• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

蛋白质基因组学在尿液分析中用于鉴定前列腺癌新型生物标志物的应用:一项探索性研究。

Application of Proteogenomics to Urine Analysis towards the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study.

作者信息

Lima Tânia, Barros António S, Trindade Fábio, Ferreira Rita, Leite-Moreira Adelino, Barros-Silva Daniela, Jerónimo Carmen, Araújo Luís, Henrique Rui, Vitorino Rui, Fardilha Margarida

机构信息

Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, 3810-193 Aveiro, Portugal.

Cancer Biology and Epigenetics Group, Research Center of Portuguese Oncology Institute of Porto (GEBC CI-IPOP) & Porto Comprehensive Cancer Center (P.CCC), 4200-072 Porto, Portugal.

出版信息

Cancers (Basel). 2022 Apr 15;14(8):2001. doi: 10.3390/cancers14082001.

DOI:10.3390/cancers14082001
PMID:35454907
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9031064/
Abstract

To identify new protein targets for PCa detection, first, a shotgun discovery experiment was performed to characterize the urinary proteome of PCa patients. This revealed 18 differentially abundant urinary proteins in PCa patients. Second, selected targets were clinically tested by immunoblot, and the soluble E-cadherin fragment was detected for the first time in the urine of PCa patients. Third, the proteogenome landscape of these PCa patients was characterized, revealing 1665 mutant protein isoforms. Statistical analysis revealed 6 differentially abundant mutant protein isoforms in PCa patients. Analysis of the likely effects of mutations on protein function and PPIs involving the dysregulated mutant protein isoforms suggests a protective role of mutations HSPG2Q1062H and VASNR161Q and an adverse role of AMBPA286G and CD55S162L in PCa patients. This work originally characterized the urinary proteome, focusing on the proteogenome profile of PCa patients, which is usually overlooked in the analysis of PCa and body fluids. Combined analysis of mass spectrometry data using two different software packages was performed for the first time in the context of PCa, which increased the robustness of the data analysis. The application of proteogenomics to urine proteomic analysis can be very enriching in mutation-related diseases such as cancer.

摘要

为了确定用于前列腺癌检测的新蛋白质靶点,首先进行了一项鸟枪法发现实验,以表征前列腺癌患者的尿液蛋白质组。这揭示了前列腺癌患者中有18种尿液蛋白质丰度存在差异。其次,通过免疫印迹对选定的靶点进行临床测试,首次在前列腺癌患者的尿液中检测到可溶性E-钙黏蛋白片段。第三,对这些前列腺癌患者的蛋白质基因组图谱进行了表征,揭示了1665种突变蛋白异构体。统计分析显示前列腺癌患者中有6种突变蛋白异构体丰度存在差异。对突变对蛋白质功能的可能影响以及涉及失调突变蛋白异构体的蛋白质-蛋白质相互作用的分析表明,突变HSPG2Q1062H和VASNR161Q在前列腺癌患者中具有保护作用,而AMBPA286G和CD55S162L具有不利作用。这项工作最初对尿液蛋白质组进行了表征,重点关注前列腺癌患者的蛋白质基因组图谱,这在前列腺癌和体液分析中通常被忽视。在前列腺癌的背景下,首次使用两个不同的软件包对质谱数据进行了联合分析,这提高了数据分析的稳健性。蛋白质基因组学在尿液蛋白质组分析中的应用对于癌症等与突变相关的疾病可能非常有意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/5bf67aefab78/cancers-14-02001-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/9dff5ad6d601/cancers-14-02001-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/4def568920f9/cancers-14-02001-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/52ec383dfb05/cancers-14-02001-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/86adb2c57f83/cancers-14-02001-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/5bf67aefab78/cancers-14-02001-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/9dff5ad6d601/cancers-14-02001-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/4def568920f9/cancers-14-02001-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/52ec383dfb05/cancers-14-02001-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/86adb2c57f83/cancers-14-02001-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21be/9031064/5bf67aefab78/cancers-14-02001-g005.jpg

相似文献

1
Application of Proteogenomics to Urine Analysis towards the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study.蛋白质基因组学在尿液分析中用于鉴定前列腺癌新型生物标志物的应用:一项探索性研究。
Cancers (Basel). 2022 Apr 15;14(8):2001. doi: 10.3390/cancers14082001.
2
Comparative Proteome Profiling and Mutant Protein Identification in Metastatic Prostate Cancer Cells by Quantitative Mass Spectrometry-based Proteogenomics.基于定量质谱的蛋白质基因组学在转移性前列腺癌细胞中的比较蛋白质组学分析和突变蛋白鉴定。
Cancer Genomics Proteomics. 2019 Jul-Aug;16(4):273-286. doi: 10.21873/cgp.20132.
3
Editorial: The Application of Proteogenomics to Urine Analysis for the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study.社论:蛋白质基因组学在尿液分析中用于鉴定前列腺癌新型生物标志物的应用:一项探索性研究。
Cancers (Basel). 2023 Aug 17;15(16):4143. doi: 10.3390/cancers15164143.
4
Bioinformatic analysis of dysregulated proteins in prostate cancer patients reveals putative urinary biomarkers and key biological pathways.前列腺癌患者失调蛋白的生物信息学分析揭示了潜在的尿生物标志物和关键生物学途径。
Med Oncol. 2021 Jan 16;38(1):9. doi: 10.1007/s12032-021-01461-6.
5
Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies.前列腺癌细胞外囊泡蛋白质组学:质谱研究的比较分析。
Int J Mol Sci. 2021 Dec 19;22(24):13605. doi: 10.3390/ijms222413605.
6
The Proteome of Primary Prostate Cancer.原发性前列腺癌的蛋白质组。
Eur Urol. 2016 May;69(5):942-52. doi: 10.1016/j.eururo.2015.10.053. Epub 2015 Dec 2.
7
Combined serum and EPS-urine proteomic analysis using iTRAQ technology for discovery of potential prostate cancer biomarkers.使用iTRAQ技术对血清和前列腺液-尿液进行联合蛋白质组学分析以发现潜在的前列腺癌生物标志物。
Discov Med. 2016 Nov;22(122):281-295.
8
In-depth proteomic analyses of exosomes isolated from expressed prostatic secretions in urine.从尿液中提取的前列腺分泌物外泌体的深入蛋白质组学分析。
Proteomics. 2013 May;13(10-11):1667-1671. doi: 10.1002/pmic.201200561. Epub 2013 Apr 23.
9
More advantages in detecting bone and soft tissue metastases from prostate cancer using F-PSMA PET/CT.使用F-PSMA PET/CT检测前列腺癌骨和软组织转移方面有更多优势。
Hell J Nucl Med. 2019 Jan-Apr;22(1):6-9. doi: 10.1967/s002449910952. Epub 2019 Mar 7.
10
Comparative Secretome Profiling and Mutant Protein Identification in Metastatic Prostate Cancer Cells by Quantitative Mass Spectrometry-based Proteomics.基于定量质谱蛋白质组学的转移性前列腺癌细胞比较分泌蛋白质组分析及突变蛋白鉴定
Cancer Genomics Proteomics. 2018 Jul-Aug;15(4):279-290. doi: 10.21873/cgp.20086.

引用本文的文献

1
The extracellular matrix component perlecan/HSPG2 regulates radioresistance in prostate cancer cells.细胞外基质成分基底膜聚糖/硫酸乙酰肝素蛋白聚糖2调节前列腺癌细胞的放射抗性。
Front Cell Dev Biol. 2024 Aug 1;12:1452463. doi: 10.3389/fcell.2024.1452463. eCollection 2024.
2
Urine biomarkers can predict prostate cancer and PI-RADS score prior to biopsy.尿液生物标志物可预测前列腺癌和活检前的 PI-RADS 评分。
Sci Rep. 2024 Aug 5;14(1):18148. doi: 10.1038/s41598-024-68026-1.
3
Recent progress in mass spectrometry-based urinary proteomics.基于质谱的尿液蛋白质组学的最新进展。

本文引用的文献

1
TGF-β Signaling and Resistance to Cancer Therapy.转化生长因子-β信号传导与癌症治疗耐药性
Front Cell Dev Biol. 2021 Nov 30;9:786728. doi: 10.3389/fcell.2021.786728. eCollection 2021.
2
Mass spectrometry-based targeted proteomics for analysis of protein mutations.基于质谱的靶向蛋白质组学分析蛋白质突变。
Mass Spectrom Rev. 2023 Mar;42(2):796-821. doi: 10.1002/mas.21741. Epub 2021 Oct 31.
3
Multifunctionality of prostatic acid phosphatase in prostate cancer pathogenesis.前列腺酸性磷酸酶在前列腺癌发病机制中的多功能性。
Clin Proteomics. 2024 Feb 22;21(1):14. doi: 10.1186/s12014-024-09462-z.
4
The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery.细胞外基质和整合素黏附复合体相关分子在前列腺癌生物标志物发现中的潜力
Biomedicines. 2023 Dec 28;12(1):79. doi: 10.3390/biomedicines12010079.
5
Editorial: The Application of Proteogenomics to Urine Analysis for the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study.社论:蛋白质基因组学在尿液分析中用于鉴定前列腺癌新型生物标志物的应用:一项探索性研究。
Cancers (Basel). 2023 Aug 17;15(16):4143. doi: 10.3390/cancers15164143.
6
Tracking Prostate Carcinogenesis over Time through Urine Proteome Profiling in an Animal Model: An Exploratory Approach.通过动物模型中的尿液蛋白质组谱分析追踪前列腺癌的发生发展:一种探索性方法。
Int J Mol Sci. 2022 Jul 8;23(14):7560. doi: 10.3390/ijms23147560.
7
Mutation Association with Immune Checkpoint Inhibitor Outcome in Melanoma and Non-Small Cell Lung Cancer.黑色素瘤和非小细胞肺癌中与免疫检查点抑制剂疗效相关的突变
Cancers (Basel). 2022 Jul 19;14(14):3495. doi: 10.3390/cancers14143495.
Biosci Rep. 2021 Oct 29;41(10). doi: 10.1042/BSR20211646.
4
Association of Urinary MyProstateScore, Age, and Prostate Volume in a Longitudinal Cohort of Healthy Men: Long-term Findings from the Olmsted County Study.健康男性纵向队列中尿 MyProstateScore、年龄和前列腺体积的关联:奥姆斯特德县研究的长期结果
Eur Urol Open Sci. 2021 May 25;29:30-35. doi: 10.1016/j.euros.2021.04.009. eCollection 2021 Jul.
5
MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.MetaboAnalyst 5.0:缩小原始光谱与功能见解之间的差距。
Nucleic Acids Res. 2021 Jul 2;49(W1):W388-W396. doi: 10.1093/nar/gkab382.
6
Clinicopathological and prognostic significance of osteopontin expression in patients with prostate cancer: a systematic review and meta-analysis.在前列腺癌患者中骨桥蛋白表达的临床病理和预后意义:系统评价和荟萃分析。
Biosci Rep. 2021 Aug 27;41(8). doi: 10.1042/BSR20203531.
7
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.《全球癌症统计数据 2020:全球 185 个国家和地区 36 种癌症的发病率和死亡率估计》。
CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
8
Bioinformatic analysis of dysregulated proteins in prostate cancer patients reveals putative urinary biomarkers and key biological pathways.前列腺癌患者失调蛋白的生物信息学分析揭示了潜在的尿生物标志物和关键生物学途径。
Med Oncol. 2021 Jan 16;38(1):9. doi: 10.1007/s12032-021-01461-6.
9
The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets.2021 年的 STRING 数据库:可定制的蛋白质-蛋白质网络,以及用户上传的基因/测量集的功能特征分析。
Nucleic Acids Res. 2021 Jan 8;49(D1):D605-D612. doi: 10.1093/nar/gkaa1074.
10
SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein-protein binding affinity.SAAMBE-SEQ:一种基于序列的方法,用于预测突变对蛋白质-蛋白质结合亲和力的影响。
Bioinformatics. 2021 May 17;37(7):992-999. doi: 10.1093/bioinformatics/btaa761.