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膀胱癌中免疫细胞死亡相关吉西他滨耐药基因的预后及免疫微环境的生物信息学分析

Bioinformatics analysis of prognosis and immune microenvironment of immunological cell death-related gemcitabine-resistance genes in bladder cancer.

作者信息

Liu Chao, Wang Xiao-Lan, Shen Er-Chang, Wang Bing-Zhi, Meng Rui, Cui Yong, Wang Wen-Jie, Shao Qiang

机构信息

Department of Urology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.

Department of Clinical Medicine, Nantong University, Nantong, China.

出版信息

Transl Androl Urol. 2022 Dec;11(12):1715-1728. doi: 10.21037/tau-22-736.

Abstract

BACKGROUND

Bladder cancer (BC) is the most common malignant tumor of the urinary system. Gemcitabine resistance partly accounts for treatment failure and recurrence in BC. Immunological cell death (ICD) is correlated with chemoresistance. The prognosis of patients with similar tumor stage still varies in response to chemotherapy, recurrence, and disease progression. Therefore, our study aimed to provide a prognostic model based on ICD-related and gemcitabine-resistance genes for BC.

METHODS

The data of BC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs), and differentially expressed gemcitabine resistance-related genes (DEGRRGs) were identified using the edgeR package. The survival-associated DEGRRGs were identified by univariate Cox analysis. A prognostic model was established by univariate Cox regression analysis and validated by GEO dataset. The outcome of low-risk group and high-risk group was analyzed by the Kaplan-Meier curve. The relationship between risk score and immune cell infiltration was investigated using the TIMER online database.

RESULTS

The prognosis of patients in the ICD-high group was significantly better than ICD-low group A prognostic model containing 5 gemcitabine resistance-related ICD-associated genes, including , and , was established. In both TCGA prognostic model and GEO validation model, patients in the low-risk group had better outcomes than high-risk group. According to the receiver operating characteristic (ROC) curves, the risk score area under ROC curve (AUC) of the TCGA prognostic model were calculated to be 0.705, while the risk score of the GEO validation model were calculated to be 0.716. Patients in the high-risk group had a significantly higher immune score, stromal score, and infiltration of M0 macrophages, M1 macrophages, M2 macrophages, and activated CD4 T cells. Patients in the high-risk group had significantly lower infiltration of the regulatory T cells, resting dendritic cell (DCs), and activated DCs.

CONCLUSIONS

The present study highlighted the functional role of gemcitabine resistance-related ICD-associated genes, constructed a prognostic score for the outcome evaluation and searched for potential targets to overcome gemcitabine chemoresistance in BC.

摘要

背景

膀胱癌(BC)是泌尿系统最常见的恶性肿瘤。吉西他滨耐药是BC治疗失败和复发的部分原因。免疫性细胞死亡(ICD)与化疗耐药相关。肿瘤分期相似的患者对化疗、复发和疾病进展的反应预后仍存在差异。因此,我们的研究旨在为BC提供一种基于ICD相关和吉西他滨耐药基因的预后模型。

方法

BC患者的数据来自癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)。使用edgeR软件包鉴定差异表达基因(DEGs)和差异表达的吉西他滨耐药相关基因(DEGRRGs)。通过单因素Cox分析鉴定与生存相关的DEGRRGs。通过单因素Cox回归分析建立预后模型,并由GEO数据集进行验证。采用Kaplan-Meier曲线分析低风险组和高风险组的预后。使用TIMER在线数据库研究风险评分与免疫细胞浸润之间的关系。

结果

ICD高组患者的预后明显优于ICD低组。建立了一个包含5个与吉西他滨耐药相关的ICD相关基因的预后模型,包括 、 和 。在TCGA预后模型和GEO验证模型中,低风险组患者的预后均优于高风险组。根据受试者工作特征(ROC)曲线,计算出TCGA预后模型的风险评分ROC曲线下面积(AUC)为0.705,而GEO验证模型的风险评分为0.716。高风险组患者的免疫评分、基质评分以及M0巨噬细胞、M1巨噬细胞、M2巨噬细胞和活化CD4 T细胞的浸润显著更高。高风险组患者的调节性T细胞、静息树突状细胞(DCs)和活化DCs的浸润显著更低。

结论

本研究突出了吉西他滨耐药相关ICD相关基因的功能作用,构建了用于结果评估的预后评分,并寻找克服BC中吉西他滨化疗耐药的潜在靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d4d/9827393/c8945ee60243/tau-11-12-1715-f1.jpg

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