基于泛素结合酶E2相关基因解读透明细胞肾细胞癌的潜在分子机制:确定UBE2C与调节性T细胞浸润相关。

IF 5 3区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
BioFactors Pub Date : 2024-11-29 DOI:10.1002/biof.2143
Xiaoqiang Feng, Zhenwei Wang, Meini Cen, Zongtai Zheng, Bangqi Wang, Zongxiang Zhao, Zhihui Zhong, Yesong Zou, Qian Lv, Shiyu Li, Li Huang, Hai Huang, Xiaofu Qiu
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引用次数: 0

摘要

肾透明细胞癌(ccRCC)是一种高度侵袭性和常见的肾癌,晚期治疗选择有限。最近的研究强调了泛素-蛋白酶体系统在肿瘤进展中的重要性,特别是泛素偶联酶E2 (UBE2)家族成员的作用。然而,ube2相关基因(UBE2RGs)在ccRCC中的预后意义尚不清楚。在本研究中,从Cancer Genome Atlas和Gene Expression Omnibus数据库中检索ccRCC患者的大量rna测序和单细胞rna测序数据。进行差异表达分析以鉴定与ccRCC相关的UBE2RGs。采用10种机器学习方法的组合来开发最佳预后模型,并在训练和验证队列中使用1、3和5年总生存期(OS)的曲线下面积(AUC)值来评估其预测性能。对基因本体和京都基因基因组百科全书进行了功能富集分析,以探索所涉及的生物学途径。通过相关分析探讨风险评分与肿瘤突变负荷(tumor mutational burden, TMB)和免疫细胞浸润的关系。通过免疫表型评分和肿瘤免疫、功能障碍和排斥评分评估免疫治疗和化疗敏感性,以确定潜在的预测意义。在体外,通过特异性小干扰RNA敲除786-O细胞中的关键基因UBE2C,验证其对凋亡、迁移、细胞周期、肿瘤细胞迁移、侵袭和诱导调节性T细胞(Tregs)的影响。sc-RNA分析显示,UBE2活性在恶性细胞中显著上调,提示其在肿瘤进展中的作用。通过Lasoo Cox回归构建了由UBE2C、UBE2D3和UBE2T组成的三基因预后模型,该模型具有较强的预测准确性,1年、3年和5年生存率的AUC分别为0.745、0.766和0.771。该模型被证实为ccRCC的独立预后因素。高危组患者预后较差,TMB评分较高,对免疫治疗的反应性较低。此外,免疫浸润和化疗敏感性分析显示,UBE2RGs与多种免疫细胞和药物相关,这表明UBE2RGs可能是ccRCC的潜在治疗靶点。体外实验证实,UBE2C的减少导致细胞凋亡率升高,肿瘤细胞侵袭转移能力下降。此外,si-UBE2C细胞减少了细胞因子转化生长因子-β1 (TGF-β1)的释放,导致共培养体系中Tregs的比例降低。本研究提出了一种基于UBE2RGs的新型三基因预后模型,该模型对ccRCC患者的OS、免疫治疗和化疗具有重要的预测价值。这些发现强调了UBE2家族成员作为ccRCC的生物标志物和治疗靶点的潜力,值得在前瞻性临床试验中进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering potential molecular mechanisms in clear cell renal cell carcinoma based on the ubiquitin-conjugating enzyme E2 related genes: Identifying UBE2C correlates to infiltration of regulatory T cells

Renal clear cell carcinoma (ccRCC) is a highly aggressive and common form of kidney cancer, with limited treatment options for advanced stages. Recent studies have highlighted the importance of the ubiquitin-proteasome system in tumor progression, particularly the role of ubiquitin-conjugating enzyme E2 (UBE2) family members. However, the prognostic significance of UBE2-related genes (UBE2RGs) in ccRCC remains unclear. In this study, bulk RNA-sequencing and single-cell RNA-sequencing data from ccRCC patients were retrieved from the Cancer Genome Atlas and Gene Expression Omnibus databases. Differential expression analysis was performed to identify UBE2RGs associated with ccRCC. A combination of 10 machine learning methods was applied to develop an optimal prognostic model, and its predictive performance was evaluated using area under the curve (AUC) values for 1-, 3-, and 5-year overall survival (OS) in both training and validation cohorts. Functional enrichment analyses of gene ontology and Kyoto Encyclopedia of Genes and Genomes were conducted to explore the biological pathways involved. Correlation analysis was conducted to investigate the association between the risk score and tumor mutational burden (TMB) and immune cell infiltration. Immunotherapy and chemotherapy sensitivity were assessed by immunophenoscore and tumor immune, dysfunction, and exclusion scores to identify potential predictive significance. In vitro, knockdown of the key gene UBE2C in 786-O cells by specific small interfering RNA to validate its impact on apoptosis, migration, cell cycle, migration, invasion of tumor cells, and induction of regulatory T cells (Tregs). Analysis of sc-RNA revealed that UBE2 activity was significantly upregulated in malignant cells, suggesting its role in tumor progression. A three-gene prognostic model comprising UBE2C, UBE2D3, and UBE2T was constructed by Lasoo Cox regression and demonstrated robust predictive accuracy, with AUC values of 0.745, 0.766, and 0.771 for 1-, 3-, and 5-year survival, respectively. The model was validated as an independent prognostic factor in ccRCC. Patients in the high-risk group had a worse prognosis, higher TMB scores, and low responsiveness to immunotherapy. Additionally, immune infiltration and chemotherapy sensitivity analyses revealed that UBE2RGs are associated with various immune cells and drugs, suggesting that UBE2RGs could be a potential therapeutic target for ccRCC. In vitro experiments confirmed that the reduction of UBE2C led to an increase in apoptosis rate, as well as a decrease in tumor cell invasion and metastasis abilities. Additionally, si-UBE2C cells reduced the release of the cytokine Transforming Growth Factor-beta 1 (TGF-β1), leading to a decreased ratio of Tregs in the co-culture system. This study presents a novel three-gene prognostic model based on UBE2RGs that demonstrates significant predictive value for OS, immunotherapy, and chemotherapy in ccRCC patients. The findings underscore the potential of UBE2 family members as biomarkers and therapeutic targets in ccRCC, warranting further investigation in prospective clinical trials.

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来源期刊
BioFactors
BioFactors 生物-内分泌学与代谢
CiteScore
11.50
自引率
3.30%
发文量
96
审稿时长
6-12 weeks
期刊介绍: BioFactors, a journal of the International Union of Biochemistry and Molecular Biology, is devoted to the rapid publication of highly significant original research articles and reviews in experimental biology in health and disease. The word “biofactors” refers to the many compounds that regulate biological functions. Biological factors comprise many molecules produced or modified by living organisms, and present in many essential systems like the blood, the nervous or immunological systems. A non-exhaustive list of biological factors includes neurotransmitters, cytokines, chemokines, hormones, coagulation factors, transcription factors, signaling molecules, receptor ligands and many more. In the group of biofactors we can accommodate several classical molecules not synthetized in the body such as vitamins, micronutrients or essential trace elements. In keeping with this unified view of biochemistry, BioFactors publishes research dealing with the identification of new substances and the elucidation of their functions at the biophysical, biochemical, cellular and human level as well as studies revealing novel functions of already known biofactors. The journal encourages the submission of studies that use biochemistry, biophysics, cell and molecular biology and/or cell signaling approaches.
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