# Hes1-loss promotes dysregulation of epithelial homeostasis and inflammation in a serrated adenocarcinoma model

> **NIH NIH R01** · CASE WESTERN RESERVE UNIVERSITY · 2022 · $1

## Abstract

Distinct from the conventional adenoma–carcinoma pathway, the “serrated
pathway” is a molecular pathway postulated for a subset of CRCs that develop from
some serrated precursor lesions. In addition, inflammation is a known risk factor for
CRC. However, molecular carcinogenic mechanism driving serrated lesion
transformation, which is frequently associated with chronic inflammation, remains an
important knowledge gap. Previously we found that Notch/HES1 expression is lost in
most human sessile serrated adenoma and right-sided CRC, and is a ubiquitous marker
for IBD-associated serrated lesions and CRC. In addition, Notch/Hes1-loss underlies
the gut pathology of an animal model of colitis-associated CRC featuring serrated-like
lesions. In this proposal, we will use a combination of approaches (organoid culture,
molecular and cellular signaling network analysis of human SSA and serrated CRC, and
microbiome deep gene sequencing, etc) and our unique carcinogen-free animal models
to study the mechanism by which HES1-loss disrupts epithelial homeostasis, and
defining how this process cooperates with a pro-tumorigenic cytokine milieu
represented by IL1β to promote carcinogenesis.

## Key facts

- **NIH application ID:** 10433908
- **Project number:** 5R01CA222064-05
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Lan Zhou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1
- **Award type:** 5
- **Project period:** 2018-07-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10433908

## Citation

> US National Institutes of Health, RePORTER application 10433908, Hes1-loss promotes dysregulation of epithelial homeostasis and inflammation in a serrated adenocarcinoma model (5R01CA222064-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10433908. Licensed CC0.

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