Gene co‐expression network analyses in Mild cognitive impairment

Background Multiomic data analysis has been extensively developed in recent years. Particularly, microarray data can be used as biomarkers of disease of progression specially in pathologies as cancer, autoimmunity and others. (Dahinden et al., 2010; Soleimani Zakeri et al., 2020). This modality of data analysis can also be employed in other multifactorial diseases as Alzheimer’s disease including the progression from normal cognition to mild cognitive impairment and finally to dementia. These genes work in intricate relationship with each other. The establishment of computational gene interaction networks shows the intricacy of the biological systems but increases the complexity of the analysis, for this reason, the removal of redundant edges leaves a network backbone revealing important driver nodes that are essential for the observed interactions, reducing the number of genes of interest and narrowing potential diagnostic or therapeutic targets. (Gates et al., 2021). Method We used data from the ImaGENE study and the Weighted gene co‐expression network analysis (WGCNA) pipeline to analyze gene‐gene interactions of mRNA transcripts obtaining hierarchical clustering of genes and eigengenes in a sample of 160 individuals (Table 1). The backbones were obtained using the shortest path computation. (Gates et al., 2021; Simas et al., 2021). Gene ontology was used to classify processes associated with these genes. Result We identified 42 hubs of differentially expressed genes and we analyzed the top 5 groups with the strongest association with amnestic mild cognitive impairment (MCI) phenotype (pfdr