Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.
Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.
复制标题
DOI：
10.1016/j.csbj.2022.02.019
复制
发表时间：
2022
影响因子：
6
通讯作者：
Ling F
中科院分区：
文献类型：
--
作者：
Han C;Zhong J;Zhang Q;Hu J;Liu R;Liu H;Mo Z;Chen P;Ling F
动态网络生物标记物(DNB)方法自提出以来得到了长足的发展。本文讨论了利用不同类型的转录组数据来识别与疾病进展的关键时间点相关的表达特征的动态变化的DNB方法的进展。DNB方法擅长识别癌症和其他疾病发展过程的潜在生物标记物，这些标记物表现为正常阶段和关键阶段之间有限的分子轮廓变化。我们强调，通过使用DNB方法，利用大宗或单细胞RNA测序数据，已广泛发现不同类型癌症的癌症临界点或癌前状态。这种方法也可以应用于其他动态研究，帮助识别预警信号，如新冠肺炎疫情爆发前的预测。我们还讨论了如何利用可靠的癌症生物标记物的识别和新方法的开发来进行早期检测和干预，并为进一步验证和疾病/健康管理提供对广泛存在的生物标记物候选库的新路径的洞察。
The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.
登录
查看更多内容
影响因子：
45.3
作者：
Dodd, Lori E.;Korn, Edward L.;Mooney, Margaret M.
通讯作者：
Mooney, Margaret M.
通过在AECOPD患者中将炎症介体与临床信息学相结合：一项初步研究，选择了疾病特异性的生物标志物。
影响因子：
5.3
作者：
Chen H;Song Z;Qian M;Bai C;Wang X
通讯作者：
Wang X
基于动态网络生物标志物检测复杂疾病的组织特异性早期预警信号：通过跨组织分析研究 2 型糖尿病
影响因子：
9.5
作者：
Li, Meiyi;Zeng, Tao;Chen, Luonan
通讯作者：
Chen, Luonan
SMAD7 和 SERPINE1 作为新型动态网络生物标志物检测和调节 TGF-β 诱导的 EMT 临界点
影响因子：
18.9
作者：
Jiang, Zhonglin;Lu, Lina;Chen, Luonan
通讯作者：
Chen, Luonan
通过动态网络生物标志物寻找乳腺癌内分泌抵抗过程的临界点
影响因子：
5.5
作者：
Liu R;Wang J;Ukai M;Sewon K;Chen P;Suzuki Y;Wang H;Aihara K;Okada-Hatakeyama M;Chen L
通讯作者：
Chen L