• 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

Deep proteome profiling promotes whole proteome characterization and drug discovery for esophageal squamous cell carcinoma.

作者信息

Liu Wei, Cui Yongping, Liu Wen, Liu Zhihua, Xu Liyan, Li Enmin

机构信息

College of Science, Heilongjiang Institute of Technology, Harbin 150050, China.

Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, the Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China.

出版信息

Cancer Biol Med. 2022 Mar 14;19(3):273-7. doi: 10.20892/j.issn.2095-3941.2022.0024.

DOI:10.20892/j.issn.2095-3941.2022.0024
PMID:35289157
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8958891/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f21d/8958891/6973fe2713a1/cbm-19-273-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f21d/8958891/6973fe2713a1/cbm-19-273-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f21d/8958891/6973fe2713a1/cbm-19-273-g001.jpg

相似文献

1
Deep proteome profiling promotes whole proteome characterization and drug discovery for esophageal squamous cell carcinoma.深度蛋白质组分析促进食管鳞状细胞癌的全蛋白质组表征和药物发现。
Cancer Biol Med. 2022 Mar 14;19(3):273-7. doi: 10.20892/j.issn.2095-3941.2022.0024.
2
Integrative analysis of saliva-derived exosomal proteome and lipidome for the diagnosis of esophageal squamous cell carcinoma.用于食管癌诊断的唾液来源外泌体蛋白质组和脂质组的综合分析
BMC Cancer. 2025 Aug 1;25(1):1254. doi: 10.1186/s12885-025-14452-x.
3
A novel staging system based on deep learning for overall survival in patients with esophageal squamous cell carcinoma.一种基于深度学习的用于预测食管鳞癌患者总生存的新分期系统。
J Cancer Res Clin Oncol. 2023 Sep;149(11):8935-8944. doi: 10.1007/s00432-023-04842-8. Epub 2023 May 8.
4
Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.基于深度学习的食管鳞状细胞癌淋巴结转移增强CT扫描预测
Jpn J Radiol. 2025 Apr 11. doi: 10.1007/s11604-025-01780-y.
5
ESCCPred: a machine learning model for diagnostic prediction of early esophageal squamous cell carcinoma using autoantibody profiles.ESCCPred：一种基于自身抗体谱的早期食管鳞状细胞癌诊断预测的机器学习模型。
Br J Cancer. 2024 Sep;131(5):883-894. doi: 10.1038/s41416-024-02781-w. Epub 2024 Jul 2.
6
Investigating the molecular mechanisms, drug prediction, and validation of CCNA2 and MAD2L1 in esophageal squamous cell carcinoma based on bioinformatics.基于生物信息学研究CCNA2和MAD2L1在食管鳞状细胞癌中的分子机制、药物预测及验证
BMC Med Genomics. 2025 Jul 1;18(1):107. doi: 10.1186/s12920-025-02173-w.
7
Simultaneous concurrent chemoradiotherapy and esophagectomy for synchronous head and neck and esophageal squamous cell carcinoma: a retrospective review.同步放化疗与食管切除术治疗同步性头颈部和食管鳞状细胞癌：一项回顾性研究。
Radiat Oncol. 2025 Jul 9;20(1):107. doi: 10.1186/s13014-025-02681-0.
8
Endoscopic imaging modalities for diagnosing invasion depth of superficial esophageal squamous cell carcinoma: a systematic review and meta-analysis.用于诊断浅表性食管鳞状细胞癌浸润深度的内镜成像方式：一项系统评价和荟萃分析。
BMC Gastroenterol. 2017 Feb 2;17(1):24. doi: 10.1186/s12876-017-0574-0.
9
Fibrinogen promotes malignant biological tumor behavior involving epithelial-mesenchymal transition via the p-AKT/p-mTOR pathway in esophageal squamous cell carcinoma.在食管鳞状细胞癌中，纤维蛋白原通过p-AKT/p-mTOR途径促进涉及上皮-间质转化的恶性肿瘤生物学行为。
J Cancer Res Clin Oncol. 2017 Dec;143(12):2413-2424. doi: 10.1007/s00432-017-2493-4. Epub 2017 Aug 11.
10
Esophageal achalasia: a risk factor for carcinoma. A systematic review and meta-analysis.食管贲门失弛缓症：一种癌症风险因素。系统评价与荟萃分析。
Dis Esophagus. 2017 Oct 1;30(10):1-8. doi: 10.1093/dote/dox072.

本文引用的文献

1
Phosphoproteomics reveals therapeutic targets of esophageal squamous cell carcinoma.磷酸化蛋白质组学揭示食管鳞状细胞癌的治疗靶点。
Signal Transduct Target Ther. 2021 Nov 12;6(1):381. doi: 10.1038/s41392-021-00682-5.
2
Mutational signatures in esophageal squamous cell carcinoma from eight countries with varying incidence.来自八个发病率不同国家的食管鳞癌中的突变特征。
Nat Genet. 2021 Nov;53(11):1553-1563. doi: 10.1038/s41588-021-00928-6. Epub 2021 Oct 18.
3
A multi-omics study delineates new molecular features and therapeutic targets for esophageal squamous cell carcinoma.
多组学研究描绘了食管鳞癌的新分子特征和治疗靶点。
Clin Transl Med. 2021 Sep;11(9):e538. doi: 10.1002/ctm2.538.
4
Large-scale and high-resolution mass spectrometry-based proteomics profiling defines molecular subtypes of esophageal cancer for therapeutic targeting.基于大规模和高分辨率质谱的蛋白质组学分析为治疗靶点定义了食管癌的分子亚型。
Nat Commun. 2021 Aug 16;12(1):4961. doi: 10.1038/s41467-021-25202-5.
5
Integrin-Linked Kinase Is Involved In the Proliferation and Invasion of Esophageal Squamous Cell Carcinoma.整合素连接激酶参与食管鳞状细胞癌的增殖和侵袭
J Cancer. 2020 Jan 1;11(2):324-333. doi: 10.7150/jca.33737. eCollection 2020.
6
Identification of prothymosin alpha (PTMA) as a biomarker for esophageal squamous cell carcinoma (ESCC) by label-free quantitative proteomics and Quantitative Dot Blot (QDB).通过无标记定量蛋白质组学和定量点杂交（QDB）鉴定原胸腺素α（PTMA）作为食管鳞状细胞癌（ESCC）的生物标志物。
Clin Proteomics. 2019 Apr 5;16:12. doi: 10.1186/s12014-019-9232-6. eCollection 2019.
7
Clinical potential of mass spectrometry-based proteogenomics.基于质谱的蛋白质基因组学的临床潜力。
Nat Rev Clin Oncol. 2019 Apr;16(4):256-268. doi: 10.1038/s41571-018-0135-7.
8
iTRAQ-Based Quantitative Proteomic Analyses of High Grade Esophageal Squamous Intraepithelial Neoplasia.基于iTRAQ的高级别食管鳞状上皮内瘤变定量蛋白质组学分析
Proteomics Clin Appl. 2017 Dec;11(11-12). doi: 10.1002/prca.201600167. Epub 2017 Sep 25.
9
Colorectal Cancer Cell Line Proteomes Are Representative of Primary Tumors and Predict Drug Sensitivity.结直肠癌细胞系蛋白质组可代表原发性肿瘤并预测药物敏感性。
Gastroenterology. 2017 Oct;153(4):1082-1095. doi: 10.1053/j.gastro.2017.06.008. Epub 2017 Jun 16.
10
iTRAQ-based quantitative proteomic analysis of esophageal squamous cell carcinoma.基于iTRAQ的食管鳞状细胞癌定量蛋白质组学分析
Tumour Biol. 2016 Feb;37(2):1909-18. doi: 10.1007/s13277-015-3840-1. Epub 2015 Sep 2.