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IL11+ fibroblasts are implicated in nonresponse to anti–TNF-α via fibrosis in inflammatory bowel disease
Wangyue Li, Wei Huang, Jiaxin Wang, Yiwen Tu, Qidi Yang, Yao Zhou, Zile Zhang, Haiming Zhuang, Yubei Gu, Duowu Zou, Yao Zhang
Wangyue Li, Wei Huang, Jiaxin Wang, Yiwen Tu, Qidi Yang, Yao Zhou, Zile Zhang, Haiming Zhuang, Yubei Gu, Duowu Zou, Yao Zhang
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Research Article Gastroenterology Inflammation

IL11+ fibroblasts are implicated in nonresponse to anti–TNF-α via fibrosis in inflammatory bowel disease

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Abstract

Inflammatory bowel disease (IBD) is frequently accompanied by intestinal fibrosis, with nonresponse to long-term anti–TNF-α therapy occurring in approximately 23%–46% of patients. Integrated analysis of single-cell and bulk RNA-seq datasets revealed an expansion of IL11+ fibroblasts in inflamed intestine and their significant enrichment in nonresponders. We further identified IL11+ fibroblasts as a central communication hub that engaged in extensive crosstalk with monocytes and may contribute to inflammatory amplification and fibrotic remodeling. Additionally, we employed machine learning approaches, including least absolute shrinkage and selection operator, support vector machines, and random forest, to derive an IL11+ fibroblast–related gene signature effectively predicting nonresponse to anti–TNF-α in validation and test cohorts. IHC further confirmed the overexpression of IL-11 in nonresponders. The signature genes we found are not only associated with immune and inflammatory responses but also with fibrosis, indicating a robust association between fibrosis and anti–TNF-α treatment failure. In summary, this study highlights the important role of IL11+ fibroblasts in orchestrating both inflammation and fibrosis and provides an applicable model for predicting nonresponse to anti–TNF-α in IBD, thereby laying the foundation for precision medicine and targeted therapeutic strategies.

Authors

Wangyue Li, Wei Huang, Jiaxin Wang, Yiwen Tu, Qidi Yang, Yao Zhou, Zile Zhang, Haiming Zhuang, Yubei Gu, Duowu Zou, Yao Zhang

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Figure 5

Development of a predictive model for nonresponse to anti–TNF-α based on IL11+ fibroblasts.

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Development of a predictive model for nonresponse to anti–TNF-α based on...
(A) Venn diagram depicting the overlap among IL11+ fibroblast DEGs, genes in stromal-M13, and nonresponse-associated genes. (B–D) Feature selection by LASSO (B), random forest (C), and SVM (D). The optimal factor sets selected by each algorithm are shown in vertical axes. (E) Venn diagram showing the intersection of factor sets identified by LASSO, random forest, and SVM, finally yielding 5 key genes. (F) Heatmap showing Pearson’s correlation coefficients among the key genes (TWIST1, PI15, TNFRSF11B, IL11, IL13RA2). Statistical significance was determined by Wilcoxon rank-sum test with Benjamini-Hochberg procedure. (G) ROC curves of TWIST1, PI15, TNFRSF11B, IL11, and IL13RA2 in predicting nonresponse to anti–TNF-α. (H) ROC curves of the nonresponse prediction models constructed by applying 13 machine learning algorithms to the key genes. Used algorithm and corresponding model AUC values are listed on the right. (I) Forest plot showing AUC values of all 13 prediction models.

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