Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples.
Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples.
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从癌症样本的横截面转录组数据推断潜在的时间进展和调控网络
DOI：
10.1371/journal.pcbi.1008379
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Nie Q
中科院分区：
文献类型：
--
作者：
Sun X;Zhang J;Nie Q
解开疾病进展背后的分子调控网络对于理解疾病机制和确定药物靶点至关重要。现有的基因调控网络（GRNs）的推断方法主要依赖于时间过程的基因表达数据。然而，大多数来自癌症患者横断面研究的组学数据往往缺乏足够的时间信息，这给GRN推断带来了关键挑战。通过使用基于随机游走的流形距离量化潜在进展，我们提出了一种基于潜在时间进展的贝叶斯方法，PROB，用于从肿瘤样本的横截面转录组数据中推断GRNs。从数学上证明了PROB算法对数据测量变异性的鲁棒性，并进行了数值验证。对真实的数据的性能评估表明，PROB在伪时间推理和GRN推理方面都优于其他方法。膀胱癌和乳腺癌的应用表明，我们的方法可以有效地识别癌症进展或药物靶点的关键调节因子。经实验验证，所鉴定的ACSS 1可促进膀胱癌细胞的上皮向间质转化，并且预测的FOXM 1-靶标相互作用得到验证，并可预测乳腺癌的复发。我们的研究为临床转录组数据建模提供了新的有效方法，用于表征癌症进展，并促进了基于调控网络的方法转化为精准医学。基因调控网络的重建是系统生物学研究的重要课题。基于样本的转录组数据中缺乏时间信息，这导致了推断GRN及其向精准医学的转化面临重大挑战。为了解决上述挑战，我们建议通过基于转录组相似性对患者样本进行排序来解码潜在的时间信息，并设计一种基于潜在时间进展的贝叶斯方法来从基于样本的癌症患者转录组数据中推断GRNs。我们的方法的优点包括它的能力，推断因果GRNs（有向和有符号的边缘）和它的鲁棒性的测量数据的变化。仿真数据和真实的数据的性能评估表明，我们的方法优于其他现有的方法在伪时间推理和GRN推理。然后，我们的方法被应用于重建膀胱癌中的EMT调控网络，并确定乳腺癌进展的关键调控因子。重要的是，预测的关键调控因子/相互作用是经过实验验证的。我们的研究表明，从肿瘤样本的静态表达数据推断动态进展轨迹有助于揭示癌症进展的调控机制，并发现可能用作候选药物靶点的关键调控因子。
Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine. Reconstructing gene regulatory network (GRN) is an essential question in systems biology. The lack of temporal information in sample-based transcriptomic data leads to a major challenge for inferring GRN and its translation to precision medicine. To address the above challenge, we propose to decode the latent temporal information underlying cancer progression via ordering patient samples based on transcriptomic similarity, and design a latent-temporal progression-based Bayesian method to infer GRNs from sample-based transcriptomic data of cancer patients. The advantages of our method include its capability to infer causal GRNs (with directed and signed edges) and its robustness to the measurement variability in the data. Performance evaluation using both simulated data and real data demonstrate that our method outperforms other existing methods in both pseudotime inference and GRN inference. Our method is then applied to reconstruct EMT regulatory networks in bladder cancer and to identify key regulators underlying progression of breast cancer. Importantly, the predicted key regulators/interactions are experimentally validated. Our study suggests that inferring dynamic progression trajectory from static expression data of tumor samples helps to uncover regulatory mechanisms underlying cancer progression and to discovery key regulators which may be used as candidate drug targets.
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DOI：
10.1109/tcbb.2012.169
发表时间：
2013-01-01
影响因子：
4.5
作者：
Higa, Carlos H. A.;Andrade, Tales P.;Hashimoto, Ronaldo F.
通讯作者：
Hashimoto, Ronaldo F.
DOI：
10.1126/science.1179050
发表时间：
2009-10-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Amit I;Garber M;Chevrier N;Leite AP;Donner Y;Eisenhaure T;Guttman M;Grenier JK;Li W;Zuk O;Schubert LA;Birditt B;Shay T;Goren A;Zhang X;Smith Z;Deering R;McDonald RC;Cabili M;Bernstein BE;Rinn JL;Meissner A;Root DE;Hacohen N;Regev A
通讯作者：
Regev A
影响因子：
9.5
作者：
Kim, Yongsoo;Han, Seungmin;Hwang, Daehee
通讯作者：
Hwang, Daehee
影响因子：
8.8
作者：
Guo, Charles C.;Majewski, Tadeusz;Czerniak, Bogdan
通讯作者：
Czerniak, Bogdan
影响因子：
14.9
作者：
Han H;Cho JW;Lee S;Yun A;Kim H;Bae D;Yang S;Kim CY;Lee M;Kim E;Lee S;Kang B;Jeong D;Kim Y;Jeon HN;Jung H;Nam S;Chung M;Kim JH;Lee I
通讯作者：
Lee I