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This study proposes a patient quasi-potential landscape method to estimate the cancerous time of phenotypic-state emergence and uses it with mutations to identify 39 oncogenetic 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\n[https://doi.org/10.1093/bib/bbae520](https://doi.org/10.1093/bib/bbae520)  \nProblem Solving Protocol  \nCancerous time estimation for interpreting the evolution of lung adenocarcinoma  \nYourui Han1 ,‡, Bolin Chen1 , 2 ,‡, *, Jun Bian3 , Ruiming Kang4 , Xuequn Shang1 , 2  \n1 School of Computer Science, Northwestern Polytechnical University, Xi’an 710012, China  \n2 Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710012, China  \n3 Department of General Surgery, Xi’an Children’s Hospital, Xi’an Jiaotong University Affiliated Children’s Hospital, Xi’an 710003, China  \n4 Rewise (Hangzhou) Information Technology Co. , LTD, Hangzhou 310000, China  \n*[Corresponding author:](Corresponding author: blchen@nwpu.edu.cn)[ blchen@nwpu.edu.cn](Corresponding author: blchen@nwpu.edu.cn)  \n‡Yourui Han and Bolin Chen contributed equally.  \nAbstract  \nThe evolution of lung adenocarcinoma is accompanied by a multitude of gene mutations and dysfunctions, rendering its phenotypic state and evolutionary direction highly complex. To interpret the evolution of lung adenocarcinoma, various methods have been developed to elucidate the molecular pathogenesis and functional evolution processes. However, most of these methods are constrained by the absence of cancerous temporal information, and the challenges of heterogeneous characteristics. To handle these problems, in this study, a patient quasi-potential landscape method was proposed to estimate the cancerous time of phenotypic states’ emergence during the evolutionary process. Subsequently, a total of 39 different oncogenetic paths were identified based on cancerous time and mutations, reflecting the molecular pathogenesis of the evolutionary process of lung adenocarcinoma. To interpret the evolution patterns of lung adenocarcinoma, three oncogenetic graphs were obtained as the common evolutionary patterns by merging the oncogenetic paths. Moreover, patients were evenly re-divided into early, middle, and late evolutionary stages according to cancerous time, and a feasible framework was developed to construct the functional evolution network of lung adenocarcinoma. A total of six significant functional evolution processes were identified from the functional evolution network based on the pathway enrichment analysis, which plays critical roles in understanding the development of lung adenocarcinoma.  \nKeywords: cancerous time estimation; cancer evolution; oncogenetic graph; functional evolution process; lung adenocarcinoma.  \nIntroduction  \nLung adenocarcinoma is one of the most common subtypes of lung cancer. Like other cancers, the occurrence and development of lung adenocarcinoma exhibit a complex and gradual evolutionary process, where cancer cells continually adapt to and transform their environment after experiencing accidental mutations [1–3] . This high adaptability of cancer cells makes the traits exhibited by patients strongly heterogeneous, accompanied by multiple distinct pathological stages along their evolutionary processes [4] . Exploring the evolutionary process of lung adenocarcinoma is conducive to studying the molecular pathogenesis and functional evolution processes, which also promote the devel  \nopment of diagnosis biomarkers and therapeutic methods.  \nTo explore the molecular pathogenesis, some oncogenetic modeling methods have been developed for cross-sectional samples collected through biopsies of patients. These methods assume different individuals’ genetic alteration profiles are independent observations from the same multivariate stochastic process, and construct directed graphical models that reflect the dependencies or causalities between genetic alterations among the patient population. Various conceivable forms of oncogenetic graphs have been attempted, such as in the forms of (i) a tree  \n(e.g. oncotrees [5]); (ii) a forest str","cbCaimZiwoK1EkZ6","https://ap.wps.com/l/cbCaimZiwoK1EkZ6","pdf",4276182,14,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What problem does the study address about lung adenocarcinoma evolution analysis?\",\"answer\":\"Most existing oncogenetic methods do not explicitly incorporate cancer progression time and are limited by the lack of temporal information and heterogeneous patient characteristics.\"},{\"question\":\"What method is proposed to estimate when phenotypic states emerge?\",\"answer\":\"A patient quasi-potential landscape method is proposed to estimate the cancerous time of phenotypic-state emergence during the evolutionary process.\"},{\"question\":\"How are evolutionary patterns and functional evolution processes derived?\",\"answer\":\"Oncogenetic graphs are obtained by merging 39 identified oncogenetic paths, patients are re-divided into early, middle, and late stages by cancerous time, and six significant functional evolution processes are identified via pathway enrichment analysis.\"}]","Cancerous time estimation for interpreting the evolution of lung adenocarcinoma | PDF",1789982835,35]