To establish a TAM polarization-centered model and elucidate the mechanisms of tumor-immune crosstalk in cervical cancer.
Approach:
Data Integration: Bulk transcriptomics from TCGA were integrated with single-cell RNA sequencing data (GSE208653).
Prognostic Signature Construction: Weighted gene co-expression network analysis (WGCNA) combined with a multi-algorithm machine learning framework was used to construct a prognostic signature.
Validation: The signature was independently validated in the GEO GSE52903 cohort.
Functional Validation: In vitro assays were performed using cervical cancer cell lines co-cultured with THP-1-derived macrophages.
Key Findings:
A five-gene prognostic signature (TP73, TFRC, SHC1, SCD, and PFKFB3) was developed, effectively stratifying patient survival.
High risk scores correlated with a suppressed antitumor immune landscape and diminished predicted chemosensitivity.
TFRC was confirmed as a tumor-intrinsic factor that enhances pro-M2 signaling.
In vitro assays demonstrated that tumor-derived TFRC orchestrates an immunosuppressive M2-like macrophage niche.
Interpretation:
The study establishes a prognostic model linking macrophage plasticity to clinical outcomes, identifying TFRC as a factor in shaping the immunosuppressive niche in cervical cancer.
Limitations:
The study may be limited by the sample size of the clinical cohorts used for validation.
Potential batch effects and technical variability in RNA sequencing data could influence results.
Conclusion:
The findings provide a foundation for understanding the relationship between tumor-intrinsic iron metabolism and immune evasion.
