• 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

用于识别与乳腺癌预后相关生物标志物的综合多组学分析模型

Integrated Multi-Omics Analysis Model to Identify Biomarkers Associated With Prognosis of Breast Cancer.

作者信息

Fan Yeye, Kao Chunyu, Yang Fu, Wang Fei, Yin Gengshen, Wang Yongjiu, He Yong, Ji Jiadong, Liu Liyuan

机构信息

School of Mathematics, Shandong University, Jinan, China.

Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, China.

出版信息

Front Oncol. 2022 Jun 10;12:899900. doi: 10.3389/fonc.2022.899900. eCollection 2022.

DOI:10.3389/fonc.2022.899900
PMID:35761863
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9232398/
Abstract

BACKGROUND

With the rapid development and wide application of high-throughput sequencing technology, biomedical research has entered the era of large-scale omics data. We aim to identify genes associated with breast cancer prognosis by integrating multi-omics data.

METHOD

Gene-gene interactions were taken into account, and we applied two differential network methods JDINAC and LGCDG to identify differential genes. The patients were divided into case and control groups according to their survival time. The TCGA and METABRIC database were used as the training and validation set respectively.

RESULT

In the TCGA dataset, C11orf1, OLA1, RPL31, SPDL1 and IL33 were identified to be associated with prognosis of breast cancer. In the METABRIC database, ZNF273, ZBTB37, TRIM52, TSGA10, ZNF727, TRAF2, TSPAN17, USP28 and ZNF519 were identified as hub genes. In addition, RPL31, TMEM163 and ZNF273 were screened out in both datasets. GO enrichment analysis shows that most of these hub genes were involved in zinc ion binding.

CONCLUSION

In this study, a total of 15 hub genes associated with long-term survival of breast cancer were identified, which can promote understanding of the molecular mechanism of breast cancer and provide new insight into clinical research and treatment.

摘要

背景

随着高通量测序技术的快速发展和广泛应用，生物医学研究已进入大规模组学数据时代。我们旨在通过整合多组学数据来鉴定与乳腺癌预后相关的基因。

方法

考虑基因-基因相互作用，我们应用两种差异网络方法JDINAC和LGCDG来鉴定差异基因。根据患者的生存时间将其分为病例组和对照组。分别将TCGA和METABRIC数据库用作训练集和验证集。

结果

在TCGA数据集中，鉴定出C11orf1、OLA1、RPL31、SPDL1和IL33与乳腺癌预后相关。在METABRIC数据库中，ZNF273、ZBTB37、TRIM52、TSGA10、ZNF727、TRAF2、TSPAN17、USP28和ZNF519被鉴定为枢纽基因。此外，在两个数据集中均筛选出RPL31、TMEM163和ZNF273。基因本体富集分析表明，这些枢纽基因大多参与锌离子结合。

结论

本研究共鉴定出15个与乳腺癌长期生存相关的枢纽基因，这有助于增进对乳腺癌分子机制的理解，并为临床研究和治疗提供新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/85f7aa50ae0d/fonc-12-899900-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/443fd4c36528/fonc-12-899900-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/c23de8f18937/fonc-12-899900-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/99ccab906d1b/fonc-12-899900-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/5386d267d6df/fonc-12-899900-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/9357ef363c81/fonc-12-899900-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/fde6779001f7/fonc-12-899900-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/07bd276ddf70/fonc-12-899900-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/200e2625558e/fonc-12-899900-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/85f7aa50ae0d/fonc-12-899900-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/443fd4c36528/fonc-12-899900-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/c23de8f18937/fonc-12-899900-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/99ccab906d1b/fonc-12-899900-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/5386d267d6df/fonc-12-899900-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/9357ef363c81/fonc-12-899900-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/fde6779001f7/fonc-12-899900-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/07bd276ddf70/fonc-12-899900-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/200e2625558e/fonc-12-899900-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec4e/9232398/85f7aa50ae0d/fonc-12-899900-g009.jpg

相似文献

1
Integrated Multi-Omics Analysis Model to Identify Biomarkers Associated With Prognosis of Breast Cancer.用于识别与乳腺癌预后相关生物标志物的综合多组学分析模型
Front Oncol. 2022 Jun 10;12:899900. doi: 10.3389/fonc.2022.899900. eCollection 2022.
2
Identification of a 6-gene signature for the survival prediction of breast cancer patients based on integrated multi-omics data analysis.基于整合多组学数据分析的乳腺癌患者生存预测的 6 基因标志物的鉴定。
PLoS One. 2020 Nov 10;15(11):e0241924. doi: 10.1371/journal.pone.0241924. eCollection 2020.
3
Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database.乳腺癌中免疫和基质相关基因：基于癌症基因组图谱（TCGA）数据库的肿瘤微环境综合分析
Front Med (Lausanne). 2020 Mar 5;7:64. doi: 10.3389/fmed.2020.00064. eCollection 2020.
4
JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.JDINAC：基于联合密度的非参数差异互作用网络分析和分类，用于高维稀疏组学数据。
Bioinformatics. 2017 Oct 1;33(19):3080-3087. doi: 10.1093/bioinformatics/btx360.
5
Integrating multi-omics data through deep learning for accurate cancer prognosis prediction.通过深度学习整合多组学数据，实现癌症预后的精准预测。
Comput Biol Med. 2021 Jul;134:104481. doi: 10.1016/j.compbiomed.2021.104481. Epub 2021 May 9.
6
Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis.利用整合多组学数据分析鉴定预测肺腺癌患者生存的七个基因标志物。
J Clin Lab Anal. 2022 Feb;36(2):e24190. doi: 10.1002/jcla.24190. Epub 2021 Dec 23.
7
Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.基于乳腺癌元维度组学数据间的相互作用预测删失生存数据。
J Biomed Inform. 2015 Aug;56:220-8. doi: 10.1016/j.jbi.2015.05.019. Epub 2015 Jun 3.
8
Integrated bioinformatics analysis to identify 15 hub genes in breast cancer.综合生物信息学分析以鉴定乳腺癌中的15个关键基因。
Oncol Lett. 2019 Aug;18(2):1023-1034. doi: 10.3892/ol.2019.10411. Epub 2019 May 30.
9
A Novel Signature Integrated of Immunoglobulin, Glycosylation and Anti-Viral Genes to Predict Prognosis for Breast Cancer.一种整合免疫球蛋白、糖基化和抗病毒基因的新型标志物用于预测乳腺癌预后
Front Genet. 2022 Apr 1;13:834731. doi: 10.3389/fgene.2022.834731. eCollection 2022.
10
Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis.基于整合生物信息学分析鉴定胃癌的枢纽基因并构建 mRNA-miRNA-lncRNA 网络
PLoS One. 2021 Dec 30;16(12):e0261728. doi: 10.1371/journal.pone.0261728. eCollection 2021.

引用本文的文献

1
Advancing precision and personalized breast cancer treatment through multi-omics technologies.通过多组学技术推进精准和个性化乳腺癌治疗。
Am J Cancer Res. 2024 Dec 15;14(12):5614-5627. doi: 10.62347/MWNZ5609. eCollection 2024.
2
The role of TMEM163 protein in thyroid microcarcinoma: expression pattern and clinical implications.跨膜蛋白163（TMEM163）蛋白在甲状腺微小癌中的作用：表达模式及临床意义
J Endocrinol Invest. 2025 Feb;48(2):303-315. doi: 10.1007/s40618-024-02434-y. Epub 2024 Oct 1.
3
Exploring TSGA10 Function: A Crosstalk or Controlling Mechanism in the Signaling Pathway of Carcinogenesis?

本文引用的文献

1
Classification of Breast Cancer Nottingham Prognostic Index Using High-Dimensional Embedding and Residual Neural Network.使用高维嵌入和残差神经网络对乳腺癌诺丁汉预后指数进行分类
Cancers (Basel). 2022 Feb 13;14(4):934. doi: 10.3390/cancers14040934.
2
The Incidence of Breast Cancer Recurrence 10-32 Years After Primary Diagnosis.乳腺癌患者首次诊断后 10-32 年内的复发情况。
J Natl Cancer Inst. 2022 Mar 8;114(3):391-399. doi: 10.1093/jnci/djab202.
3
Zinc Signaling in the Mammary Gland: For Better and for Worse.乳腺中的锌信号传导：利弊并存。
探索TSGA10的功能：致癌信号通路中的相互作用或调控机制？
Cancers (Basel). 2024 Aug 31;16(17):3044. doi: 10.3390/cancers16173044.
4
Nucleosome reorganisation in breast cancer tissues.乳腺癌组织中的核小体重排。
Clin Epigenetics. 2024 Apr 1;16(1):50. doi: 10.1186/s13148-024-01656-4.
5
Noxa inhibits oncogenesis through ZNF519 in gastric cancer and is suppressed by hsa-miR-200b-3p.Noxa 通过 ZNF519 抑制胃癌发生，受 hsa-miR-200b-3p 抑制。
Sci Rep. 2024 Mar 19;14(1):6568. doi: 10.1038/s41598-024-57099-7.
6
One-pot method for preparing DNA, RNA, and protein for multiomics analysis.一种用于多组学分析的 DNA、RNA 和蛋白质的一站式制备方法。
Commun Biol. 2024 Mar 14;7(1):324. doi: 10.1038/s42003-024-05993-1.
7
Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers.早期癌症检测的生物标志物：近期进展全景，聚焦胰腺癌和肝癌
ACS Pharmacol Transl Sci. 2024 Feb 14;7(3):586-613. doi: 10.1021/acsptsci.3c00346. eCollection 2024 Mar 8.
8
Construction of a prognostic 6-gene signature for breast cancer based on multi-omics and single-cell data.基于多组学和单细胞数据构建乳腺癌预后6基因特征。
Front Oncol. 2023 Nov 21;13:1186858. doi: 10.3389/fonc.2023.1186858. eCollection 2023.
9
Multi-omics reveal neuroprotection of Acer truncatum Bunge Seed extract on hypoxic-ischemia encephalopathy rats under high-altitude.多组学揭示了在高海拔环境下，接骨木提取物对缺氧缺血性脑病大鼠的神经保护作用。
Commun Biol. 2023 Oct 2;6(1):1001. doi: 10.1038/s42003-023-05341-9.
10
The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity.肿瘤微环境在耐药性中的作用：揭示乳腺癌异质性的新兴技术
Front Oncol. 2023 May 17;13:1170264. doi: 10.3389/fonc.2023.1170264. eCollection 2023.
Biomedicines. 2021 Sep 12;9(9):1204. doi: 10.3390/biomedicines9091204.
4
Combined homologous recombination repair deficiency and immune activation analysis for predicting intensified responses of anthracycline, cyclophosphamide and taxane chemotherapy in triple-negative breast cancer.联合同源重组修复缺陷和免疫激活分析预测三阴性乳腺癌中蒽环类、环磷酰胺和紫杉烷化疗的强化反应。
BMC Med. 2021 Sep 1;19(1):190. doi: 10.1186/s12916-021-02068-4.
5
DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.DeepProg：一种使用多组学数据进行预后预测的深度学习和机器学习模型的集成。
Genome Med. 2021 Jul 14;13(1):112. doi: 10.1186/s13073-021-00930-x.
6
A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs.一个整合多组学数据的机器学习框架预测癌症相关的长链非编码 RNA。
BMC Bioinformatics. 2021 Jun 16;22(1):332. doi: 10.1186/s12859-021-04256-8.
7
Frontiers of metal-coordinating drug design.金属配位药物设计前沿
Expert Opin Drug Discov. 2021 May;16(5):497-511. doi: 10.1080/17460441.2021.1851188. Epub 2020 Dec 1.
8
Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma.整合放射基因组学分析预测透明细胞肾细胞癌的分子特征和生存。
Aging (Albany NY). 2021 Mar 26;13(7):9960-9975. doi: 10.18632/aging.202752.
9
Using machine learning approaches for multi-omics data analysis: A review.使用机器学习方法进行多组学数据分析：综述
Biotechnol Adv. 2021 Jul-Aug;49:107739. doi: 10.1016/j.biotechadv.2021.107739. Epub 2021 Mar 29.
10
Efficient Multitask Multiple Kernel Learning With Application to Cancer Research.高效多任务多核学习及其在癌症研究中的应用。
IEEE Trans Cybern. 2022 Sep;52(9):8716-8728. doi: 10.1109/TCYB.2021.3052357. Epub 2022 Aug 18.