Research points out that it is particularly important to comprehensively evaluate immune microenvironmental indicators and gene mutation characteristics to select the best treatment plan. Therefore, exploring the relevant genes of pulmonary injury is an important basis for the improvement of survival. In recent years, with the massive production of omics data, a large number of computational methods have been applied in the field of biomedicine. Most of these computational methods are devel-oped for a certain type of diseases or whole diseases. Algorithms that specifically identify genes associated with pulmonary injury have not yet been developed. To fill this gap, we developed a novel method, named AdaRVM, to identify pulmonary injury-related genes in large scale. AdaRVM is the fusion of Adaboost and Relevance Vector Machine (RVM) to achieve fast and high-precision pattern recognition of pulmonary injury genetic mechanism. AdaRVM found that Cavin-2 gene has strong potential to be related to pulmonary injury. As we known, the formation and function of Caveolae are mediated by two family proteins: Caveolin and Cavin. Many studies have explored the role of Caveolin proteins, but people still knew little about Cavin family members. To verify our method and reveal the functions of cavin-2, we integrated six genome-wide association studies (GWAS) data related to lung function traits, four expression Quantitative Trait Loci (eQTL) data, and one methylation Quantitative Trait Loci (mQTL) data by Summary data level Mendelian Randomization (SMR). We found strong relationship between cavin-2 and canonical signaling pathways ERK1/2, AKT, and STAT3 which are all known to be related to lung injury.
Keywords: adaboost; cavin-2; lung cancer; pulmonary injury; relevance vector machine.
Copyright © 2022 Li, Huang, Tang, Liu and Zhou.