液-液相分离(LLPS)是一种复杂而微妙的现象,其形成和调节在癌症发生中起着至关重要的作用。增长,programming,入侵,和转移。这个领域拥有大量未充分利用的非结构化数据,需要进一步挖掘潜在有价值的信息。因此,我们通过使用信息学方法(例如层次聚类,回归统计,突发热点,和Walktrap算法分析)。在过去的十年里,该领域享有良好的发展趋势(年增长率:34.98%)和全球合作(国际合作:27.31%)。通过基于机器学习的无监督层次聚类,全球研究热点分为五个主要研究集群:集群1(药物递送中相分离的作用和机制),簇2(基因表达调控中的相分离),簇3(RNA-蛋白质相互作用中的相分离),第4组(神经退行性疾病相分离对癌症研究的参考价值),和簇5(相分离的作用和机制)。进一步的时间序列分析表明,集群5是新兴的研究集群。此外,回归曲线和热点爆发分析点的结果一致,超级增强子(a=0.5515,R2=0.6586,p=0.0044)和应力颗粒(a=0.8000,R2=0.6000,p=0.0085)是该领域最有潜力的恒星分子。更有趣的是,基于随机行走策略的Walktrap算法进一步揭示了“相位分离,癌症,转录,超级增强剂,表观遗传学(相关性百分比[RP]=100%,发展百分比[DP]=29.2%),“应力颗粒,免疫疗法,肿瘤微环境,RNA结合蛋白(RP=79.2%,DP=33.3%)和“纳米粒子,凋亡\“(RP=70.8%,DP=25.0%)与该字段密切相关,但仍不发达,值得进一步探索。总之,这项研究描绘了全球科学格局,发现了一个重要的新兴研究集群,确定了几个关键的研究分子,并预测了几个关键但仍未开发的方向,值得进一步研究,为后续癌症相分离的基础和临床研究提供了重要的参考价值。
Liquid-liquid phase separation (LLPS) is a complex and subtle phenomenon whose formation and regulation take essential roles in cancer initiation, growth, progression, invasion, and metastasis. This domain holds a wealth of underutilized unstructured data that needs further excavation for potentially valuable information. Therefore, we retrospectively analyzed the global scientific knowledge in the field over the last decade by using informatics methods (such as hierarchical clustering, regression statistics, hotspot burst, and Walktrap algorithm analysis). Over the past decade, this area enjoyed a favorable development trend (Annual Growth Rate: 34.98%) and global collaboration (International Co-authorship: 27.31%). Through unsupervised hierarchical clustering based on machine learning, the global research hotspots were divided into five dominant research clusters: Cluster 1 (Effects and Mechanisms of Phase Separation in Drug Delivery), Cluster 2 (Phase Separation in Gene Expression Regulation), Cluster 3 (Phase Separation in RNA-Protein Interaction), Cluster 4 (Reference Value of Phase Separation in Neurodegenerative Diseases for Cancer Research), and Cluster 5 (Roles and Mechanisms of Phase Separation). And further time-series analysis revealed that Cluster 5 is the emerging research cluster. In addition, results from the regression curve and hotspot burst analysis point in unison to super-enhancer (a=0.5515, R2=0.6586, p=0.0044) and stress granule (a=0.8000, R2=0.6000, p=0.0085) as the most potential star molecule in this field. More interestingly, the Random-Walk-Strategy-based Walktrap algorithm further revealed that \"phase separation, cancer, transcription, super-enhancer, epigenetics\"(Relevance Percentage[RP]=100%, Development Percentage[DP]=29.2%), \"stress granule, immunotherapy, tumor microenvironment, RNA binding protein\"(RP=79.2%, DP=33.3%) and \"nanoparticle, apoptosis\"(RP=70.8%, DP=25.0%) are closely associated with this field, but are still under-developed and worthy of further exploration. In conclusion, this study profiled the global scientific landscape, discovered a crucial emerging research cluster, identified several pivotal research molecules, and predicted several crucial but still under-developed directions that deserve further research, providing an important reference value for subsequent basic and clinical research of phase separation in cancer.