In this research project, we set out to uncover the biological processes that drive the development of liver cancer from fatty liver disease. For this transition, cell death is of major importance: It could be shown that liver cells can die in a "controlled" manner due to various stress factors (e.g. excessive fat storage within the liver cells). Such cellular "suicide programs" are present in all cells and can cause different reactions in the surrounding liver tissue/ cells. We focused on a specific type of cell death called necroptosis.
Using genetic mouse models and newly developed real-time imaging techniques, we were able to observe how necroptosis unfolds in a living organism. We discovered that there is a sublethal or "zombie" state of necroptosis, where cells are damaged but do not fully die. This partial cell death leads to the release of inflammatory signals that can promote liver inflammation and cancer. Whether this process is harmful or protective depends on other molecular pathways activated at the same time — especially a key inflammation regulator known as NF-kappaB. This means necroptosis can either worsen disease or help fight it, depending on the context.
From these insights, we developed a gene signature that reliably predicts patient outcomes with liver cancer. This signature has been validated in multiple patient groups and could serve as a prognostic biomarker in clinical practice in order to help to identify individuals who need more intensive monitoring or targeted treatment.
We also studied a protein called MLKL, a key necroptosis mediator. Interestingly, we found that MLKL may play a role in disease even when it’s not causing cell death — especially in fat tissue, which is closely connected to liver health. This provides a new perspective on how metabolic inflammation in fatty liver disease might be modulated independently of cell death mechanisms.
Finally, we applied artificial intelligence (AI) to analyze images of routinely obtained histological tissue sections to detect important genetic changes linked to cancer. These AI-based tools are now being adapted to assess disease risk in patients with fatty liver disease, opening up new possibilities for early diagnosis and personalized medicine.
Our research findings on the transition from fatty liver disease to liver cancer have been shared with the scientific community and the general publicity in various ways. We published the results in scientific journals and presented them at scientific symposia and invited university lectures, and we also made them accessible on our own homepage.
