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预测CXCR4和FAP阳性实体瘤中的微环境——用于治疗诊断靶结构的泛癌机器学习工作流程

Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors-A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures.

作者信息

Marquardt André, Hartrampf Philipp, Kollmannsberger Philip, Solimando Antonio G, Meierjohann Svenja, Kübler Hubert, Bargou Ralf, Schilling Bastian, Serfling Sebastian E, Buck Andreas, Werner Rudolf A, Lapa Constantin, Krebs Markus

机构信息

Department of Pathology, Klinikum Stuttgart, 70174 Stuttgart, Germany.

Department of Nuclear Medicine, University Hospital Würzburg, 97080 Würzburg, Germany.

出版信息

Cancers (Basel). 2023 Jan 6;15(2):392. doi: 10.3390/cancers15020392.

DOI:10.3390/cancers15020392
PMID:36672341
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9856808/
Abstract

(1) C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database-representing = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.

摘要

(1) C-X-C基序趋化因子受体4(CXCR4)和成纤维细胞活化蛋白α(FAP)是很有前景的治疗诊断靶点。然而,CXCR4和FAP阳性是否标志着不同的微环境尚不清楚,尤其是在实体瘤中。(2) 我们使用随机森林(RF)分析,在来自癌症基因组图谱(TCGA)数据库的泛癌队列中搜索与CXCR4和FAP过表达相关的独立于肿瘤实体的mRNA和微小RNA特征,该队列代表来自29个肿瘤实体的9242个样本。通过StringDB聚类分析、EnrichR、Metascape和基因集富集分析(GSEA)对CXCR4和FAP阳性样本进行评估。研究结果在1541个肿瘤样本中通过相关性分析进行验证。TIMER2.0分析了CXCR4/FAP表达与免疫相关细胞浸润水平之间的关联。(3) 我们确定了代表大多数实体癌的独立于肿瘤实体的CXCR4和FAP基因特征。CXCR4阳性标志着免疫相关的微环境,而FAP过表达突出了与血管生成相关的生态位。TIMER2.0分析证实了CXCR4阳性肿瘤中CD8+细胞以及FAP阳性肿瘤中内皮细胞的特征性浸润水平。(4) 针对CXCR4和FAP的正电子发射断层扫描(PET)成像可为实体恶性肿瘤微环境的无实体诊断治疗提供非侵入性决策辅助。此外,这种机器学习工作流程可以很容易地应用于其他治疗诊断靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/7579747f9dd5/cancers-15-00392-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/86b9d9b7c2b3/cancers-15-00392-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/ebc8931a4126/cancers-15-00392-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/c2d79c88c4fd/cancers-15-00392-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/1c0e8c4ea809/cancers-15-00392-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/9dbd4e7d18fe/cancers-15-00392-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/7579747f9dd5/cancers-15-00392-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/86b9d9b7c2b3/cancers-15-00392-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/ebc8931a4126/cancers-15-00392-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/c2d79c88c4fd/cancers-15-00392-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/1c0e8c4ea809/cancers-15-00392-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/9dbd4e7d18fe/cancers-15-00392-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0c9/9856808/7579747f9dd5/cancers-15-00392-g006.jpg

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