Background

The pattern of cell death known as disulfidptosis was recently discovered. Disulfidptosis, which may affect the growth of tumor cells, represents a potential new approach to treating tumors. Glycolysis affects tumor proliferation, invasion, chemotherapy resistance, the tumor microenvironment (TME), and immune evasion. However, the efficacy and therapeutic significance of disulfidptosis-related glycolysis genes (DRGGs) in stomach adenocarcinoma (STAD) remain uncertain.

Methods

STAD clinical data and RNA sequencing data were downloaded from the TCGA database. DRGGs were screened using Cox regression and Lasso regression analysis to construct a prognostic risk model. The accuracy of the model was verified using survival studies, receiver operating characteristic (ROC) curves, column plots, and calibration curves. Additionally, our study investigated the relationships between the risk scores and immune cell infiltration, tumor mutational burden (TMB), and anticancer drug sensitivity.

Results

We have successfully developed a prognosis risk model with 4 DRGGs (NT5E, ALG1, ANKZF1, and VCAN). The model showed excellent performance in predicting the overall survival of STAD patients. The DRGGs prognostic model significantly correlated with the TME, immune infiltrating cells, and treatment sensitivity.

Conclusions

The risk model developed in this work has significant clinical value in predicting the impact of immunotherapy in STAD patients and assisting in the choice of chemotherapeutic medicines. It can correctly estimate the prognosis of STAD patients.

中文翻译：

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二硫醇解相关基因风险模型构建预测胃腺癌预后及免疫浸润分析

背景

最近发现了称为二硫下垂症的细胞死亡模式。二硫下垂可能影响肿瘤细胞的生长，代表了一种潜在的治疗肿瘤的新方法。糖酵解影响肿瘤增殖、侵袭、化疗耐药、肿瘤微环境 (TME) 和免疫逃避。然而，二硫键分解相关糖酵解基因（DRGG）在胃腺癌（STAD）中的功效和治疗意义仍不确定。

方法

STAD临床数据和RNA测序数据从TCGA数据库下载。使用Cox回归和Lasso回归分析筛选DRGG，构建预后风险模型。使用生存研究、受试者工作特征 (ROC) 曲线、柱形图和校准曲线验证模型的准确性。此外，我们的研究还调查了风险评分与免疫细胞浸润、肿瘤突变负荷（TMB）和抗癌药物敏感性之间的关系。

结果

我们成功开发了包含 4 个 DRGG（NT5E、ALG1、ANKZF1 和 VCAN）的预后风险模型。该模型在预测 STAD 患者的总生存期方面表现出优异的性能。 DRGGs 预后模型与 TME、免疫浸润细胞和治疗敏感性显着相关。

结论

这项工作开发的风险模型对于预测免疫治疗对 STAD 患者的影响和协助化疗药物的选择具有重要的临床价值。可以正确估计STAD患者的预后。