Predicting fibroblast activation protein overexpression in the overall survival rate of cancer patients: a systematic review and meta-analysis.

Abstract

Introduction: Fibroblast activation protein-alpha (FAP-α) is a vital surface marker of cancer-associated fibroblasts, and its high expression is associated with distant metastasis. This systematic review and meta-analysis were performed to predict the role of FAP-α overexpression in the overall survival rate of cancer patients.

Methods: This systematic review and meta-analysis were first performed on studies published in online databases, including PubMed, Scopus, and Web of Sciences, based on the PRISMA framework. We focused on all cancer patients with reported FAP-α expression levels and survival rate outcomes. Two reviewers independently conducted study selection and data extraction, and discrepancies were resolved through group discussion. Pooled hazard ratios (HRs) were then calculated by the fixed-effect and random-effects models to determine the association between FAP-α with crude HR (univariable), adjusted HR (multivariable), progression-free survival, and disease-free survival.

Results: Forty-one studies were included in the systematic review, and 25 were included in the meta-analysis. Meta-analysis showed that high FAP-α expression was associated with the pooled HR for overall survival (HR = 1.49, 95% CI: 1.19-1.85, P < 0.001) and HR of 1.53 in studies that reported the adjusted effect of high FAP-α expression on overall survival (HR = 1.53, 95% CI: 1.16-2.03, P = 0.003), and HR of 1.36 for disease-free survival (HR = 1.36, 95% CI: 0.750-2.469, P = 0.31). In addition, lymph node metastasis and distant metastasis were significant factors and had pooled HRs of 2.053 (95% CI: 1.603-2.630, P < 0.001) and 2.630 (95% CI: 1.902-3.637, P < 0.001), respectively.

Conclusions: Our results showed that cancer patients with FAP-α overexpression had a significant association with poor overall survival. Incorporating FAP-α testing into cancer diagnostic protocols can help identify high-risk patients requiring more critical treatment interventions.