Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology

Tao Huang¹  Xiangyin Kong²  Xiubao Ren³  Yingting Liu⁴  You Zhou⁴  Yi Zhou⁴  Jian Liu⁴  Jingting Jiang⁴  Bin Xu⁴  Ming Liu⁴  Xiao Zheng⁴  Haifeng Deng⁴  Jianchuan Xia⁶ 

[1] Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China;CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China;Department of Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China;Institute of Cell Therapy, Soochow University, Changzhou, China;Jiangsu Engineering Research Center for Tumor Immunotherapy, Changzhou, China;State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China;Tumor Biological Diagnosis and Treatment Center, The Third Affiliated Hospital of Soochow University, Changzhou, China;

关键词: WGCNA;    differential correlation;    switching mechanism;    gene regulation;    lung adenocarcinoma;   

DOI  :  10.3389/fcell.2021.675438

来源: DOAJ

【 摘 要 】

BackgroundWith the advent of large-scale molecular profiling, an increasing number of oncogenic drivers contributing to precise medicine and reshaping classification of lung adenocarcinoma (LUAD) have been identified. However, only a minority of patients archived improved outcome under current standard therapies because of the dynamic mutational spectrum, which required expanding susceptible gene libraries. Accumulating evidence has witnessed that understanding gene regulatory networks as well as their changing processes was 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.MethodsWeighted gene co-expression network analysis (WGCNA) was employed to build a gene interaction network using the 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 were used for the construction and visualization of biological networks.ResultsA total of 176 modules were detected in the network, among which yellow and medium orchid 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 analyses highlighted their potential values in the diagnosis and treatment of LUAD. Moreover, real-time qPCR confirmed the expression patterns of ATP13A4-AS1, HIGD1B, DAP3, and ISG20L2 in LUAD tissues and cell lines.ConclusionOur study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.

【 授权许可】

Unknown