Identification of Novel Biomarkers for Hypertension and Ventricular Remodeling Based on Transcriptomics and Machine Learning

Zipeng Li, Bohao Zhang, Limeng Chao, Xin Tian, Wei Chen, Chang Liu, Hai Tian,
Identification of Novel Biomarkers for Hypertension and Ventricular Remodeling Based on Transcriptomics and Machine Learning,
iScience,
2025,
113668,
ISSN 2589-0042,
https://doi.org/10.1016/j.isci.2025.113668.
(https://www.sciencedirect.com/science/article/pii/S2589004225019297)
Abstract: Background
Ventricular remodeling associated with hypertension is characterized by a complex molecular mechanism. In the context of clinical treatment, substantial challenges persist.
Methods and Results
A total of 12 differentially expressed genes were jointly obtained from different cohorts and 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.
Conclusions
The research revealed that NR1H2 and MT1E serve as crucial risk markers for the progression of hypertension and ventricular remodeling. These efforts are intended to offer valuable insights into the underlying mechanisms and uncover potential targets for clinical intervention.
Keywords: Hypertension; Ventricular Remodeling; Machine Learning; bioinformatics