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Current Gene Therapy

Author(s): Changsheng Li, Hexiao Tang, Zetian Yang, Zheng Tang, Nitao Cheng, Jingyu Huang* and Xuefeng Zhou*

DOI: 10.2174/1566523222666220829140649

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Mechanism of CAV and CAVIN Family Genes in Acute Lung Injury based on DeepGENE

Page: [72 - 80] Pages: 9

  • * (Excluding Mailing and Handling)

Abstract

Background: The fatality rate of acute lung injury (ALI) is as high as 40% to 60%. Although various factors, such as sepsis, trauma, pneumonia, burns, blood transfusion, cardiopulmonary bypass, and pancreatitis, can induce ALI, patients with these risk factors will eventually develop ALI. The rate of developing ALI is not high, and the outcomes of ALI patients vary, indicating that it is related to genetic differences between individuals. In a previous study, we found multiple functions of cavin-2 in lung function. In addition, many other studies have revealed that CAV1 is a critical regulator of lung injury. Due to the strong relationship between cavin-2 and CAV1, we suspect that cavin-2 is also associated with ALI. Furthermore, we are curious about the role of the CAV family and cavin family genes in ALI.

Methods: To reveal the mechanism of CAV and CAVIN family genes in ALI, we propose DeepGENE to predict whether CAV and CAVIN family genes are associated with ALI. This method constructs a gene interaction network and extracts gene expression in 84 tissues. We divided these features into two groups and used two network encoders to encode and learn the features.

Results: Compared with DNN, GBDT, RF and KNN, the AUC of DeepGENE increased by 7.89%, 16.84%, 20.19% and 32.01%, respectively. The AUPR scores increased by 8.05%, 15.58%, 22.56% and 23.34%. DeepGENE shows that CAVIN-1, CAVIN-2, CAVIN-3 and CAV2 are related to ALI.

Conclusion: DeepGENE is a reliable method for identifying acute lung injury-related genes. Multiple CAV and CAVIN family genes are associated with acute lung injury-related genes through multiple pathways and gene functions.

Keywords: CAV family genes, CAVIN family genes, acute lung injury, deep learning, gene expression, gene network.

Graphical Abstract

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