Identification of uveitis-associated functions based on the feature selection analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment scores

Abstract

Uveitis is a typical type of eye inflammation affecting the middle layer of eye (i.e., uvea layer) and can lead to blindness in middle-aged and young people. Therefore, a comprehensive study determining the disease susceptibility and the underlying mechanisms for uveitis initiation and progression is urgently needed for the development of effective treatments. In the present study, 108 uveitis-related genes are collected on the basis of literature mining, and 17,560 other human genes are collected from the Ensembl database, which are treated as non-uveitis genes. Uveitis- and non-uveitis-related genes are then encoded by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment scores based on the genes and their neighbors in STRING, resulting in 20,681 GO term features and 297 KEGG pathway features. Subsequently, we identify functions and biological processes that can distinguish uveitis-related genes from other human genes by using an integrated feature selection method, which incorporate feature filtering method (Boruta) and four feature importance assessment methods (i.e., LASSO, LightGBM, MCFS, and mRMR). Some essential GO terms and KEGG pathways related to uveitis, such as GO:0001841 (neural tube formation), has04612 (antigen processing and presentation in human beings), and GO:0043379 (memory T cell differentiation), are identified. The plausibility of the association of mined functional features with uveitis is verified on the basis of the literature. Overall, several advanced machine learning methods are used in the current study to uncover specific functions of uveitis and provide a theoretical foundation for the clinical treatment of uveitis.

Keywords: KEGG pathway; enrichment theory; feature selection; gene ontology; uveitis.

FIGURE 1

Flow chart of the whole analytical process. A total of 108 uveitis-associated genes and 17,560 other human genes are collected. Uveitis- and non-uveitis-related genes are then encoded by GO and KEGG enrichment scores, resulting in 20,681 GO term features and 297 KEGG pathway features. Boruta is used for feature filtering to obtain functional features related to uveitis. Subsequently, LASSO, LightGBM, MCFS, and mRMR are used to evaluate feature importance, and features are ranked from highest to lowest in terms of feature importance in four feature lists. Finally, highly relevant features were obtained by taking the intersection of the top 40 features in each feature list.

FIGURE 2

Distribution of top 40 features on GO terms and KEGG pathways in four feature lists. Features on GO terms are much more than those on KEGG pathways in each feature list.

FIGURE 3

Venn diagram of top 40 features selected by LASSO, LightGBM, MCFS, and mRMR methods. Overlapping circles indicate the features identified by multiple methods. Two features are ranked high by all four feature ranking algorithms.