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CancerPro：通过组合富集分析和知识网络洞察来解读泛癌预后格局。

CancerPro: deciphering the pan-cancer prognostic landscape through combinatorial enrichment analysis and knowledge network insights.

作者信息

Wang Zhigang, Yuan Yize, Wang Zhe, Zhang Wenjia, Chen Chong, Duan Zhaojun, Peng Suyuan, Zheng Jie, He Yongqun, Yang Xiaolin

机构信息

Department of Biomedical Engineering, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China.

Department of Immunology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China.

出版信息

NAR Genom Bioinform. 2024 Nov 21;6(4):lqae157. doi: 10.1093/nargab/lqae157. eCollection 2024 Dec.

DOI:10.1093/nargab/lqae157
PMID:39633722
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11616677/
Abstract

Gene expression levels serve as valuable markers for assessing prognosis in cancer patients. To understand the mechanisms underlying prognosis and explore potential therapeutics across diverse cancers, we developed CancerPro (https:/medcode.link/cancerpro). This knowledge network platform integrates comprehensive biomedical data on genes, drugs, diseases and pathways, along with their interactions. By integrating ontology and knowledge graph technologies, CancerPro offers a user-friendly interface for analyzing pan-cancer prognostic markers and exploring genes or drugs of interest. CancerPro implements three core functions: gene set enrichment analysis based on multiple annotations; in-depth drug analysis; and in-depth gene list analysis. Using CancerPro, we categorized genes and cancers into distinct groups and utilized network analysis to identify key biological pathways associated with unfavorable prognostic genes. The platform further pinpoints potential drug targets and explores potential links between prognostic markers and patient characteristics such as glutathione levels and obesity. For renal and prostate cancer, CancerPro identified risk genes linked to immune deficiency pathways and alternative splicing abnormalities. This research highlights CancerPro's potential as a valuable tool for researchers to explore pan-cancer prognostic markers and uncover novel therapeutic avenues. Its flexible tools support a wide range of biological investigations, making it a versatile asset in cancer research and beyond.

摘要

基因表达水平是评估癌症患者预后的重要标志物。为了了解预后的潜在机制并探索多种癌症的潜在治疗方法，我们开发了CancerPro（https:/medcode.link/cancerpro）。这个知识网络平台整合了关于基因、药物、疾病和通路的全面生物医学数据及其相互作用。通过整合本体论和知识图谱技术，CancerPro提供了一个用户友好的界面，用于分析泛癌预后标志物并探索感兴趣的基因或药物。CancerPro实现了三个核心功能：基于多种注释的基因集富集分析；深入的药物分析；以及深入的基因列表分析。使用CancerPro，我们将基因和癌症分类到不同的组中，并利用网络分析来识别与不良预后基因相关的关键生物学通路。该平台进一步确定了潜在的药物靶点，并探索了预后标志物与患者特征（如谷胱甘肽水平和肥胖）之间的潜在联系。对于肾癌和前列腺癌，CancerPro识别出了与免疫缺陷通路和可变剪接异常相关的风险基因。这项研究突出了CancerPro作为研究人员探索泛癌预后标志物和发现新治疗途径的宝贵工具的潜力。其灵活的工具支持广泛的生物学研究，使其成为癌症研究及其他领域的多功能资产。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/ec841388735d/lqae157fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/242047ba4357/lqae157fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/87846fa45d9a/lqae157fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/d7e387461490/lqae157fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/cb08722d3a95/lqae157fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/b1464745154f/lqae157fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/ec841388735d/lqae157fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/242047ba4357/lqae157fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/87846fa45d9a/lqae157fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/d7e387461490/lqae157fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/cb08722d3a95/lqae157fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/b1464745154f/lqae157fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75b/11616677/ec841388735d/lqae157fig6.jpg

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Mutation Burden Independently Predicts Survival in the Pan-Cancer Atlas.突变负担独立预测泛癌图谱中的生存情况。
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Body mass index and survival after cancer diagnosis: A pan-cancer cohort study of 114 430 patients with cancer.癌症诊断后的体重指数与生存率：一项对114430例癌症患者的全癌队列研究。
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Memo1 binds reduced copper ions, interacts with copper chaperone Atox1, and protects against copper-mediated redox activity in vitro.Memo1 结合还原态的铜离子，与铜伴侣蛋白 Atox1 相互作用，并在体外防止铜介导的氧化还原活性。
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SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.SynLethDB 2.0：一个基于网络的合成致死知识库，用于新型抗癌药物发现。
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