A Senescence-Associated Gene Signature for Prognostic Prediction and Therapeutic Targeting in Adrenocortical Carcinoma

衰老相关基因特征在肾上腺皮质癌预后预测和治疗靶向中的应用

阅读:1

Abstract

Background/Objectives: Cellular senescence plays a critical role in tumorigenesis, immune cell infiltration, and treatment response. Adrenocortical carcinoma (ACC) is a malignant tumor that lacks effective therapies. This study aimed to construct and validate a senescence-related gene signature as an independent prognostic predictor for ACC and explore its impact on the tumor microenvironment, immunotherapy, and chemotherapy response. Methods: Data were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Using Kaplan-Meier survival analysis, LASSO penalized Cox regression and multivariable Cox regression, we identified a prognostic model with four senescence-related genes (HJURP, CDK1, FOXM1, and CHEK1). The model's prognostic value was validated through survival analysis, risk score curves, and receiver operating characteristic (ROC) curves. Tumor mutation burden was assessed with maftools, and the tumor microenvironment was analyzed using CIBERSORT and ESTIMATE. Immune and chemotherapeutic responses were assessed through Tumor Immune Dysfunction and Exclusion (TIDE) and OncoPredict. Results: The risk score derived from our model showed a strong association with overall survival (OS) in ACC patients (p < 0.001, HR = 2.478). Higher risk scores were correlated with more advanced tumor stages and a greater frequency of somatic mutations. Differentially expressed genes (DEGs) that were downregulated in the high-risk group were significantly enriched in immune-related pathways. Furthermore, high-risk patients were predicted to have reduced sensitivity to immunotherapy (p = 0.02). Bioinformatics analysis identified potential chemotherapeutic agents, including BI-2536 and MIM1, as more effective treatment options for high-risk patients. Conclusions: Our findings indicate that this prognostic model may serve as a valuable tool for predicting overall survival (OS) and treatment responses in ACC patients, including those receiving chemotherapy and immunotherapy.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。