Core liver homeostatic co-expression networks are preserved but respond to perturbations in an organism and disease specific manner

Summary:
We used WGCNA and module preservation analyse on liver transcriptomic data of mice (GSE148080), human MAFLD/MeSH (GSE126848), and human liver cancer (hepatocellular carcinoma [HCC]). The liver cancer dataset includes:
A) TCGA, LIHC_US; B) ICGC datasets (LIRI_JP, LICA_FR).
We defined a core homeostatic system within the liver that are preserved between all these models. We found module-eigengene QTLs for some of the modules that were predictive of patients survival. The framework presented in the original paper (Core liver homeostatic co-expression networks are preserved but respond to perturbations in an organism and disease specific manner) can be used to understand homeostasis at systems levels in pre-clinical models and in humans.