ArticleTranslational cancer research2026
Divergent roles of PRR11 and HEPACAM in pleural mesothelioma: PRR11 as a prognostic biomarker and HEPACAM as a stage-associated gene with paradoxical expression.
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Pleural mesothelioma (PM) is an aggressive malignancy with poor prognosis. While identifying drivers of tumor progression is critical, the molecular alterations specifically associated with T-stage advancement remain poorly understood. This study aimed to investigate genes linked to T-stage progression in PM using The Cancer Genome Atlas (TCGA) data, with a focus on the previously unreported roles of PRR11 and HEPACAM in this disease. Methods: RNA-sequencing data and clinical information for 87 PM patients were obtained from the TCGA database. After excluding patients with missing survival data, 43 patients were included in the survival analysis. Differential expression analysis was performed between advanced (T3-T4) and early (T1-T2) T-stage tumors using DESeq2. Survival analysis was conducted using Kaplan-Meier curves and multivariate Cox proportional hazards models. Associations between gene expression and T stage were evaluated, and the correlation between PRR11 and HEPACAM was assessed using Spearman correlation analysis. Results: A total of 116 differentially expressed genes (DEGs) were identified between T3-T4 and T1-T2 tumors. While PRR11 was the most significantly upregulated gene in the overall DEGs analysis (log Conclusions: This study reveals divergent roles for PRR11 and HEPACAM in PM. PRR11 is a robust independent prognostic biomarker, despite its lack of correlation with local tumor invasion as defined by T stage. HEPACAM demonstrates a complex, stage-dependent expression pattern, suggesting a potential context-dependent role in late-stage tumor biology. The inverse correlation between these genes may reflect opposing cellular programs in PM progression. These findings provide new insights into the molecular heterogeneity of PM and highlight the importance of integrating multiple analytical approaches when interpreting transcriptomic data.
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