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

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

(1) <b>Background:</b> 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) <b>Methods:</...

Full description

Bibliographic Details
Main Authors: André Marquardt, Philipp Hartrampf, Philip Kollmannsberger, Antonio G. Solimando, Svenja Meierjohann, Hubert Kübler, Ralf Bargou, Bastian Schilling, Sebastian E. Serfling, Andreas Buck, Rudolf A. Werner, Constantin Lapa, Markus Krebs
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Cancers
Subjects:
machine learning
tumor microenvironment
immune infiltration
angiogenesis
mRNA
miRNA
Online Access:https://www.mdpi.com/2072-6694/15/2/392

Internet

https://www.mdpi.com/2072-6694/15/2/392

Similar Items