Systems-level analysis identifies IRF6 as an inhibitor of epithelial-mesenchymal transition

2026 · doi:10.64898/2026.01.31.702311
preprint OA: closed
Full text JSON View at publisher
⚙ AI-generated summary by claude@2026-07, 2026-07-14 ⓘ

This study integrated transcriptomic data and modeling to identify IRF6 as an inhibitor of epithelial-mesenchymal transition, observing its downregulation during EMT and correlation with worse patient survival.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by claude@2026-07, 2026-07-04 · read from full text ⓘ

The paper studied epithelial–mesenchymal transition (EMT) and mesenchymal–epithelial transition (MET) by integrating bulk and spatial transcriptomic analyses from cell lines and patient samples, alongside mechanism-based dynamical modeling, to identify transcription factors that stabilize an epithelial phenotype. It reports that IRF6 strongly associates with epithelial traits and is often inhibited during EMT, and that IRF6 knock-down in multiple cancer cell lines drives progression toward a mesenchymal phenotype. A stated caveat is that conclusions are supported by correlations and in vitro perturbations combined with modeling, and not by direct in vivo lineage or mechanistic proof of all downstream targets. Relevance to endometriosis: the paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Background Epithelial-mesenchymal transition (EMT) and its reverse process Mesenchymal-Epithelial Transition (MET) are crucial during metastasis and therapy resistance. While the dynamics and master regulators of EMT are well-studied, the transcription factors that can prevent EMT or promote MET are relatively less understood. Results Here, by integrating bulk and spatial transcriptomic data analysis from cell lines and patient samples, with mechanism-based dynamical modelling, we identify IRF6 as a factor that strongly associates with an epithelial phenotype and is often inhibited during EMT. In vitro experiments in multiple cancer cell lines demonstrate the progression to a mesenchymal phenotype upon IRF6 knock-down, suggesting a role as an inhibitor of EMT. Finally, we observe that IRF6 expression levels correlates with worse patient survival in a subset of solid tumour types. Conclusion Our integrated computational-experimental systems-level analysis suggests that IRF6 is frequently downregulated during EMT and can also prevent the progression towards a complete EMT, underscoring its role as an MET stabilizing factor.
Full text 1,226 characters · extracted from oa-doi-fallback · 3 sections · click to expand

Abstract

Background Epithelial-mesenchymal transition (EMT) and its reverse process Mesenchymal-Epithelial Transition (MET) are crucial during metastasis and therapy resistance. While the dynamics and master regulators of EMT are well-studied, the transcription factors that can prevent EMT or promote MET are relatively less understood.

Results

Here, by integrating bulk and spatial transcriptomic data analysis from cell lines and patient samples, with mechanism-based dynamical modelling, we identify IRF6 as a factor that strongly associates with an epithelial phenotype and is often inhibited during EMT. In vitro experiments in multiple cancer cell lines demonstrate the progression to a mesenchymal phenotype upon IRF6 knock-down, suggesting a role as an inhibitor of EMT. Finally, we observe that IRF6 expression levels correlates with worse patient survival in a subset of solid tumour types.

Conclusion

Our integrated computational-experimental systems-level analysis suggests that IRF6 is frequently downregulated during EMT and can also prevent the progression towards a complete EMT, underscoring its role as an MET stabilizing factor. Competing Interest Statement The authors have declared no competing interest.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2026) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00