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Yunfeng Dai

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Open access Aug 2026

Influence of tumor mutational burden and immune infiltration on cervical squamous cell carcinoma prognosis

Introduction: Cervical squamous cell carcinoma (CESC) remains a significant global health challenge for women, necessitating the discovery of precise molecular biomarkers to optimize personalized treatment and immunotherapy strategies. Objective: Leveraging bioinformatics data from The Cancer Genome Atlas (TCGA) and the independent CGCI–HTMCP–CC cohort for external validation, this study rigorously evaluated the prognostic role of tumor mutational burden (TMB) in CESC. Methods: Our methodology utilized standardized transcripts per million (TPM) normalization and the maximally selected rank statistics (maxstat) algorithm to establish biologically optimal TMB thresholds, moving beyond traditional median-based stratification. Results: While somatic mutation analysis identified high frequencies in TTN (29%) and PIK3CA (27%), high TMB levels did not directly correlate with patient overall survival (p = 0.720). However, a marginal non-significant trend was observed between elevated TMB and advanced tumor T-staging (p = 0.057). Differential expression and Cox regression analyses highlighted PTGS2 as a distinctive TMB-related risk gene. Based on this, a TMB-related risk score (TMBRS) was constructed, demonstrating moderate yet consistent predictive utility (area under the curve = 0.696) across both primary and independent validation cohorts. Detailed immune profiling via Cell-type Identification by Estimating Relative Subsets of RNA Transcripts and Tumor Immune Estimation Resource revealed that high TMB and lower risk scores are specifically associated with increased infiltration of CD8+ T cells and M1 macrophages, suggesting enhanced local immune recognition. Conclusion: Although the clinical utility of the TMBRS is currently moderate, this research provides a critical proof of concept for the interplay among PTGS2, mutational load, and the tumor microenvironment, offering valuable mechanistic insights for future large-scale prospective clinical trials.

Batchimeg Tsedenbal, Battogtokh Chimeddorj, Shu-Tao Tan et al. · 0 citations

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