Smooth Muscle Cells Take Center Stage as Gene Study Reveals New Clues to Pulmonary Hypertension

Pulmonary hypertension is one of medicine’s most unforgiving diagnoses. The disease, defined hemodynamically as a mean pulmonary arterial pressure above 20 mmHg at rest measured by right heart catheterization, affects roughly five new individuals per million each year and strikes at any age. Patients typically begin with exertional breathlessness, fatigue, and palpitations, but the condition relentlessly progresses toward right heart failure, edema, syncope, and even sudden death. Although drugs such as prostacyclin analogs, endothelin receptor antagonists, and phosphodiesterase-5 inhibitors have eased symptoms, the long-term prognosis remains poor. Now, a team of researchers publishing in the journal iScience has taken a major step toward understanding why, by combining two powerful genomic technologies to expose the molecular machinery driving the disease.

The study, led by Peipei Wu, Jingru Huan, and Chengliang Liu together with colleagues at Anhui Medical University, integrated bulk RNA sequencing with single-cell RNA sequencing to dissect pulmonary hypertension at unprecedented resolution. The rationale is elegant. Bulk RNA sequencing measures the average gene expression across a mixed tissue sample and has long been used to find differentially expressed genes in the disease, but lung vascular tissue contains many cell types, including endothelial cells, smooth muscle cells, and infiltrating immune cells, so the technique cannot say which cell is doing what. Single-cell RNA sequencing resolves that heterogeneity cell by cell, yet it is often limited by small sample sizes and shallow sequencing depth. Merging the two approaches allows researchers to pull robust disease-associated genes from the global transcriptomic landscape and then anchor them to specific cell types.

The team began with the single-cell side of the analysis, mining the public dataset GSE210248, which contains pulmonary vascular tissue from three pulmonary hypertension patients and three healthy controls. After rigorous quality control, in which cells with too few detected genes or excessive mitochondrial gene content were discarded, the researchers identified 19,582 genes and selected the top 1,500 highly variable ones for downstream analysis. Using principal component analysis and uniform manifold approximation and projection, a dimensionality-reduction technique that lets scientists visualize high-dimensional data in two dimensions, they clustered the cells into 14 distinct populations. These included smooth muscle cells, endothelial cells, fibroblasts, macrophages, monocytes, several T cell subsets, natural killer cells, B cells, dendritic cells, adipocytes, epithelial cells, and hematopoietic stem cells, painting a remarkably complete picture of the pulmonary vascular microenvironment.

Next came the question of who talks to whom. Using the CellChat tool, which infers communication networks from ligand-receptor expression patterns, the researchers mapped the interactions among all 14 cell types. Smooth muscle cells emerged as a dominant hub of intercellular communication. Endothelial cells interacted most strongly with T cells, epithelial cells with macrophages, and smooth muscle cells with dendritic cells. Among the specific ligand-receptor pairs mediating this crosstalk, two stood out: Annexin A1 binding to formyl peptide receptor 1, which mediates communication between smooth muscle cells and monocytes, and complement component 3 binding to its receptor C3AR1, which mediates smooth muscle cell-macrophage communication. These findings dovetail with growing evidence that immune-stromal paracrine loops drive pulmonary vascular remodeling. Notably, elevated plasma Annexin A1 has previously been linked to reduced survival in pulmonary arterial hypertension, and complement C3 sits at the intersection of all three complement pathways, coordinating endothelial activation, smooth muscle phenotype switching, immune cell recruitment, and platelet activation.

Because pulmonary artery smooth muscle cells are considered central players in the disease, the researchers then applied high-dimensional weighted gene co-expression network analysis, or hdWGCNA, specifically to the smooth muscle cell population. This method groups genes that rise and fall together across cells, revealing functional modules rather than single genes. With the soft threshold optimized to five to ensure a scale-free network, hierarchical clustering partitioned the smooth muscle transcriptome into eight co-expression modules, four of which, labeled M2, M6, M7, and M8, showed significant and specific enrichment in smooth muscle cells. Within these modules, S100A10, ANXA2, RPL13A, and RPL13 emerged as key network nodes. Gene Ontology analysis of the four module gene sets revealed strong enrichment in extracellular matrix organization, extracellular structure organization, and related processes, while Kyoto Encyclopedia of Genes and Genomes pathway analysis pointed to the TGF-beta signaling pathway, amino sugar and nucleotide sugar metabolism, and the HIF-1 signaling pathway, all well-established drivers of pulmonary vascular pathology.

With the single-cell landscape mapped, the team turned to the bulk side, pooling three independent transcriptomic cohorts from the Gene Expression Omnibus: GSE113439 with 15 patients and 11 controls, GSE53408 with 12 patients and 11 controls, and GSE117261 with 58 patients and 25 controls. After correcting batch effects across the datasets, they identified differentially expressed genes using a log fold-change threshold of 0.585 and an adjusted p value below 0.05. They then calculated the area under the receiver operating characteristic curve for each gene, a standard measure of diagnostic power, and retained 19 genes with an AUC above 0.7. Among these, MFGE8, a gene encoding milk fat globule-epidermal growth factor-8, achieved an AUC of 0.81 on its own, demonstrating strong standalone diagnostic performance.

To squeeze the most diagnostic value from these candidates, the researchers built multiple machine learning models, splitting the data into training and test sets at a 7:3 ratio. A partial least squares model performed best, achieving an AUC of 0.895, and it distilled the disease signature down to five hub genes: C1R, MFGE8, S100A11, PCOLCE2, and SFRP2. Critically, the model held up outside its training data. In two external validation cohorts, GSE15197 and GSE24988, the model achieved AUCs of 0.756 and 0.712 respectively, confirming that the five-gene signature generalizes across independent patient groups. The team also mapped the chromosomal locations of the five genes and probed their relationships with the immune microenvironment using the CIBERSORT algorithm, finding that S100A11 and PCOLCE2 correlated positively with M0 macrophages, SFRP2 with activated CD4 memory T cells and plasma cells, and MFGE8 with monocytes, consistent with its known role as a bridging molecule in phagocytosis.

Single-sample gene set enrichment analysis added another layer of biological insight. Comparing the HALLMARK pathway collection between patients and controls revealed that angiogenesis and epithelial-mesenchymal transition signatures were significantly upregulated in pulmonary hypertension, both closely tied to endothelial dysfunction and smooth muscle hypertrophy. Each hub gene carried its own pathway fingerprint: C1R was associated with xenobiotic metabolism and glycolysis, SFRP2 with epithelial-mesenchymal transition and coagulation, S100A11 with reactive oxygen species and IL-6/JAK-STAT3 signaling, and PCOLCE2 with oxidative phosphorylation. These associations underscore the metabolic reprogramming, from glycolysis to altered mitochondrial respiration, that sustains the hyperproliferative phenotype of pulmonary artery smooth muscle cells in the disease.

The most compelling part of the study is its experimental validation. The team cultured human pulmonary artery smooth muscle cells under normal oxygen or under 1 percent oxygen for 24 hours to mimic the hypoxic conditions of pulmonary hypertension. Reverse transcription quantitative PCR and western blotting showed that MFGE8, SFRP2, S100A11, and PCOLCE2 were all upregulated under hypoxia, with immunofluorescence confirming cytoplasmic localization. When the researchers silenced each gene with small interfering RNAs, both cell counting kit-8 assays and EdU incorporation assays showed that knocking down any of the four genes significantly inhibited smooth muscle cell proliferation. Digging deeper into mechanism, the team found that SFRP2 and S100A11 were positively correlated with HIF-1alpha, the master transcriptional regulator of the hypoxic response. Silencing either gene partially reduced HIF-1alpha protein levels, and, strikingly, overexpressing HIF-1alpha could partially rescue the proliferation slowdown caused by S100A11 or SFRP2 knockdown, placing these genes squarely within the hypoxia-driven growth circuitry of the diseased vasculature.

The authors are candid about the limitations. The analysis relies on publicly available datasets that, despite careful batch correction, still require validation in prospective cohorts. Subclustering of smooth muscle cells was not possible because too few cells were available, and the in vitro work was confined to acute hypoxia in a single cell type, leaving the genes’ effects on hemodynamics and right ventricular function untested in living animals. C1R, in particular, showed no differential expression under acute hypoxia, likely because it is expressed mainly by immune or endothelial cells or responds to chronic inflammatory rather than hypoxic signals. Even so, the study delivers a coherent new model of pulmonary hypertension in which smooth muscle cells act as central orchestrators of vascular remodeling, wired into the immune system through Annexin A1 and complement signaling and driven to proliferate by a hypoxia-HIF-1alpha axis involving MFGE8, SFRP2, S100A11, and PCOLCE2. If future work confirms these genes in patients, the five-gene signature could become both an early diagnostic blood test and a roadmap for the targeted therapies that pulmonary hypertension patients still desperately need.

Subject of Research: Integrative bulk and single-cell transcriptomic analysis of pulmonary hypertension identifying smooth muscle cell signaling hubs and diagnostic gene biomarkers

Article Title: Key genes and pathways of pulmonary hypertension identified by integrating bulk and single-cell RNA sequencing

Article References: Wu, P., Huan, J., Liu, C., Jin, H., Wang, R., Wang, Y., Cao, C., & Zhang, B. (2026). Key genes and pathways of pulmonary hypertension identified by integrating bulk and single-cell RNA sequencing. iScience, 29(11), Article 117846. https://doi.org/10.1016/j.isci.2026.117846

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117846

Keywords: pulmonary hypertension, single-cell RNA sequencing, bulk RNA sequencing, smooth muscle cells, hdWGCNA, machine learning, HIF-1alpha, biomarkers, vascular remodeling, TGF-beta signaling, immune infiltration, diagnostic gene signature