基于信息论的基因网络模式分析揭示乳腺癌原发肿瘤和淋巴结首次转移的主要基因

IF 3.9 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Letters Pub Date : 2024-10-01 DOI:10.1016/j.patrec.2024.07.006
Irving Ulises Martínez Vargas , Moises Omar León Pineda , Matías Alvarado Mentado
{"title":"基于信息论的基因网络模式分析揭示乳腺癌原发肿瘤和淋巴结首次转移的主要基因","authors":"Irving Ulises Martínez Vargas ,&nbsp;Moises Omar León Pineda ,&nbsp;Matías Alvarado Mentado","doi":"10.1016/j.patrec.2024.07.006","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, we use pattern analysis in genetic networks to identify differentially expressed genes in primary breast cancer tumors and their first metastasis in lymph nodes, using human biopsies from the GEO and GDCDP databases. By applying Information-Theory-based algorithms to process gene expression profile matrices, we obtained the genetic networks of the following tissues: (1) breast cancer-free, (2) primary breast cancer tumors, and (3) first metastasis of breast cancer in lymph nodes. Topological analysis of the genetic networks delves for identifying patterns of pairs of genes with higher mutual information than a threshold; then, among these genes, the ones with highest degree are elected. We propose the plausible hypothesis that the elected genes, having principal roles in each network, could be relevant as biomarkers regarding the genetic information. A subsequent gene ontology-based analysis of the molecular and functional characteristics of these genes reveals specific signaling pathways signatures in cancer-free tissue and in the tumor microenvironment associated with primary and metastatic requirements. Furthermore, a state-of-the-art review of the functional roles of genes reveals tumor suppressor genes in cancer-free tissue and proliferation- and migration-associated genes in cancer.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"186 ","pages":"Pages 369-376"},"PeriodicalIF":3.9000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Main genes in breast cancer primary tumor and first metastasis in lymph nodes revealed by information-theory-based genetic networks pattern analysis\",\"authors\":\"Irving Ulises Martínez Vargas ,&nbsp;Moises Omar León Pineda ,&nbsp;Matías Alvarado Mentado\",\"doi\":\"10.1016/j.patrec.2024.07.006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this paper, we use pattern analysis in genetic networks to identify differentially expressed genes in primary breast cancer tumors and their first metastasis in lymph nodes, using human biopsies from the GEO and GDCDP databases. By applying Information-Theory-based algorithms to process gene expression profile matrices, we obtained the genetic networks of the following tissues: (1) breast cancer-free, (2) primary breast cancer tumors, and (3) first metastasis of breast cancer in lymph nodes. Topological analysis of the genetic networks delves for identifying patterns of pairs of genes with higher mutual information than a threshold; then, among these genes, the ones with highest degree are elected. We propose the plausible hypothesis that the elected genes, having principal roles in each network, could be relevant as biomarkers regarding the genetic information. A subsequent gene ontology-based analysis of the molecular and functional characteristics of these genes reveals specific signaling pathways signatures in cancer-free tissue and in the tumor microenvironment associated with primary and metastatic requirements. Furthermore, a state-of-the-art review of the functional roles of genes reveals tumor suppressor genes in cancer-free tissue and proliferation- and migration-associated genes in cancer.</div></div>\",\"PeriodicalId\":54638,\"journal\":{\"name\":\"Pattern Recognition Letters\",\"volume\":\"186 \",\"pages\":\"Pages 369-376\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Recognition Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167865524002095\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167865524002095","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

在本文中,我们使用遗传网络中的模式分析来识别原发性乳腺癌肿瘤及其淋巴结首次转移的差异表达基因,使用来自GEO和GDCDP数据库的人体活检。通过应用基于信息理论的算法处理基因表达谱矩阵,我们获得了以下组织的遗传网络:(1)无乳腺癌,(2)原发性乳腺癌肿瘤,(3)乳腺癌在淋巴结的首次转移。遗传网络的拓扑分析探讨了识别具有更高互信息的基因对的模式;然后,在这些基因中,选择度最高的基因。我们提出了一个合理的假设,即选择的基因在每个网络中都起着主要作用,可以作为遗传信息的生物标志物。随后对这些基因的分子和功能特征进行了基于基因本体论的分析,揭示了无癌组织和肿瘤微环境中与原发性和转移性要求相关的特定信号通路特征。此外,对基因功能作用的最新回顾揭示了肿瘤抑制基因在无癌组织中的作用以及肿瘤中增殖和迁移相关基因的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Main genes in breast cancer primary tumor and first metastasis in lymph nodes revealed by information-theory-based genetic networks pattern analysis
In this paper, we use pattern analysis in genetic networks to identify differentially expressed genes in primary breast cancer tumors and their first metastasis in lymph nodes, using human biopsies from the GEO and GDCDP databases. By applying Information-Theory-based algorithms to process gene expression profile matrices, we obtained the genetic networks of the following tissues: (1) breast cancer-free, (2) primary breast cancer tumors, and (3) first metastasis of breast cancer in lymph nodes. Topological analysis of the genetic networks delves for identifying patterns of pairs of genes with higher mutual information than a threshold; then, among these genes, the ones with highest degree are elected. We propose the plausible hypothesis that the elected genes, having principal roles in each network, could be relevant as biomarkers regarding the genetic information. A subsequent gene ontology-based analysis of the molecular and functional characteristics of these genes reveals specific signaling pathways signatures in cancer-free tissue and in the tumor microenvironment associated with primary and metastatic requirements. Furthermore, a state-of-the-art review of the functional roles of genes reveals tumor suppressor genes in cancer-free tissue and proliferation- and migration-associated genes in cancer.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Pattern Recognition Letters
Pattern Recognition Letters 工程技术-计算机:人工智能
CiteScore
12.40
自引率
5.90%
发文量
287
审稿时长
9.1 months
期刊介绍: Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition. Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.
期刊最新文献
Bilateral symmetry-based augmentation method for improved tooth segmentation in panoramic X-rays GAF-Net: A new automated segmentation method based on multiscale feature fusion and feedback module Segmentation of MRI tumors and pelvic anatomy via cGAN-synthesized data and attention-enhanced U-Net Multichannel image classification based on adaptive attribute profiles Incremental component tree contour computation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1