Computational single-cell methods for predicting cancer risk.

IF 3.8 3区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Biochemical Society transactions Pub Date : 2024-06-26 DOI:10.1042/BST20231488
Andrew E Teschendorff
{"title":"Computational single-cell methods for predicting cancer risk.","authors":"Andrew E Teschendorff","doi":"10.1042/BST20231488","DOIUrl":null,"url":null,"abstract":"<p><p>Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.</p>","PeriodicalId":8841,"journal":{"name":"Biochemical Society transactions","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochemical Society transactions","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1042/BST20231488","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.

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
预测癌症风险的单细胞计算方法。
尽管最近在生物技术方面取得了突破,但癌症风险预测仍然是一项艰巨的计算和实验挑战。要提高预防、早期检测和存活率,解决这一问题至关重要。在此,我简要总结了一些新出现的关键理论和计算挑战,以及有望帮助实现癌症风险预测目标的最新计算进展。重点是基于单细胞数据的计算策略,特别是自下而上的网络建模方法,这些方法旨在从系统生物学的角度以单细胞分辨率估算癌症干性和去分化。我将介绍两种很有前景的方法,一种是基于扩散网络熵概念的独立于组织和细胞系的方法,另一种是利用转录因子调控子的组织和细胞系特异性方法。将这些工具应用于侵袭性癌症前各阶段的单细胞和单核 RNA-seq 数据显示,它们可以成功地勾勒出细胞间的异质性癌症风险图谱,识别出那些更有可能转变成癌症的细胞。对单细胞奥米克数据进行自下而上的系统生物学建模是一种新的计算分析范式,有望促进预防、早期检测和癌症风险预测策略的开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Biochemical Society transactions
Biochemical Society transactions 生物-生化与分子生物学
CiteScore
7.80
自引率
0.00%
发文量
351
审稿时长
3-6 weeks
期刊介绍: Biochemical Society Transactions is the reviews journal of the Biochemical Society. Publishing concise reviews written by experts in the field, providing a timely snapshot of the latest developments across all areas of the molecular and cellular biosciences. Elevating our authors’ ideas and expertise, each review includes a perspectives section where authors offer comment on the latest advances, a glimpse of future challenges and highlighting the importance of associated research areas in far broader contexts.
期刊最新文献
How does CHD4 slide nucleosomes? Progress towards understanding risk factor mechanisms in the development of autism spectrum disorders. Suppression of double-stranded RNA sensing in cancer: molecular mechanisms and therapeutic potential. Human E3 ubiquitin ligases: accelerators and brakes for SARS-CoV-2 infection. Histone H3 mutations and their impact on genome stability maintenance.
×
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