Effects and potential mechanisms of IGF1/IGF1R in the liver fibrosis: A review

Gui, Ruirui; Li, Wanqiao; Li, Zhipeng; Wang, Hongbin; Wu, Yuchen; Jiao, Wenlin; Zhao, Gang; Shen, Yannan; Wang, Luping; Zhang, Jialu; Chen, Sihan; Hao, Linlin; Cheng, Yunyun

doi:10.1016/j.ijbiomac.2023.126263

Cited by 6 publications

(1 citation statement)

References 154 publications

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“…Furthermore, insulin-like growth factor 1 (IGF1) and its receptor IGF1R play a crucial role in liver health and function, primarily expressed in the liver tissue. Studies on liver fibrosis have revealed the core role of the IGF1/IGF1R signaling system in controlling the liver fibrosis process ( Gui et al, 2023 ). In the intra-cell signaling network of all three cell types identified by PathFinder, IGF1R is activated and further triggers target GNLY and HBEGF through FGFR3.…”

Section: Resultsmentioning

confidence: 99%

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications

Feng,

Song,

Province

et al. 2024

Front. Cell. Neurosci.

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Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer’s Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD, and the second is a human cirrhosis cohort. The evaluation confirms the promising potential of using PathFinder as a general signaling network inference model.

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Section: Resultsmentioning

confidence: 99%