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FBP1 controls liver cancer evolution from senescent MASH hepatocytes

Nature volume 637, pages 461–469 (2025)Cite this article

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An Author Correction to this article was published on 31 January 2025

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Abstract

Hepatocellular carcinoma (HCC) originates from differentiated hepatocytes undergoing compensatory proliferation in livers damaged by viruses or metabolic-dysfunction-associated steatohepatitis (MASH)1. While increasing HCC risk2, MASH triggers p53-dependent hepatocyte senescence3, which we found to parallel hypernutrition-induced DNA breaks. How this tumour-suppressive response is bypassed to license oncogenic mutagenesis and enable HCC evolution was previously unclear. Here we identified the gluconeogenic enzyme fructose-1,6-bisphosphatase 1 (FBP1) as a p53 target that is elevated in senescent-like MASH hepatocytes but suppressed through promoter hypermethylation and proteasomal degradation in most human HCCs. FBP1 first declines in metabolically stressed premalignant disease-associated hepatocytes and HCC progenitor cells4,5, paralleling the protumorigenic activation of AKT and NRF2. By accelerating FBP1 and p53 degradation, AKT and NRF2 enhance the proliferation and metabolic activity of previously senescent HCC progenitors. The senescence-reversing and proliferation-supportive NRF2–FBP1–AKT–p53 metabolic switch, operative in mice and humans, also enhances the accumulation of DNA-damage-induced somatic mutations needed for MASH-to-HCC progression.

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Fig. 1: p53 dependence, FBP1 downregulation and promoter hypermethylation in human HCC.
Fig. 2: DNA-damage-induced FBP1 and p53 upregulation in MASH.
Fig. 3: FBP1 or p53 loss and AKT activation license NRASG12V tumorigenesis.
Fig. 4: NRF2-induced FBP1 degradation relieves senescence to license tumorigenesis.
Fig. 5: NRF2 and FBP1 inversely affect the hepatocyte transcriptome.
Fig. 6: Mutational signatures and HCC emergence from DNA-damaged and senescent progenitors.

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Data availability

RNA-seq and snRNA-seq data are available at the GEO under accession numbers GSE240047 and GSE266447. DNA-seq data are available at the Sequence Read Archive (SRA) database under the accession number PRJNA1002629. Proteomic data are available at MassIVE (MSV000092592). Uncropped source blots and images are available in Supplementary Fig. 10. Source data are provided with this paper.

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Acknowledgements

We thank the members of the Karin laboratory for discussions; the staff at Cell Signaling Technologies, Santa Cruz Technologies, Thermo Fisher Scientific and Med Chem Express for gifts of antibodies and reagents; and the members of the UCSD Tissue Technology Shared Resource (TTSR) at the UCSD School of Medicine for assistance and the Collaborative Center for Multiplexed Proteomics at UCSD. Research was supported by NIH grants to M.K. (R01DK120714, R01DK133448, R01CA234128, R01CA281784, P01CA281819, R01DK133448), M.C.S. (R35CA220483), T.K. (DK099205) and L.B.A. (R01ES030993, R01ES032547, R01CA269919). L.B.A. was also supported by a Packard Fellowship for Science and Engineering. R.C. and J.E.E.T.-P. were supported by NHMRC Project Grant APP1087125. R.C. is the recipient of a Cancer Council WA Postdoctoral Research Fellowship. D.D. was supported by NIH/ACTRI grants R01DK137061, R01DK133930, KL2TR001444 and the San Diego Digestive Diseases Research Center (SDDRC) (NIH grant DK120515). Y.Z. and L.G. were supported by the National Nature Science Foundation of China (82172990, 82372838) and (32470830, 92478135), respectively. This publication includes data generated at the UC San Diego Moores Cancer Center using a 10x Chromium Connect system that was purchased with funding from an NIH SIG grant (S10OD032398-01) and with support from the Moores Cancer Center support grant P30 CA023100.

Author information

Author notes

  1. These authors contributed equally: Li Gu, Yahui Zhu

Authors and Affiliations

  1. Laboratory of Gene Regulation and Signal Transduction, Departments of Pharmacology and Pathology, School of Medicine, University of California San Diego (UCSD), La Jolla, CA, USA

    Li Gu, Maiya Lee, Kosuke Watari, Breanna Bareng, Masafumi Ohira, Yuxiao Liu & Michael Karin

  2. Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China

    Li Gu

  3. Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China

    Li Gu

  4. School of Medicine, Chongqing University, Chongqing, China

    Yahui Zhu

  5. State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China

    Yahui Zhu

  6. Department of Cellular and Molecular Medicine, UCSD, La Jolla, CA, USA

    Shuvro P. Nandi & Ludmil B. Alexandrov

  7. Department of Bioengineering, UCSD, La Jolla, CA, USA

    Shuvro P. Nandi & Ludmil B. Alexandrov

  8. Moores Cancer Center, UCSD, La Jolla, CA, USA

    Shuvro P. Nandi & Ludmil B. Alexandrov

  9. Division of Endocrinology & Metabolism, UCSD, La Jolla, CA, USA

    Sadatsugu Sakane & Tatiana Kisseleva

  10. Curtin Medical School, Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia, Australia

    Rodrigo Carlessi & Janina E. E. Tirnitz-Parker

  11. Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Western Australia, Australia

    Rodrigo Carlessi & Janina E. E. Tirnitz-Parker

  12. Department of Pharmacology, UCSD, La Jolla, CA, USA

    Consuelo Sauceda & David J. Gonzalez

  13. Skaggs School of Pharmacy and Pharmaceutical Sciences, UCSD, La Jolla, CA, USA

    Consuelo Sauceda & David J. Gonzalez

  14. Department of Medicine, UCSD, La Jolla, CA, USA

    Debanjan Dhar & Souradipta Ganguly

  15. Department of Pathology, UCSD, La Jolla, CA, USA

    Mojgan Hosseini

  16. Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA

    Marcos G. Teneche & Peter D. Adams

  17. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    M. Celeste Simon

  18. Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    M. Celeste Simon

  19. Curtin Health Innovation Institute and Curtin Medical School, Curtin University, Perth, Western Australia, Australia

    Janina E. E. Tirnitz-Parker & Benjamin J. Dwyer

  20. Harry Perkins Institute of Medical Research and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, Australia

    Peter J. Leedman, Louise N. Winteringham & Alistair R. R. Forrest

  21. Department of Hepatology, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia

    Michael C. Wallace

  22. Medical Imaging Department, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia

    Jonathan Tibballs

  23. Minderoo Foundation, Perth, Western Australia, Australia

    Kim W. Carter

Authors
  1. Li Gu
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  2. Yahui Zhu
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Consortia

The Liver Cancer Collaborative

  • Janina E. E. Tirnitz-Parker
  • , Peter J. Leedman
  • , Michael C. Wallace
  • , Louise N. Winteringham
  • , Benjamin J. Dwyer
  • , Jonathan Tibballs
  • , Kim W. Carter
  •  & Alistair R. R. Forrest

Contributions

M.K., L.G. and Y.Z. conceived the project. L.G. and Y.Z. designed the study and performed most experiments. S.P.N. and L.B.A. analysed DNA-seq data. M.L., K.W., B.B., M.O., Y.L., D.D., S.G., M.G.T., P.D.A. and M.H. assisted with experiments and data analysis. S.S. and T.K. assisted with human hepatocytes and NASH samples. R.C. and J.E.E.T.-P. conducted and analysed daHep scRNA-seq analysis and performed methylome sequencing on specimens collected by the Liver Cancer Collaborative. C.S. and D.J.G. performed mouse proteomic analysis. M.C.S. provided Fbp1F/F mice and other key reagents. M.K. and L.G. wrote the manuscript., and all of the authors read and commented on the manuscript.

Corresponding authors

Correspondence to Li Gu, Yahui Zhu or Michael Karin.

Ethics declarations

Competing interests

M.K. is a founder and stockholder in Elgia Pharmaceuticals and received research support from Merck and Janssen Pharmaceuticals and holds a patent for the use of MUP-uPA mice as a NASH-HCC model. L.B.A. is a compensated consultant and has equity interest in io9 and Genome Insight; his spouse is an employee of Biotheranostics. L.B.A. is also listed as an inventor on US Patent 10,776,718 for source identification by non-negative matrix factorization. L.B.A. declares US provisional applications with the following serial numbers: 63/289,601, 63/269,033, 63/483,237, 63/366,392 and 63/367,846. L.B.A. and S.P.N. also declare US provisional applications with serial numbers 63/412,835 and 63/492,348. The other authors declare no competing interests.

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Nature thanks Peter Campbell, Lars Zender and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 FBP1, ALDOB and TP53 are downregulated in human HCC.

a, Relative staining intensities of the indicated proteins from Fig. 1a. b, Relative ALDOB amounts in NT and T tissues from the CPTAC-LIHC database. c, Relative FBP1 and ALDOB amounts in NT and T tissues from the PXD006512-LIHC database. d, Percent survival of PXD006512-LIHC patients stratified according to ALDOB expression by “best expression cut-off”. Significance determined by log-rank test. e, FBP1 expression in TCGA-LIHC with WT (n = 308) and mutant (n = 107) TP53. f, Relative FBP1 promoter methylation levels in the TCGA-LIHC database. g, Relative FBP1 promoter methylation levels in HCC patients from the TCGA database. Low and high were defined as in Methods. h-j, Relative promoter methylation levels of G6PC (h), PCK1 (i) and TP53 (j) in the TCGA-LIHC database. Data in a, b, c, e, f, h, i and j are mean ± SEM. Statistical significance determined by two-sided unpaired t-test or Mann–Whitney U test (a, b, c, e, f, h, i and j) based on data normality distribution. **P < 0.01, ***P < 0.001, ns, not significant. Box plots show center line (median), box limits (first and third quartiles) and whiskers (outer data points).

Source Data

Extended Data Fig. 2 FBP1 is transcriptionally activated by TP53.

a, Schematic of the FBP1 gene 5′ and 3′ regions, showing putative TP53 binding sites and amplicons used for ChIP-qPCR. b, Relative amounts of Fbp1, p21 and Tp53 mRNAs in primary hepatocytes from Tp53F/F and Tp53ΔHep mice. c, IB demonstrating shRNA mediated TP53 knockdown and FBP1 downregulation in human hepatocytes. d, Relative FBP1, p21CIP1 and TP53 mRNA amounts in human primary hepatocytes stably transfected with shCtrl, shTP53#1 and shTP53#2. e, f, Relative FBP1, p21CIP1 and TP53 mRNAs in HepG2 (e) and SK-HEP-1 (f) cells stably transfected with shCtrl, shTP53#1 and shTP53#2. IB showing TP53 knockdown in SK-HEP-1 cells is on the right. g-j, ChIP-qPCR probing TP53 recruitment to the Fbp1 gene in Tp53F/F and Tp53ΔHep livers (g), SK-HEP-1 (h), HepG2 (i) and Huh7 (j) cells (n = 3 BR each). k, Relative FBP1, p21CIP1and TP53 mRNAs (left, middle) and proteins (right) in NCD and HFD and CSD and HFrD fed Tp53F/F and Tp53ΔHep mice (16 weeks; n = 5 biological replicates/BR). Quantification of relative normalized protein amounts is shown below each strip. Data in b, d, e, f, g, h, i, j and k are mean ± SEM. Statistical significance determined by two-sided unpaired t-test or Mann–Whitney U test (b, g, h, i and j) and one-way ANOVA with Tukey post-hoc tests or Kruskal–Wallis test with Dunn post-hoc tests (d, e, f and k) based on data normality distribution. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, not significant.

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Extended Data Fig. 3 FBP1 and TP53 are induced in MASH and blunt diet-induced DNA damage.

a, IB analysis of the indicated proteins in livers of HFrD-fed MUP-uPA mice collected at the indicated time points. Quantification of relative normalized protein amounts is shown below each strip. b, Frozen liver sections from the indicated mice stained for 53BP1 and P-γH2AX (n = 4-5 BR). Scale bars, 10 μm. Quantification of 53BP1- and P-γH2AX- positive cells per HMF is shown underneath. c, IB analysis of the indicated liver proteins after 22 weeks of CSD or HFrD feeding. MW markers, densitometric quantification of protein ratios (HFrD/CSD) and P values are on the right. d, Representative IHC of MUP-uPA livers after 32 weeks of HFrD feeding (n = 4-5 BR). Scale bars, 50 μm. Quantification of staining intensity/HMF of indicated proteins is shown underneath. e, MUP-uPA/Fbp1F/F and MUP-uPA/Fbp1ΔHep livers at 22 weeks of CSD or HFrD. f, g Representative IHC (f) and Image J (g) quantification of MUP-uPA/Fbp1F/F and MUP-uPA/Fbp1ΔHep livers in 22 weeks after HFrD (n = 7-9 BR). Scale bars, 50 μm. Data in b, c, d and g are mean ± SEM. Statistical significance determined by one-way ANOVA with Tukey post-hoc tests or Kruskal–Wallis test with Dunn post-hoc tests (b, g) and two-sided unpaired t-test or Mann–Whitney U test (d) based on data normality distribution. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

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Extended Data Fig. 4 FBP1 and TP53 are induced in human MASH and downregulated in daHep.

a, Relative FBP1 and ALDOB expression from the GSE162694 human normal and MASH liver dataset (normal, n = 31; MASH, 0, n = 35; 1, n = 30; 2, n = 27; 3, n = 8; 4, n = 12). b, Representative IHC of human normal and MASH livers (n = 7-9 BR). Image J quantifications are to the right. c, SR staining and 53BP1 and P-γ-H2AX IHC of human normal and MASH liver tissues (n = 7-9 BR). Image J quantification is shown underneath. d, UMAP visualizations and unsupervised clustering of 78250 hepatocyte nuclei from integrated GSE185477, GSE174748, GSE192742 and GSE212046 datasets. Five hepatocyte subsets were annotated based on gene expression and liver pathology metadata (top left). FBP1 and TP53 expression shown by UMAP visualization (top right and bottom left). NRF2 pathway signatures containing n = 141 target genes in daHep and HCC are shown in the bottom right. The ___location of daHep nuclei is highlighted by blue ellipses. e, UMAP visualizations and unsupervised clustering of 12,540 mouse hepatocyte nuclei from the GSE200366 dataset. Four subsets previously identified, three representing normal hepatocyte zonation (Zone_1_Hep, Zone_2_Hep, Zone_3_Hep) and one daHep cluster. Density maps of Fbp1 and Dnmt1 mRNA expression in the UMAP space are shown with the daHep cluster highlighted by blue ellipses. Data in a, b and c are mean ± SEM. Statistical significance determined by one-way ANOVA with Tukey post-hoc tests or Kruskal–Wallis test with Dunn post-hoc tests (a) and two-sided unpaired t-test or Mann–Whitney U test (b, c) based on data normality distribution. *P < 0.05, **P < 0.01, ****P < 0.0001. Box plots show center line (median), box limits (first and third quartiles) and whiskers (outer data points).

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Extended Data Fig. 5 Fbp1 ablation promotes MASH to HCC progression.

a, Normal hepatocytes and HCC progenitor cells (HcPC) were isolated from 22 weeks HFrD fed MUP-uPA/Fbp1F/F and MUP-uPA/Fbp1ΔHep livers (n = 5). Numbers/liver and diameters of aggregates are shown underneath. Scale bars, 100 μm. b, c, IB analysis of the indicated proteins in normal hepatocytes and HcPC from a. MW markers, densitometric quantification of protein ratios (HcPC/Normal hepatocytes) and P values are depicted on the right. d, Tumour numbers (left) and volumes (right) of MUP-uPA/Fbp1F/F and MUP-uPA/Fbp1ΔHep livers after 40 weeks of HFrD (n = 5-7 BR each). e, Representative CD44, MYC, and AFP staining of liver sections from above mice. Scale bars, 50 μm. Relative staining intensities per HMF are below. f, IB analysis of liver lysates from above mice prepared after 40 weeks of HFrD. MW markers, densitometric quantification of protein ratios (MUP-uPA/Fbp1ΔHep/MUP-uPA/Fbp1F/F) and P values are shown on the right. g, Tumour numbers (left) and volumes (right) of MUP-uPA/Fbp1F/F and MUP-uPA/Fbp1ΔHep livers after 40 weeks of HFrD +/− Nutlin-3a treatment (25 mg/kg) 2x/week for 8 weeks (n = 4-5 BR). h, MUP-uPA/Fbp1F/F and MUP-uPA/Fbp1ΔHep livers were transduced with AAV8-Ctrl, AAV8-FBP1 and AAV8-FBP1E98A 6–8 weeks after HFrD initiation (n = 4-5 BR). Mice were examined at 40 weeks. Tumour numbers (left) and volumes (right) are shown. Data in a, d, e, g and h are mean ± SEM. Statistical significance determined by two-sided unpaired t-test or Mann–Whitney U test (a, d and e) and one-way ANOVA with Tukey post-hoc tests or Kruskal–Wallis test with Dunn post-hoc tests (g, h) based on data normality distribution. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

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Extended Data Fig. 6 FBP1 loss and AKT activation license NRASG12V-induced tumorigenesis.

a, Overall survival of Fbp1F/F and Fbp1ΔHep mice receiving NRASG12V HTVI (n = 6-8 each). Significance was determined by log-rank test. b, IHC analysis of Fbp1F/F and Fbp1ΔHep livers at the indicated times post-NRASG12V HTVI (n = 4-5 BR per time point). Scale bars, 50 μm. c, Relative staining intensities per HMF from b. d, IB of liver lysates 1, 4, and 12 weeks after NRASG12V HTVI of Fbp1F/F and Fbp1ΔHep mice. e, f, IHC of FFPE liver sections from indicated mice livers(e). Scale bars, 50 μm. Relative staining intensities (f). g, IB of NT and T lysates of Fbp1ΔHep mice 16 weeks after NRASG12V HTVI. h, IB of Tp53F/F and Tp53ΔHep liver lysates 1 and 4 weeks post NRASG12V HTVI. Data in c and f are mean ± SEM. Statistical significance determined by two-sided unpaired t-test or Mann–Whitney U test (c, f) based on data normality distribution. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

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Extended Data Fig. 7 FBP1 inhibits NRASG12V-induced tumorigenesis regardless of its catalytic activity.

a, Schematic of NRASG12V HTVI followed by AAV8 viral transduction. b, H&E and SR staining of liver sections from Fig. 3e (n = 4-5 BR). Scale bars, 50 μm. SR staining intensities per HMF determined by Image J are shown to the right. c, IB analysis of Fbp1F/F and Fbp1ΔHep liver lysates from Fig. 3e. d, IHC of FFPE liver sections from 3e. Scale bars, 50 μm. e, Relative staining intensities per HMF determined by Image J of d. f, ITT of mice from Fig. 3e. AUC quantifications are shown below. Data in b, e and f are mean ± SEM. Statistical significance determined by one-way ANOVA with Tukey post-hoc tests or Kruskal–Wallis test with Dunn post-hoc tests (b, e and f) based on data normality distribution. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, not significant.

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Extended Data Fig. 8 SA-β-Gal expression in primary hepatocytes and its ATM/ATR dependence.

a, b, Primary Fbp1F/F and Fbp1ΔHep hepatocytes were transduced with NRASG12V (a) or treated with etoposide (20 μM, 12 h) (b) and stained for SA-β-Gal (n = 4 BR). Scale bars, 100 μm. Image J quantification is shown on the right of a and in b. c, Image J quantification of primary hepatocytes prepared from WT mice 8 weeks post-HTVI with Ctrl or NRASG12V that were treated with ATMi (KU60019, 5 μM) or ATRi (AZD6738, 2 μM) for 6 h before SA-β-Gal staining (n = 4 BR). d, Image J quantification of primary hepatocytes prepared from MUP-uPA mice fed for 22 weeks with CSD or HFrD and treated with ATMi (KU60019, 5 μM) or ATRi (AZD6738, 2 μM) for 6 h before staining for SA-β-Gal (n = 3 BR). e, f, IB analysis of WT livers after 12 weeks HTVI of Ctrl or NRASG12V (e) and MUP-uPA mice fed with CSD and HFrD for 22 weeks (f). All mice were injected with ATMi (KU60019, 10 mg/kg) or ATRi (AZD6738, 10 mg/kg) 2x/week for 4 weeks (n = 4 BR). g, Primary hepatocytes prepared from 8 weeks old Fbp1F/F mice, were transfected with NRASG12V and after 2 days were infected with AAV-TBG-GFP and AAV-TBG-Cre (107 virus copies per well) for 2 days before staining for SA-β-Gal (n = 3 BR). Image J quantification is shown. h, Primary hepatocytes from WT mice were transfected with NRASG12V and after 2 days were treated with BpV (1 μM) and NK252 (2 μM) for 20 h before staining for SA-β-Gal (n = 3 BR). Image J quantification is shown. i, Experimental scheme. MUP-uPA/Fbp1F/F mice were fed HFrD for 22 weeks, infected with AAV-TBG-Cre and AAV-TBG-GFP and analysed 4 weeks later (n = 5 BR). j, IB analysis of liver lysates from above mice. Quantification of relative normalized protein amounts is shown below each strip. k, IHC of FFPE liver sections from above mice. Scale bars, 50 μm. Relative staining intensities per HMF determined by Image J are shown to the right. Data in a, b, c, d, g, h and k are mean ± SEM. Statistical significance determined by one-way ANOVA with Tukey post-hoc tests or Kruskal–Wallis test with Dunn post-hoc tests (a, b, c, d, g, h and k) based on data normality distribution. *P < 0.05, ***P < 0.001, ****P < 0.0001, ns, not significant.

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Extended Data Fig. 9 NRF2 activation licences NRASG12V-induced HCC development.

a-d, IHC of FFPE liver sections from Nrf2Tg/Tg and Nrf2Act-Hep livers 1, 4, 12 and 16 weeks after NRASG12 HTVI (n = 4-5 BR) (a, c). Scale bars, 50 μm. Relative staining intensities per HMF determined by Image J (b, d). e, Tumour numbers and volumes in Nrf2Tg/Tg and Nrf2Act-Hep mice 12 and 16 weeks after HTVI of NRASG12V (n = 4-5 BR). Data in b, d and e are mean ± SEM. Statistical significance determined by two-sided unpaired t-test or Mann–Whitney U test (b, d) and Kruskal–Wallis test with Dunn post-hoc tests (e) based on data normality distribution. **P < 0.01, ***P < 0.001, ****P < 0.0001.

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Extended Data Fig. 10 NRF2 and FBP1 inversely control cell senescence and proliferation genes.

a, Venn diagram showing overlap in upregulated genes in Nrf2Act-Hep vs Nrf2Tg/Tg and Fbp1ΔHep vs Fbp1F/F livers (n = 3 BR). b, Venn diagram showing overlap in downregulated genes in Nrf2Act-Hep vs Nrf2Tg/Tg and Fbp1ΔHep vs Fbp1F/F livers (n = 3 BR). c, GO BP enrichment analysis of the overlapping upregulated genes in Nrf2Act-Hep vs. Nrf2Tg/Tg and Fbp1ΔHep vs. Fbp1F/F livers 1 week after NRASG12V HTVI (n = 3 BR). Dot size corresponds to the number of genes associated with each GO BP term, and dot colour corresponds to statistical significance. d, GO BP enrichment analysis of overlapping downregulated genes in Nrf2Act-Hep vs. Nrf2Tg/Tg and Fbp1ΔHep vs. Fbp1F/F livers 1 week after NRASG12V HTVI (n = 3 BR). e, f, Venn diagrams depicting the overlap in genes that are upregulated (e) or downregulated (f) in Fbp1ΔHep vs. Fbp1F/F, Nrf2Act-Hep vs. Nrf2Tg/Tg and Tp53ΔHep vs. Tp53F/F NCD livers. g, h, Venn diagrams depicting the overlap in genes that are upregulated (g) or downregulated (h) in Fbp1ΔHep vs. Fbp1F/F, Nrf2Act-Hep vs. Nrf2Tg/Tg 1 week after NRASG12V HTVI and Tp53ΔHep vs. Tp53F/F HFD-fed livers. i, j, Heatmaps representing cell-senescence- (i) and cell-cycle- (j) associated genes in Nrf2Act-Hep livers 3 weeks after NRASG12V HTVI followed by AAV8-Ctrl and AAV8-FBP1 transduction (n = 3 BR).

Extended Data Fig. 11 NRF2 activation and FBP1 loss enable propagation of NRAS induced mutations.

a, INDEL types in HCCs from MUP-uPA/Fbp1ΔHep mice fed HFrD or HFD for 40 weeks. b, Mutational signatures displayed based on trinucleotide frequency of HFD-induced MUP-uPA/Fbp1ΔHep HCCs (1 tumour per run). c, d Duplex sequencing showing the number of mutations (c) and INDELs (d) in each signature in the indicated mice. NT- non-tumour, T-tumour. e, Number of mutations in each INDEL signature expressed as somatic mutations per megabase in HCCs from NRASG12V transduced Nrf2Act-Hep livers. f, g, Number of mutations and INDEL types in HCCs of NRASG12V transduced Nrf2Act-Hep livers for 16 weeks.

Extended Data Fig. 12 HCC emerges from senescent progenitors.

a, Schematic representation of the p21-Cre construct. The diagram was created using BioRender. b, Relative Cre and p21CIP1 mRNA amounts in primary hepatocytes transfected with the indicated vectors and treated +/− etoposide (20 μM, 48 h) (n = 3 BR). c, Schematic of experimental protocol. Mice were transduced with the indicated AAV8 vectors 2 weeks (day -14) before NRASG12V HTVI. Two weeks later, the mice were i.p. injected with PTENi (BpV) (0.5 mg/kg) or Ctrl for 8 weeks and were sacked at 16 weeks post HTVI (n = 3 BR) for HCC analysis. d, IB analysis of the indicated proteins in liver lysates from above mice. Quantification of relative normalized protein amounts is shown underneath each strip. e, f H&E and IHC of FFPE liver sections from above mice. Scale bars, 20 μm. Relative staining intensities per HMF were determined by Image J are shown to the right. g, Tumour numbers (top) and volumes (bottom) in mT/mG livers transduced with AAV8-Ctrl, AAV8-TBG-Cre or AAV8-p21-Cre 2 wks prior to NRASG12 HTVI, followed by BpV (HOpic, 0.5 mg/kg) or Ctrl i.p. injections 2x/wk for 8 wks and analysis at 16 wks post-HTVI (n = 3 each). Data in b, e, f and g are mean ± SEM. Statistical significance determined by two-sided unpaired t-test or Mann–Whitney U test (e, f and g) and Kruskal–Wallis test with Dunn post-hoc tests (b) based on data normality distribution. **P < 0.01, ***P < 0.001, ****P < 0.0001.

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Gu, L., Zhu, Y., Nandi, S.P. et al. FBP1 controls liver cancer evolution from senescent MASH hepatocytes. Nature 637, 461–469 (2025). https://doi.org/10.1038/s41586-024-08317-9

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