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Background <p>Cholangiocarcinoma (CCA) is a malignancy with a poor prognosis and limited effective prognostic markers or therapeutic strategies. This study aims to construct and validate a prognostic risk model based on oxidative stress-related genes (OSRGs) to stratify patient risk and guide individualized treatment for CCA.</p> Methods <p>Using data from 66 tumors and 36 control samples obtained from the TCGA and GEO databases, we identified 3,632 differentially expressed genes (DEGs). Intersection with oxidative stress-related genes revealed 122 OSRGs, from which 10 key prognostic genes were selected through univariate Cox and LASSO regression analyses. Model performance was assessed with Kaplan-Meier survival, ROC curve, PCA, and t-SNE analyses. Tumor microenvironment (TME) features, immune cell infiltration, and chemosensitivity to cisplatin and 5-fluorouracil were also evaluated.</p> Results <p>The 10-gene model, including genes like RORA and GRIN2A, effectively distinguished between high- and low-risk groups, with high RORA and GRIN2A expression correlating with improved survival. High-risk groups displayed increased immune and ESTIMATE scores, with significant differences in immune cell infiltration. Chemosensitivity analysis indicated differential responses to various chemotherapeutics in high-risk patients.</p> Conclusion <p>This OSRG-based model demonstrates strong prognostic potential, stratifying CCA patients by risk and survival outcomes. The model provides insights into personalized treatment strategies, potentially advancing therapeutic interventions for CCA.</p>

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An oxidative stress related gene signature predicts prognosis in cholangiocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月18日

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  • Han Fan,
  • Peng Qiu,
  • Yun Lu,
  • Zhengdong Deng,
  • Li Tian

摘要

Background

Cholangiocarcinoma (CCA) is a malignancy with a poor prognosis and limited effective prognostic markers or therapeutic strategies. This study aims to construct and validate a prognostic risk model based on oxidative stress-related genes (OSRGs) to stratify patient risk and guide individualized treatment for CCA.

Methods

Using data from 66 tumors and 36 control samples obtained from the TCGA and GEO databases, we identified 3,632 differentially expressed genes (DEGs). Intersection with oxidative stress-related genes revealed 122 OSRGs, from which 10 key prognostic genes were selected through univariate Cox and LASSO regression analyses. Model performance was assessed with Kaplan-Meier survival, ROC curve, PCA, and t-SNE analyses. Tumor microenvironment (TME) features, immune cell infiltration, and chemosensitivity to cisplatin and 5-fluorouracil were also evaluated.

Results

The 10-gene model, including genes like RORA and GRIN2A, effectively distinguished between high- and low-risk groups, with high RORA and GRIN2A expression correlating with improved survival. High-risk groups displayed increased immune and ESTIMATE scores, with significant differences in immune cell infiltration. Chemosensitivity analysis indicated differential responses to various chemotherapeutics in high-risk patients.

Conclusion

This OSRG-based model demonstrates strong prognostic potential, stratifying CCA patients by risk and survival outcomes. The model provides insights into personalized treatment strategies, potentially advancing therapeutic interventions for CCA.