Cancer-associated fibroblasts-derived FMO2 as a biomarker of macrophage infiltration and prognosis in epithelial ovarian cancer

川地163 基质 医学 渗透(HVAC) 间质细胞 免疫系统 免疫 肿瘤浸润淋巴细胞 病理 免疫组织化学 癌症研究 生物 免疫学 CD8型 免疫疗法 基因 表型 物理 热力学 生物化学
作者
Sihui Yu,Rui Yang,Tianhan Xu,Xi Li,Sufang Wu,Jiawen Zhang
出处
期刊:Gynecologic Oncology [Elsevier BV]
卷期号:167 (2): 342-353 被引量:12
标识
DOI:10.1016/j.ygyno.2022.09.003
摘要

Recent molecular profiling revealed that cancer-associated fibroblasts (CAFs) are essential for matrix remodeling and tumor progression. Our study aimed to investigate the role of flavin-containing monooxygenase 2 (FMO2) in epithelial ovarian cancer (EOC) as a novel CAF-derived prognostic biomarker.Primary fibroblasts were isolated from EOC samples. Microdissection and single-cell RNA sequencing (scRNA-seq) datasets (including TCGA, GSE9891, GSE63885, GSE118828 and GSE178913) were retrieved to determine the expression profiles. Gene set enrichment analysis (GSEA) was used to explore the correlation between FMO2 and stromal activation as well as immune infiltration. The predictive value of FMO2 and combined macrophage infiltration level was verified in an independent EOC cohort (n = 113).We demonstrated that FMO2 was upregulated in tumor stroma and correlated with fibroblast activation. Besides, FMO2 had the predictive power for worse clinical outcome of EOC patients. In the mesenchymal subtype of EOC, the FMO2-defined signature revealed that FMO2 contributed to infiltration of tumor-infiltrating lymphocytes. Moreover, we confirmed the positive correlation between FMO2 and CD163+ cell infiltration level in EOC tissues, and showed that combination of FMO2 expression with CD163+ cell infiltration level in the tumor stroma could predict poor overall survival (HR = 3.63, 95% CI = 1.93-6.84, p = 0.0008). Additionally, FMO2 also predicted the prognosis of patients with ovarian cancer based on the expression of immune checkpoints (such as PD-L1 and PD1).Our results address the tumor-supporting role of FMO2 in EOC and its association with immune components, and it might be a prospective target for stroma-oriented therapies against EOC.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
觉允若意发布了新的文献求助10
刚刚
Zenobia发布了新的文献求助30
1秒前
1秒前
希望天下0贩的0应助Itazu采纳,获得10
3秒前
vv发布了新的文献求助10
4秒前
4秒前
小蘑菇应助饱满的若山采纳,获得10
5秒前
空心胶囊完成签到,获得积分10
6秒前
Orange应助ReBirth1111采纳,获得10
6秒前
丫丫完成签到,获得积分10
9秒前
xiaoyang完成签到 ,获得积分10
9秒前
科研通AI6.2应助咎如天采纳,获得10
9秒前
9秒前
9秒前
小凯发布了新的文献求助10
10秒前
10秒前
ho发布了新的文献求助30
12秒前
13秒前
黙宇循光完成签到 ,获得积分10
13秒前
美满又蓝应助Jerome采纳,获得10
13秒前
小彭ppp完成签到 ,获得积分10
14秒前
14秒前
14秒前
fuHM完成签到,获得积分10
14秒前
kepiaaaaaaa应助he采纳,获得10
15秒前
猪皮恶人发布了新的文献求助10
15秒前
Clovis33完成签到 ,获得积分10
15秒前
16秒前
16秒前
17秒前
领导范儿应助Fighter采纳,获得10
17秒前
17秒前
17秒前
18秒前
Cyr发布了新的文献求助10
18秒前
愤怒的苗条完成签到 ,获得积分10
18秒前
barryqtc发布了新的文献求助10
18秒前
浅色西完成签到,获得积分0
19秒前
cc完成签到,获得积分10
20秒前
20秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7580861
求助须知:如何正确求助?哪些是违规求助? 9160310
关于积分的说明 19598627
捐赠科研通 7163354
什么是DOI,文献DOI怎么找? 3265939
关于科研通互助平台的介绍 2430854
邀请新用户注册赠送积分活动 2257007