解读肾癌免疫治疗中GSN+炎性癌相关成纤维细胞的免疫调节功能:来自泛癌单细胞景观和空间转录组学分析的见解。

IF 5.9 1区 生物学 Q2 CELL BIOLOGY
Shan Li, Xinwei Zhou, Haoqian Feng, Kangbo Huang, Minyu Chen, Mingjie Lin, Hansen Lin, Zebing Deng, Yuhang Chen, Wuyuan Liao, Zhengkun Zhang, Jinwei Chen, Bohong Guan, Tian Su, Zihao Feng, Guannan Shu, Anze Yu, Yihui Pan, Liangmin Fu
{"title":"解读肾癌免疫治疗中GSN+炎性癌相关成纤维细胞的免疫调节功能:来自泛癌单细胞景观和空间转录组学分析的见解。","authors":"Shan Li, Xinwei Zhou, Haoqian Feng, Kangbo Huang, Minyu Chen, Mingjie Lin, Hansen Lin, Zebing Deng, Yuhang Chen, Wuyuan Liao, Zhengkun Zhang, Jinwei Chen, Bohong Guan, Tian Su, Zihao Feng, Guannan Shu, Anze Yu, Yihui Pan, Liangmin Fu","doi":"10.1111/cpr.70062","DOIUrl":null,"url":null,"abstract":"<p><p>The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN<sup>+</sup> iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN<sup>+</sup> iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN<sup>+</sup> iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN<sup>+</sup> iCAFs and CD8<sup>+</sup> Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN<sup>+</sup> iCAFs and CD8<sup>+</sup> exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8<sup>+</sup> T cells, downregulation of GSN on CAFs drives CD8<sup>+</sup> T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.</p>","PeriodicalId":9760,"journal":{"name":"Cell Proliferation","volume":" ","pages":"e70062"},"PeriodicalIF":5.9000,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deciphering the Immunomodulatory Function of GSN<sup>+</sup> Inflammatory Cancer-Associated Fibroblasts in Renal Cell Carcinoma Immunotherapy: Insights From Pan-Cancer Single-Cell Landscape and Spatial Transcriptomics Analysis.\",\"authors\":\"Shan Li, Xinwei Zhou, Haoqian Feng, Kangbo Huang, Minyu Chen, Mingjie Lin, Hansen Lin, Zebing Deng, Yuhang Chen, Wuyuan Liao, Zhengkun Zhang, Jinwei Chen, Bohong Guan, Tian Su, Zihao Feng, Guannan Shu, Anze Yu, Yihui Pan, Liangmin Fu\",\"doi\":\"10.1111/cpr.70062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN<sup>+</sup> iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN<sup>+</sup> iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN<sup>+</sup> iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN<sup>+</sup> iCAFs and CD8<sup>+</sup> Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN<sup>+</sup> iCAFs and CD8<sup>+</sup> exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8<sup>+</sup> T cells, downregulation of GSN on CAFs drives CD8<sup>+</sup> T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.</p>\",\"PeriodicalId\":9760,\"journal\":{\"name\":\"Cell Proliferation\",\"volume\":\" \",\"pages\":\"e70062\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell Proliferation\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1111/cpr.70062\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Proliferation","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1111/cpr.70062","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

癌症相关成纤维细胞(CAFs)的异质性可能影响对免疫检查点抑制剂(ICI)治疗的反应。然而,利用泛癌单细胞RNA测序(scRNA-seq)和空间转录组学测序(ST-seq)分析,有限的研究调查了炎性CAFs (iCAFs)在ICI治疗中的作用。我们进行了泛癌scRNA-seq和ST-seq分析,以确定GSN+ iCAFs亚型,探索其在ICI治疗背景下的空间分布特征。泛癌症scRNA-seq和大量RNA-seq数据被纳入到ca . sig模型中,该模型基于CAF基因签名和机器学习方法预测ICI反应。综合scRNA-seq分析,以及体内和体外实验,探讨了GSN+ iCAFs影响ICI疗效的机制。ca . sig模型在预测泛癌患者的ICI治疗反应方面表现良好。与泛癌和透明细胞肾细胞癌(ccRCC)的应答者相比,在ICI无应答者中观察到更高比例的GSN+ iCAFs。使用真实世界的免疫治疗数据,ca . sig模型准确地预测了泛癌症中的ICI反应,可能与GSN+ iCAFs和CD8+ Tex细胞之间的相互作用有关。ST-seq分析证实GSN+ iCAFs和CD8+耗尽T (Tex)细胞之间的相互作用和细胞距离影响ICI的疗效。在原代CAFs、原代肿瘤细胞和CD8+ T细胞共培养系统中,在ccRCC中,GSN在CAFs上的下调导致CD8+ T细胞进入功能失调状态。在小鼠皮下肿瘤移植模型中,GSN过表达联合ICI治疗ccRCC疗效最佳。我们的研究为患者选择ICI治疗提供了一种更好的方法,并促进了我们对CAF生物学的理解,并提出了在癌症免疫治疗中上调CAF中GSN的潜在治疗策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering the Immunomodulatory Function of GSN+ Inflammatory Cancer-Associated Fibroblasts in Renal Cell Carcinoma Immunotherapy: Insights From Pan-Cancer Single-Cell Landscape and Spatial Transcriptomics Analysis.

The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN+ iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN+ iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN+ iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN+ iCAFs and CD8+ Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN+ iCAFs and CD8+ exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8+ T cells, downregulation of GSN on CAFs drives CD8+ T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Cell Proliferation
Cell Proliferation 生物-细胞生物学
CiteScore
14.80
自引率
2.40%
发文量
198
审稿时长
1 months
期刊介绍: Cell Proliferation Focus: Devoted to studies into all aspects of cell proliferation and differentiation. Covers normal and abnormal states. Explores control systems and mechanisms at various levels: inter- and intracellular, molecular, and genetic. Investigates modification by and interactions with chemical and physical agents. Includes mathematical modeling and the development of new techniques. Publication Content: Original research papers Invited review articles Book reviews Letters commenting on previously published papers and/or topics of general interest By organizing the information in this manner, readers can quickly grasp the scope, focus, and publication content of Cell Proliferation.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信