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Shu-Tao Tan

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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
Open access Jan 2026

Genetically Predicted Muscle Mass and Function in Relation to Deep Vein Thrombosis: A Two-step Mendelian Randomization Study Highlighting the Mediating Role of BMI

Background Sarcopenia is observationally linked to venous thromboembolism, but the causal architecture and underlying biological pathways remain largely unclear. This study investigated the causal effects of sarcopenia-related traits on lower extremity deep vein thrombosis (DVT) and quantified potential mediating mechanisms. Methods We performed two-sample bidirectional Mendelian randomization (MR) and two-step mediation MR using large-scale GWAS data from UK Biobank, EMBL-EBI, and FinnGen. Exposures included appendicular lean mass (ALM), leg fat-free mass (LFM), hand grip strength, and walking pace. Eighteen candidate mediators were screened for indirect pathways. Results Genetically predicted higher ALM was significantly associated with increased DVT risk (FinnGen: OR = 1.288, 95% CI: 1.215–1.365, P < 0.001). Similar positive associations were observed for LFM (OR = 1.920–1.954, P < 0.001). By contrast, muscle functional traits - grip strength and walking pace - demonstrated no consistent causal effects. Reverse MR confirmed a unidirectional relationship. Body mass index (BMI) emerged as a pivotal mediator, accounting for 7.58% – 10.50% of the ALM–DVT effect and 52.74% – 62.73% of the LFM–DVT effect. Notably, the independent effect of ALM was largely attenuated after adjusting for metabolic confounders in multivariable MR. Conclusion Genetic predisposition to high muscle mass, rather than functional strength, increases DVT risk. This relationship appears to be significantly driven by metabolic adiposity, suggesting that the “muscle–vascular–coagulation” interaction is partly explained by body-size-related metabolic burden. Risk stratification should integrate muscle mass evaluation with comprehensive metabolic health assessments.

Qi-Xiang Sun, Yi-Ming Hu, Zhen-Yong Yang et al. · 0 citations

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