Identification of novel biomarkers for hypertension and ventricular remodeling based on transcriptomics and machine learning

Ventricular remodeling (VR) associated with hypertension (HTN) is characterized by a complex molecular mechanism. In the context of clinical treatment, substantial challenges persist. A total of 12 differentially expressed genes were jointly obtained from different cohorts and weighted gene co-expression network analysis (WGCNA). Among them, genes associated with oxidative stress were screened out. Upon verification through learning machine, it was revealed that NR1H2 and MT1E are closely associated with the progression of HTN-VR. Their stability was validated using both internal and external datasets. The study presented the immune cell infiltration patterns associated with these key genes and the potential mechanisms underlying disease progression, which were further verified in mouse models. The research revealed that NR1H2 and MT1E serve as crucial risk markers for the progression of HTN and VR. These efforts are intended to offer valuable insights into the underlying mechanisms and uncover potential targets for clinical intervention.