Exploring the gene-level ferroptosis–matrisome molecular axis of fibrosis in MASLD: a cross-cohort analysis separating biological signal from cell composition

This deposit supports the manuscript: "Exploring the gene-level ferroptosis–matrisome molecular axis of fibrosis in MASLD: a cross-cohort analysis separating biological signal from cell composition" (Banerjee S, Charoensupa R, Vanden Berghe W). The one-paragraph summary: Ten published fibrosis gene panels are statistically indistinguishable in two fully external cohorts; all converge on the matrisome, which accompanies fibrosis without carrying its measurement. Of nine ways to represent a liver biopsy's transcriptome, only a plain gene-level score (12,766 genes, no selection, frozen harmonisation) transfers across cohorts (10-seed ρ = 0.759 ± 0.111, F2+ AUROC 0.883 ± 0.068). One fifth of its signal is cell mix, measured per patient. With cell mix removed, the fibrosis score and the ferroptosis driver arm converge, meeting at FDXR (rank 17 of 12,766; slopes positive in 3/4 cohorts). The score's top genes are detectable in blood (CD163, TREM2); the signal is not inherited genetic risk and does not predict progression. Every claim is guarded: pre-registered gates, random-set nulls, a caught-and-disclosed leakage collapse, and a 131-row audit ledger. Contents: Complete analysis pipeline (R discovery stages, Python verification/refinement programme), PACKAGE_PROVENANCE.json file-level audit manifest, and reproducibility documentation. Private OSF project archive: https://osf.io/bwykj/.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-01
DOI
https://doi.org/10.5281/zenodo.23841780
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
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article

Exploring the gene-level ferroptosis–matrisome molecular axis of fibrosis in MASLD: a cross-cohort analysis separating biological signal from cell composition

Wim Vanden Berghe, Subhadip Banerjee, Rawiwan Charoensup
2 links
Zenodo (CERN European Organization for Nuclear Research)
Liver Disease Diagnosis and Treatment
article

Exploring the gene-level ferroptosis–matrisome molecular axis of fibrosis in MASLD: a cross-cohort analysis separating biological signal from cell composition

Wim Vanden Berghe, Subhadip Banerjee, Rawiwan Charoensup
article en

Abstract

This deposit supports the manuscript: "Exploring the gene-level ferroptosis–matrisome molecular axis of fibrosis in MASLD: a cross-cohort analysis separating biological signal from cell composition" (Banerjee S, Charoensupa R, Vanden Berghe W). The one-paragraph summary: Ten published fibrosis gene panels are statistically indistinguishable in two fully external cohorts; all converge on the matrisome, which accompanies fibrosis without carrying its measurement. Of nine ways to represent a liver biopsy's transcriptome, only a plain gene-level score (12,766 genes, no selection, frozen harmonisation) transfers across cohorts (10-seed ρ = 0.759 ± 0.111, F2+ AUROC 0.883 ± 0.068). One fifth of its signal is cell mix, measured per patient. With cell mix removed, the fibrosis score and the ferroptosis driver arm converge, meeting at FDXR (rank 17 of 12,766; slopes positive in 3/4 cohorts). The score's top genes are detectable in blood (CD163, TREM2); the signal is not inherited genetic risk and does not predict progression. Every claim is guarded: pre-registered gates, random-set nulls, a caught-and-disclosed leakage collapse, and a 131-row audit ledger. Contents: Complete analysis pipeline (R discovery stages, Python verification/refinement programme), PACKAGE_PROVENANCE.json file-level audit manifest, and reproducibility documentation. Private OSF project archive: https://osf.io/bwykj/.

Zenodo (CERN European Organization for Nuclear Research)
University of Antwerp (BE), Mae Fah Luang University (TH)
Good health and well-being
Openalex Percentile: Top 12%
Liver Disease Diagnosis and Treatment
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Exploring the gene-level ferroptosis–matrisome molecular axis of fibrosis in MASLD: a cross-cohort analysis separating biological signal from cell composition — Wim Vanden Berghe, Subhadip Banerjee, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS