Identification of disease-related genes and construction of a gene co-expression database in non-alcoholic fatty liver disease - Ye, Hua; Sun, Mengxia; Su, Mingli; Chen, Dahua; Liu, Huiwei; Ma, Yanyan; Luo, Wenjing; Li, Hong; Xu, Feng (Frontiers in Genetics, 2023)
Background The mechanism of NAFLD progression remains incompletely understood. Current gene-centric analysis methods lack reproducibility in transcriptomic studies. Methods A compendium of NAFLD tissue transcriptome datasets were analyzed. Gene co-expression modules were identified in the RNA-seq dataset GSE135251. Module genes were analyzed in R gProfiler package for functional annotation. Module stability was assessed by sampling. Module reproducibility was analyzed by ModulePreservation function in WGCNA package. Analysis of variance (ANOVA) and Student’s t test were used to identify differential modules. Receiver operating characteristic (ROC) curve was used to illustrate the classification performance of modules. Connectivity Map was used to mine potential drugs for NAFLD treatment. Results Sixteen gene co-expression modules were identified in NAFLD. These modules were associated with multiple functions such as nucleus, translation, transcription factors, vesicle, immune response, mitochondrion, collagen, and sterol biosynthesis. These modules were stable and reproducible in the other 10 datasets. Two modules were positively associated with steatosis and fibrosis and were differentially expressed between nonalcoholic steatohepatitis (NASH) and nonalcoholic fatty liver (NAFL). Three modules can efficiently separate control and NAFL. Four modules can separate NAFL and NASH. Two endoplasmic reticulum related modules were both up-regulated in NAFL and NASH compared to normal control. Proportions of fibroblasts and M1 macrophages are positively correlated with fibrosis. Two hub genes AEBP1 and FDFT1 may play important roles in fibrosis and steatosis. m6A genes were strongly correlated with expression of modules. Eight candidate drugs for NAFLD treatment were proposed. Finally, an easy-to-use NAFLD gene co-expression database was developed (available at https://nafld.shinyapps.io/shiny/). Conclusion Two gene modules show good performance in stratifying the NAFLD patients. The modules and hub genes may provide targets for disease treatment.