Network Modeling of TGFβ Signaling in Hepatocellular Carcinoma Epithelial-to-Mesenchymal Transition Reveals Joint Sonic Hedgehog and Wnt Pathway Activation

TLDR

Epithelial‑to‑mesenchymal transition (EMT) is coopted by cancer cells to invade and metastasize, characterized by loss of E‑cadherin and driven in hepatocellular carcinoma by dysregulated TGFβ. The study aims to use the model to identify therapeutic targets that suppress EMT, invasion, and metastasis in hepatocellular carcinoma. The authors built a 70‑node, 135‑edge EMT network integrating developmental and HCC pathways and applied discrete dynamic modeling to analyze TGFβ‑driven dynamics. The model reproduces known EMT dysregulations, predicts concurrent activation of Wnt and Sonic hedgehog pathways, and experimental validation in murine and human HCC lines confirms TGFβ as a conserved driver while revealing eight feedback motifs that stabilize EMT through cross‑talk.

Abstract

Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is TGFβ, which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We have constructed an EMT network of 70 nodes and 135 edges by integrating the signaling pathways involved in developmental EMT and known dysregulations in invasive HCC. We then used discrete dynamic modeling to understand the dynamics of the EMT network driven by TGFβ. Our network model recapitulates known dysregulations during the induction of EMT and predicts the activation of the Wnt and Sonic hedgehog (SHH) signaling pathways during this process. We show, across multiple murine (P2E and P2M) and human HCC cell lines (Huh7, PLC/PRF/5, HLE, and HLF), that the TGFβ signaling axis is a conserved driver of mesenchymal phenotype HCC and confirm that Wnt and SHH signaling are induced in these cell lines. Furthermore, we identify by network analysis eight regulatory feedback motifs that stabilize the EMT process and show that these motifs involve cross-talk among multiple major pathways. Our model will be useful in identifying potential therapeutic targets for the suppression of EMT, invasion, and metastasis in HCC.