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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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008379","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T13:45:21Z","timestamp":1614951921000},"page":"e1008379","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":27,"title":["Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3399-7260","authenticated-orcid":true,"given":"Xiaoqiang","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8804-3368","authenticated-orcid":true,"given":"Qing","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"issue":"1","key":"pcbi.1008379.ref001","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/nrg2918","article-title":"Network medicine: a network-based approach to human disease","volume":"12","author":"AL Barab\u00e1si","year":"2011","journal-title":"Nature reviews Genetics"},{"issue":"27","key":"pcbi.1008379.ref002","doi-asserted-by":"crossref","first-page":"7308","DOI":"10.1073\/pnas.1608845113","article-title":"Drawing causal inference from Big Data","volume":"113","author":"RM Shiffrin","year":"2016","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"pcbi.1008379.ref003","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1038\/nrg3885","article-title":"Quantitative and logic modelling of molecular and gene networks","volume":"16","author":"N. 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