FGF9 Recruits β-Catenin to Increase Hepatic ECM Synthesis and Promote NASH-Driven HCC

Roles of fibroblast growth factors in the treatment of diabetes

Diabetes affects about 422 million people worldwide, causing 1.5 million deaths each year. However, the incidence of diabetes is increasing, including several types of diabetes. Type 1 diabetes (5%-10% of diabetic cases) and type 2 diabetes (90%-95% of diabetic cases) are the main types of diabetes in the clinic. Accumulating evidence shows that the fibroblast growth factor (FGF) family plays important roles in many metabolic disorders, including type 1 and type 2 diabetes. FGF consists of 23 family members (FGF-1-23) in humans. Here, we review current findings of FGFs in the treatment of diabetes and management of diabetic complications. Some FGFs (e.g., FGF-15, FGF-19, and FGF-21) have been broadly investigated in preclinical studies for the diagnosis and treatment of diabetes, and their therapeutic roles in diabetes are currently under investigation in clinical trials. Overall, the roles of FGFs in diabetes and diabetic complications are involved in numerous processes. First, FGF intervention can prevent high-fat diet-induced obesity and insulin resistance and reduce the levels of fasting blood glucose and triglycerides by regulating lipolysis in adipose tissues and hepatic glucose production. Second, modulation of FGF expression can inhibit renal and cardiac fibrosis by regulating the expression of extracellular matrix components, promote diabetic wound healing process and bone repair, and inhibit cancer cell proliferation and migration. Finally, FGFs can regulate the activation of glucose-excited neurons and the expression of thermogenic genes.

Unraveling the underlying pathogenic factors driving nonalcoholic steatohepatitis and hepatocellular carcinoma: an in-depth analysis of prognostically relevant gene signatures in hepatocellular carcinoma

BACKGROUND

Nonalcoholic steatohepatitis (NASH) is a progressive manifestation of nonalcoholic fatty liver disease (NAFLD) that can lead to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Despite the growing knowledge of NASH and HCC, the association between the two conditions remains to be fully explored. Bioinformatics has emerged as a valuable approach for identifying disease-specific feature genes, enabling advancements in disease prediction, prevention, and personalized treatment strategies.

MATERIALS AND METHODS

In this study, we utilized CellChat, copy number karyotyping of aneuploid tumors (CopyKAT), consensus Non-negative Matrix factorization (cNMF), Gene set enrichment analysis (GSEA), Gene set variation analysis (GSVA), Monocle, spatial co-localization, single sample gene set enrichment analysis (ssGSEA), Slingshot, and the Scissor algorithm to analyze the cellular and immune landscape of NASH and HCC. Through the Scissor algorithm, we identified three cell types correlating with disease phenotypic features and subsequently developed a novel clinical prediction model using univariate, LASSO, and multifactor Cox regression.

RESULTS

Our results revealed that macrophages are a significant pathological factor in the development of NASH and HCC and that the macrophage migration inhibitory factor (MIF) signaling pathway plays a crucial role in cellular crosstalk at the molecular level. We deduced three prognostic genes (YBX1, MED8, and KPNA2), demonstrating a strong diagnostic capability in both NASH and HCC.

CONCLUSION

These findings shed light on the pathological mechanisms shared between NASH and HCC, providing valuable insights for the development of novel clinical strategies.