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基于机器学习生存框架的膀胱癌中二硫键相关亚型的串扰、预后特征模型的建立和免疫浸润特征分析。

Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework.

机构信息

Department of Urology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.

Department of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.

出版信息

Front Endocrinol (Lausanne). 2023 Apr 19;14:1180404. doi: 10.3389/fendo.2023.1180404. eCollection 2023.

Abstract

BACKGROUND

Bladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response.

METHODS

The expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts.

RESULTS

In the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy.

CONCLUSION

Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.

摘要

背景

膀胱癌(BLCA)是最常见的泌尿道恶性肿瘤。另一方面,二硫键蛋白降解(disulfidptosis),一种与肿瘤发生和进展密切相关的二硫键应激诱导细胞死亡机制,我们研究了二硫键蛋白降解相关基因(DRGs)对 BLCA 预后的影响,确定了各种 DRG 聚类,并开发了一种风险模型来评估患者的预后、免疫谱和治疗反应。

方法

首先在批量 RNA-Seq 和单细胞 RNA 测序数据中分析了四个 DRG 的表达和突变特征,免疫组织化学染色确定了 DRGs 在 BLCA 进展中的作用,并通过共识聚类鉴定了两个 DRG 聚类。使用来自这两个聚类的差异表达基因(DEGs),我们将十种机器学习算法转化为 80 多种组合,最终选择最佳算法构建二硫键蛋白降解相关预后签名(DRPS)。我们基于三个 BLCA 队列的平均 C 指数进行了选择。此外,我们还探讨了高低风险组之间临床特征、突变景观、免疫细胞浸润和预测免疫治疗疗效的差异。为了直观地描绘 DRPS 的临床价值,我们采用了列线图。此外,我们还利用 Tumour Immune Dysfunction and Rejection (TIDE) 和 IMvigor 210 队列验证了 DRPS 是否可以预测 BLCA 患者对免疫治疗的反应。

结果

在整合队列中,我们鉴定了几个 DRG 聚类和 DRG 基因聚类,它们在总生存(OS)和肿瘤微环境方面差异显著。在整合临床病理特征后,DRPS 显示出强大的预测能力。根据与二硫键蛋白降解相关的中位数风险评分,BLCA 患者被分为低风险(LR)和高风险(HR)组,LR 组患者预后更好,肿瘤突变负荷更高,对免疫治疗和化疗更敏感。

结论

因此,我们的研究为进一步指导 BLCA 患者的临床管理和制定治疗方案提供了有价值的工具,为个体化治疗提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4c33/10154596/a779487e8644/fendo-14-1180404-g001.jpg

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