Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning

Abstract

(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression profiles of eight GN subtypes. Then, differentially expressed immune-related genes (DIRGs) were identified to explore the molecular mechanisms of GN, and single-sample gene set enrichment analysis (ssGSEA) was performed to discover the abnormal inflammation in GN. In addition, a nomogram model was generated using the R package “glmnet”, and the calibration curve was plotted to evaluate the predictive power of the nomogram model. Finally, deep learning (DL) based on a multilayer perceptron (MLP) network was performed to explore the characteristic genes for GN. (3) Results: we screened out 274 common up-regulated or down-regulated DIRGs in the glomeruli and tubulointerstitium. These DIRGs are mainly involved in T-cell differentiation, the RAS signaling pathway, and the MAPK signaling pathway. ssGSEA indicates that there is a significant increase in DC (dendritic cells) and macrophages, and a significant decrease in neutrophils and NKT cells in glomeruli, while monocytes and NK cells are increased in tubulointerstitium. A nomogram model was constructed to predict GN based on 7 DIRGs, and 20 DIRGs of each subtype of GN in glomeruli and tubulointerstitium were selected as characteristic genes. (4) Conclusions: this study reveals that the DIRGs are closely related to the pathogenesis of GN and could serve as genetic biomarkers in GN. DL further identified the characteristic genes that are essential to define the pathogenesis of GN and develop targeted therapies for eight GN subtypes.

Finding Group Gene Biomarkers by improved LRP algoirthm and Intersection Selection Idea

“…The dataset is from [18] and the detailed of it shown in Table 1. It includes two datas that is from two tissues of the kidney: Glomeruli and Tubulointerstitium, shown in Table 2.…”

“…of finding biomarkers mainly focus on binary classification, such as methods of computing differential genes, Random Forest, Support Vector Machine, etc. However, in real life, there exist other requirements that hope to find the biomarkers for many subtypes of one disease [18,26,29,32]. Take nephropathy nephritis for example, it includes SLE(Systemic Lupus Erythematosus), IgAN(IgA Nephropathy), DN(Diabetic Nephropathy), HN(Hypertensive Nephropathy), etc.…”

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage‐associated biomarkers by bulk and single‐cell sequencing

“…Machine learning and deep learning algorithms extract valuable patterns and information from large-scale data, identifying potential SAH-related biomarkers for personalised treatment and precision medicine. 11,12 In this study, we utilised a rat model of SAH, performing singlecell transcriptomic sequencing and bulk RNA-seq on collected brain tissue. Integrating public gene expression databases, we identified macrophage features in SAH using single-cell transcriptomic data.…”

“…This sheds light on macrophage function in SAH development and the inflammatory response. Machine learning and deep learning algorithms extract valuable patterns and information from large‐scale data, identifying potential SAH‐related biomarkers for personalised treatment and precision medicine 11,12 …”