Machine learning-based integration develops an immune-derived signature for diagnosing high-altitude pulmonary hypertension.

ABSTRACT:

Background

High-altitude pulmonary hypertension (HAPH) is a common disease in high-altitude regions where implementation of gold-standard diagnostic methods remains logistically challenging.

Methods

In the retrospective analysis, we employed an integrative multi-omics approach combining single-cell RNA sequencing (scRNA-seq, n = 10), bulk RNA sequencing (RNA-seq, n = 126), and proteomic profiling (n = 42) to characterize immune microenvironment remodeling in HAPH. Subsequently, we established a machine learning-based diagnostic model. The HAPH-associated signatures were finally validated by Quantitative PCR.

Results

Through scRNA-seq analysis utilizing Ro/e and contribution scoring analysis, we first demonstrated the pivotal role of myeloid lineag

SUBMITTER: Yang D 

PROVIDER: S-EPMC12436134 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature