Weighted gene co-expression network analysis identified GBP2 connected to PPARα activity and liver cancer

• Published in: Scientific reports Vol. 14; no. 1; pp. 20745 - 8
• Main Authors: AmeliMojarad, Mandana, AmeliMojarad, Melika, Cui, Xiaonan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/67; Agonists; CD40 antigen; CXCL10 protein; GBP2; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genes; Homeostasis; Humanities and Social Sciences; Humans; Liver cancer; Liver Neoplasms - genetics; Liver Neoplasms - metabolism; Molecular modelling; multidisciplinary; Peroxisome proliferator-activated receptors; PPAR alpha - genetics; PPAR alpha - metabolism; PPARα; Protein Interaction Maps; Pyrimidines; Science; Science (multidisciplinary); Tumorigenesis; WGCNA; Liver cancer; GBP2; PPARα; WGCNA
• DOI: 10.1038/s41598-024-70832-6
• ISSN: 2045-2322

Liver cancer is the fourth leading cause of cancer-related deaths with a steadily increasing rate worldwide, as a well-known hallmark of liver cancer, metabolic alterations are related to liposomal changes, a common characteristic of primary liver cancers based on recent lipidomics studies. Peroxisome proliferator-activated receptor α (PPARα) is a ligand-activated transcription factor with important lipid homeostasis function, therefore we aimed to understand the molecular mechanisms and pathways that activate PPARα after using PPAR-α agonist WY-14643 and identify candidate biomarkers related to PPARα activity and evaluate their effects in liver cancer. The data from differently expressed genes (DEGs) between liver cancer tissue from obese subjects alone and liver tissue after treatment were evaluated by DESeq2 and module genes were analyzed using weighted gene co-expression network analysis (WGCNA). Final candidate genes were identified by intersecting genes among highly ranked DEGs and the brown module, which demonstrated a significant negative correlation with drug treatments. We conducted a protein–protein interaction network, and KEGG enrichment analysis, and core hub genes (CD40, CXCL9, CXCL10, TNFSF14, GBP2, GBP3, APOL3, CLDN1) were identified using the cyto-hubba plugin, among them we focused on GBP2 that plays key roles in oncogenesis and evaluate its expressional with clinical outcomes. In conclusion, the WGCNA-based co-expression network identified GBP2 as one of the hub genes with a negative relation with PPARα agonist treatments. higher expression of GBP2 was closely associated with HCC progression. Therefore, GBP2 might be a potential candidate for the study of PPARα activity in HCC.