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

Area of Science:

• Oncology
• Molecular Biology
• Bioinformatics

Background:

• C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are emerging as key theranostic targets in oncology.
• The distinct roles of CXCR4 and FAP in defining tumor microenvironments, particularly in solid tumors, remain inadequately understood.

Purpose of the Study:

• To investigate whether CXCR4 and FAP positivity delineate separate microenvironmental niches across various solid cancers.
• To identify entity-independent molecular signatures associated with CXCR4 and FAP overexpression.

Main Methods:

• Utilized Random Forest (RF) analysis on The Cancer Genome Atlas (TCGA) database (n=9242) to identify mRNA and microRNA signatures for CXCR4 and FAP.
• Performed StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA) for functional enrichment.
• Validated findings via correlation analyses (n=1541) and assessed immune cell infiltration using TIMER2.0.

Main Results:

• Identified robust, entity-independent gene signatures for CXCR4 and FAP across most solid cancers.
• CXCR4 overexpression was associated with an immune-related microenvironment, characterized by CD8+ cell infiltration.
• FAP overexpression was linked to an angiogenesis-associated niche, with increased endothelial cell infiltration.

Conclusions:

• CXCR4 and FAP represent distinct theranostic targets, marking separate tumor microenvironmental niches.
• CXCR4- and FAP-directed PET imaging may offer non-invasive decision support for entity-agnostic microenvironment-targeted therapies.
• The developed machine learning workflow is adaptable for other theranostic targets.