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Background <p>Aging-related genes (ARGs) are prognostic markers in cancers, but their role in lung adenocarcinoma (LUAD) remains unclear. Investigating ARGs in LUAD may provide valuable insights for clinical diagnosis and treatment.</p> Methods <p>Gene expression profiles from TCGA and GEO datasets were analyzed to identify ARG-related genes. Univariate Cox regression and LASSO analysis were used to construct a prognostic risk model. Kaplan-Meier survival analysis, ROC curves, and clinicopathological features validated its predictive accuracy. Immune cell infiltration and tumor microenvironment were assessed using CIBERSORT and ESTIMATE. RT-qPCR was used to validate the differential expression of key genes in LUAD and adjacent normal tissues.</p> Results <p>Seven prognostic ARGs (RHPN2, BLK, PTPRO, CA4, UBE2C, METTL7A, and H2BC12) were identified. The risk model stratified patients into high- and low-risk groups, with high-risk individuals showing poorer survival, increased immune evasion, and altered immune cell infiltration. These findings were validated in independent datasets. RT-qPCR confirmed elevated RHPN2, BLK, UBE2C, and H2BC12 in tumors, while PTPRO, CA4, and METTL7A were reduced.</p> Conclusion <p>A robust ARG-based risk model, leveraging Cox regression and LASSO, effectively predicts survival and immunotherapy responses in LUAD, offering new tools for personalized prognosis and treatment strategies.</p>

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A Mendelian randomization-based risk model using aging-related genes for prognosis in lung adenocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Shaowen Feng,
  • Xiaowang Liu,
  • Huiqin Yang,
  • Jiazheng Huang,
  • Ziang Wang,
  • Hui Li,
  • Jiahao Cai,
  • Zhonghe Liu

摘要

Background

Aging-related genes (ARGs) are prognostic markers in cancers, but their role in lung adenocarcinoma (LUAD) remains unclear. Investigating ARGs in LUAD may provide valuable insights for clinical diagnosis and treatment.

Methods

Gene expression profiles from TCGA and GEO datasets were analyzed to identify ARG-related genes. Univariate Cox regression and LASSO analysis were used to construct a prognostic risk model. Kaplan-Meier survival analysis, ROC curves, and clinicopathological features validated its predictive accuracy. Immune cell infiltration and tumor microenvironment were assessed using CIBERSORT and ESTIMATE. RT-qPCR was used to validate the differential expression of key genes in LUAD and adjacent normal tissues.

Results

Seven prognostic ARGs (RHPN2, BLK, PTPRO, CA4, UBE2C, METTL7A, and H2BC12) were identified. The risk model stratified patients into high- and low-risk groups, with high-risk individuals showing poorer survival, increased immune evasion, and altered immune cell infiltration. These findings were validated in independent datasets. RT-qPCR confirmed elevated RHPN2, BLK, UBE2C, and H2BC12 in tumors, while PTPRO, CA4, and METTL7A were reduced.

Conclusion

A robust ARG-based risk model, leveraging Cox regression and LASSO, effectively predicts survival and immunotherapy responses in LUAD, offering new tools for personalized prognosis and treatment strategies.