Network-Guided Key Gene Discovery for a Given Cellular Process

Abstract

Identification of key genes for a given physiological or pathological process is an essential but still very challenging task for the entire biomedical research community. Statistics-based approaches, such as genome-wide association study (GWAS)- or quantitative trait locus (QTL)-related analysis have already made enormous contributions to identifying key genes associated with a given disease or phenotype, the success of which is however very much dependent on a huge number of samples. Recent advances in network biology, especially network inference directly from genome-scale data and the following-up network analysis, opens up new avenues to predict key genes driving a given biological process or cellular function. Here we review and compare the current approaches in predicting key genes, which have no chances to stand out by classic differential expression analysis, from gene-regulatory, protein-protein interaction, or gene expression correlation networks. We elaborate these network-based approaches mainly in the context of immunology and infection, and urge more usage of correlation network-based predictions. Such network-based key gene discovery approaches driven by information-enriched 'omics' data should be very useful for systematic key gene discoveries for any given biochemical process or cellular function, and also valuable for novel drug target discovery and novel diagnostic, prognostic and therapeutic-efficiency marker prediction for a specific disease or disorder.

Identification of VIMP as a gene inhibiting cytokine production in human CD4+ effector T cells

“…Surprisingly, the pathway enrichment analysis shows that the genes linked to VIMP are significantly enriched for components involved in the TCR signaling pathway (p value = 1.2 × 10 −3 , cumulative binomial distribution) ( Figure 2 A), suggesting a potential role of VIMP in the Teff response according to our network-based analysis strategy ( Danileviciute et al., 2019 ; He et al., 2012 ). However, the genes linked to the hub gene of interests in the correlation network could follow at least two scenarios ( He and Ollert, 2016 ; Langfelder and Horvath, 2008 ; van Dam et al., 2018 ). First, those genes could be co-regulated by chance with the hub gene and perform independent functions.…”

Identification of VIMP as a gene inhibiting cytokine production in human CD4+ effector T cells

“…2A), suggesting a potential role of VIMP in the Teff response according to our network-based analysis strategy (29,30). However, the genes linked to the hub gene of interests in the correlation network could follow at least two scenarios (32)(33)(34). First, those genes could simply be co-regulated with the hub gene, but perform independent functions.…”

Identification of VIMP as a Gene Inhibiting Cytokine Production in Human CD4+ Effector T Cells

“…Surprisingly, the pathway enrichment analysis shows that the genes linked to VIMP are significantly enriched for components involved in the TCR signaling pathway (p value = 1.2 3 10 À3 , cumulative binomial distribution) (Figure 2A), suggesting a potential role of VIMP in the Teff response according to our networkbased analysis strategy . However, the genes linked to the hub gene of interests in the correlation network could follow at least two scenarios (He and Ollert, 2016;Langfelder and Horvath, 2008;van Dam et al, 2018). First, those genes could be co-regulated by chance with the hub gene and perform independent functions.…”