Identification of blood-based key biomarker and immune infiltration in Immunoglobulin A nephropathy by comprehensive bioinformatics analysis and a cohort validation

DA-SRN: Omics data analysis based on the sample network optimization for complex diseases

Effective biomarker identification and accurate sample label prediction are still challenging for complex diseases. Patient similarity network (PSN) analysis is a powerful tool in disease omics data analysis. The topology of PSN can reflect the discriminative ability of the corresponding feature space on which the sample network is built. In this study, a novel omics data analysis method based on the sample reference network (DA-SRN) is proposed to identify the potential biomarkers and predict the sample categories. DA-SRN defines the informative features and the sample reference network in optimizing the network structure by genetic algorithm. It labels the samples based on the graph neural network, the reference network and the selected informative features. DA-SRN was compared with nine efficient omics data analysis methods on the genomics, metabolomics and transcriptomics datasets to show its validation. The comparison results showed that it outperformed the other methods in area under receiver operating characteristic curve (AUROC), sensitivity, specificity and area under precision-recall curve (AUPRC) in most cases. Besides, the important metabolites identified by DA-SRN for the type 2 diabetes (T2D) metabolomics data were further examined. The pathway analysis revealed the close relationships between the identified metabolites and the critical metabolic pathways related to the occurrence and development of T2D. The experimental results illustrate that DA-SRN can extract the valuable information from the complex omics data by analyzing the sample relationship, and is promising in biomarker identification and sample discrimination for complex diseases.

Identification and Experimental Validation of Marker Genes between Diabetes and Alzheimer’s Disease

Currently, Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are widely prevalent in the elderly population, and accumulating evidence implies a strong link between them. For example, patients with T2DM have a higher risk of developing neurocognitive disorders, including AD, but the exact mechanisms are still unclear. This time, by combining bioinformatics analysis and in vivo experimental validation, we attempted to find a common biological link between AD and T2DM. We firstly downloaded the gene expression profiling (AD: GSE122063; T2DM: GSE161355) derived from the temporal cortex. To find the associations, differentially expressed genes (DEGs) of the two datasets were filtered and intersected. Based on them, enrichment analysis was carried out, and the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify the specific genes. After verifying in the external dataset and in the samples from the AD and type 2 diabetes animals, the shared targets of the two diseases were finally determined. Based on them, the ceRNA networks were constructed. Besides, the logistic regression and single-sample gene set enrichment analysis (ssGSEA) were performed. As a result, 62 DEGs were totally identified between AD and T2DM, and the enrichment analysis indicated that they were much related to the function of synaptic vesicle and MAPK signaling pathway. Based on the evidence from external dataset and RT-qPCR, CARTPT, EPHA5, and SERPINA3 were identified as the marker genes in both diseases, and their clinical significance and biological functions were further analyzed. In conclusion, discovering and exploring the marker genes that are dysregulated in both 2 diseases could help us better comprehend the intrinsic relationship between T2DM and AD, which may inspire us to develop new strategies for facing the dilemmas of clinical or basic research in cognitive dysfunction.