlighted their potential values in diagnosis and treatment of LUAD. Moreover, real-time qPCR confirmed expression patterns of ATP13A4-AS1, HIGD1B, DAP3 and ISG20L2 in LUAD tissues and cell lines. Conclusion: Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering network-based algorithm in the application of tumor etiology.