Background/Aim Transforming growth factor-beta (TGF-β) has a dichotomous role, functioning as a tumor suppressor and tumor promoter. TGF-β signatures, explored in mouse hepatocytes, have been reported to predict the clinical outcomes of hepatocellular carcinoma (HCC) patients; HCCs exhibiting early TGF-β signatures showed a better prognosis than those with late TGF-β signatures. The expression status of early and late TGF-β signatures remains unclear in defined lesions of human B-viral multistep hepatocarcinogenesis.
Methods The expression of TGF-β signatures, early and late responsive signatures of TGF-β were investigated and analyzed for their correlation in cirrhosis, low-grade dysplastic nodules (DNs), high-grade DNs, early HCCs and progressed HCCs (pHCCs) by real-time PCR and immunohistochemistry.
Results The expression levels of TGF-β signaling genes (TGFB1, TGFBR1, TGFBR2 and SMAD4) gradually increased with the progression of hepatocarcinogenesis, peaking in pHCCs. The expression of early responsive genes of TGF-β (GADD45B, FBP1, CYP1A2 and CYP3A4) gradually decreased, and that of the late TGF-β signatures (TWIST and SNAI1) significantly increased according to the progression of multistep hepatocarcinogenesis. Furthermore, mRNA levels of TWIST and SNAI1 were well correlated with those of stemness markers, with upregulation of TGF-β signaling, whereas FBP1 expression was inversely correlated with that of stemness markers.
Conclusions The enrichment of the late responsive signatures of TGF-β with induction of stemness is considered to be involved in the progression of the late stage of multistep hepatocarcinogenesis, whereas the early responsive signatures of TGF-β are suggested to have tumor-suppressive roles in precancerous lesions of the early stage of multistep hepatocarcinogenesis.
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Structure, unique biological properties, and mechanisms of action of transforming growth factor β Nataliya Zelisko, Roman Lesyk, Rostyslav Stoika Bioorganic Chemistry.2024; 150: 107611. CrossRef
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histopathological and genomic data would be a powerful approach to delineate the tumor heterogeneity.