Gene and metabolite changes triggered by downregulation of JUNB and ZNF281 in idiopathic pulmonary arterial hypertension: potential mechanisms revealed by multi-omics study

Introduction

Pulmonary arterial hypertension (PAH) is a group of pulmonary vascular diseases characterized by elevated pulmonary arterial resistance and pressure, leading to increased right ventricular load, hypertrophy, and ultimately, right heart failure and death (1). Idiopathic PAH (IPAH) is a specific subset of PAH, accounting for 35% to 67% of all PAH cases and is often referred to as the “cancer” of cardiovascular diseases (2). Epidemiological studies have indicated that the global incidence of PAH ranges from 2.0 to 7.6 cases per million adults annually, with a prevalence of 11 to 26 cases per million adults. In the United States, the estimated prevalence of pulmonary hypertension (PH) is 10.6 cases per million adults, while in the pediatric population, the prevalence is approximately 5 to 8 cases per million children (3). From an etiological perspective, pediatric-onset PAH is characterized by a higher proportion of IPAH and a greater genetic burden compared to adult-onset IPAH (4). Over the past two decades, targeted therapies directed at key pathways such as endothelin-1 (ET-1), prostacyclin, and nitric oxide (NO) have been established and widely implemented in clinical practice. However, a previous prospective cohort study revealed that although these treatments have significantly improved short-term prognosis, the mortality rate associated with PAH remains high. Long-term outcomes are still uncertain, and the clinical and economic burden imposed by this condition persists (5). Conventional diagnostic methods and therapeutic strategies, while effective in certain contexts, are insufficient to fully address the complex pathophysiological mechanisms underlying PAH. Therefore, there is an urgent need to elucidate the underlying mechanisms of PAH and to identify relevant biomarkers.

A previous study suggested that PAH may be associated with genetic polymorphisms, multi-gene defects, and abnormal secretion of cytokines and growth factors (6). The transcriptomic alterations in PAH have been investigated at the organ and tissue levels, primarily utilizing microarray technology, which has identified several genes associated with vascular remodeling and inflammation (7). Whole-blood transcriptome sequencing provides an in-depth understanding of the expression and abundance of all transcripts across the entire system, offering a revolutionary approach to the discovery of biomarkers, comprehension of drug responses, elucidation of pathophysiological processes, and non-invasive diagnosis of diseases.

Metabolomics, which focuses on analyzing small molecules in biological samples, has become increasingly important in studying metabolic abnormalities in PAH. Metabolomics is a discipline dedicated to the analysis of small molecules in biological samples. The primary objective of metabolomics is to gain a comprehensive understanding of the alterations in the metabolome that are induced by pathophysiological mechanisms. Pathophysiological alterations at every biological level ultimately lead to changes in metabolite concentrations. These changes not only facilitate the identification of novel biomarkers and therapeutic targets, through integration with other omics technologies such as genomics, transcriptomics, and proteomics, but also enhance our understanding of the pathogenesis of PAH (8).

In this study, metabolomic analysis was conducted on plasma samples from five IPAH patients and five healthy controls to identify key regulatory genes, metabolites, and abnormal pathways associated with IPAH. RNA sequencing, metabolite profiling, and bioinformatics analyses were employed to explore the expression patterns of metabolic-related genes and metabolites in PAH, aiming to identify promising metabolic biomarkers for the diagnosis and treatment of PAH. We present this article in accordance with the MDAR reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-370/rc).

Methods

Study design and patient selection

This study recruited patients who were diagnosed with PAH and admitted to the Department of Cardiology at Shanghai Children’s Hospital between January 2023 and December 2024. All PAH patients met the diagnostic criteria outlined in the 2015 European Society of Cardiology/European Respiratory Society (ESC/ERS) Guidelines for the diagnosis and treatment of PH (9). PAH in children was defined as a mean pulmonary artery pressure (mPAP) ≥25 mmHg, pulmonary artery wedge pressure (PAWP) ≤15 mmHg, and pulmonary vascular resistance (PVR) >3 Wood units, which measured by standard right heart catheterization under sea-level conditions, three months after birth. Blood samples were collected from all PAH patients before any treatment for PH reduction. The healthy control group consisted of individuals under 18 years of age with no recent history of fever, infection, or vaccination. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study has been reviewed and approved by the Ethics Committee of Shanghai Children’s Hospital (No. 2022d122-E02). Written informed consent was obtained from the parents or legal guardians of all participants prior to the enrollment of this study.

RNA sequencing

Total RNA was extracted from peripheral blood mononuclear cells (PBMCs) using Trizol reagent. RNA sequencing libraries were constructed using the NEBNext^® Ultra™ RNA Library Prep Kit (NEB, Ipswich, USA) according to the manufacturer’s instructions, with barcodes added to each sample library sequence. The quality of the libraries was assessed using the Agilent Bioanalyzer 5400 system (Agilent Technologies, Santa Clara, CA, USA). Sequencing was performed on the Illumina NovaSeq™ X Plus platform (Illumina, San Diego, CA, USA) to generate 150 bp paired-end reads. Raw data in fastq format were processed using a custom Perl script. The number of sequence fragments aligned to each gene was calculated using HTSeq v0.13.5, and the expression level was represented by fragments per kilobase of transcript per million mapped reads (FPKM).

Metabolite detection

Metabolite detection was performed using liquid chromatography-mass spectrometry (LC-MS). Blood samples were processed by protein precipitation with acetonitrile to remove interfering substances, followed by centrifugation to collect the supernatant, which was then diluted with methanol/water solution and filtered to obtain the test samples. The samples were analyzed using an ACQUITY UPLC^® HSS T3 column (2.1×100 mm, 1.8 µm) (Waters, Milford, MA, USA) at a flow rate of 0.3 mL/min and a column temperature of 40 ℃, with an injection volume of 2 µL. The mobile phase consisted of 0.1% formic acid in acetonitrile (B2) and 0.1% formic acid in water (A2) for positive ion mode, and acetonitrile (B3) and 5 mM ammonium formate in water (A3) for negative ion mode. The gradient elution program was as follows: 0–1 min, 8% B2/B3; 1–8 min, 8–98% B2/B3; 8–10 min, 98% B2/B3; 10–10.1 min, 8–98% B2/B3; 10.1–12 min, 8% B2/B3. The Thermo Q Exactive mass spectrometer (Thermo Fisher Scientific, Waltham, USA) was used in electrospray ionization (ESI) mode to collect data in both positive and negative ion modes. The spray voltage was set at 3.50 kV for positive ions and −2.50 kV for negative ions, with a sheath gas flow rate of 40 arb and an auxiliary gas flow rate of 10 arb. The capillary temperature was maintained at 325 ℃, and the resolution was set at 70,000 for full-scan MS and 17,500 for MS/MS. The top 10 ions were selected for fragmentation, and dynamic exclusion was applied to remove unnecessary MS/MS information.

Bioinformatics analysis

Raw mass spectrometry data were converted to mzXML format using Proteowizard software and processed for peak detection, filtering, and alignment using the XCMS R package with parameters set as bw =2, ppm =15, peakwidth =c(5, 30), mzwid =0.015, mzdiff =0.01, and method = “centWave” (10). Systematic errors were corrected based on quality control (QC) samples using support vector regression, and metabolites with relative standard deviation (RSD) >30% were filtered out. Metabolite identification was performed using databases such as HMDB (11), MassBank (12), LipidMaps (13), and mzCloud (14), with a search tolerance of ppm <30. The molecular weight was determined based on the m/z of the parent ion, and the molecular formula was predicted considering mass deviation and adduct ions. The secondary spectrum information was used for further identification. Data analysis was conducted using the Ropls R package for principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal PLS-DA (OPLS-DA), with model overfitting assessed by permutation tests (15). The model’s explanatory power and predictive ability were represented by R2X, R2Y, and Q2, respectively. Differential metabolites were selected based on P value (<0.05), variable importance in projection (VIP) (>1), and fold change (FC). Pathway enrichment analysis was performed using KEGG Mapper to visualize the association between differential metabolites and pathways, and the clusterProfiler package (v3.12.0) was used for Gene Ontology (GO) enrichment analysis, with P values <0.05 considered significant. Additionally, the KOBAS v3.0 software was employed to test the enrichment of differentially expressed genes (DEGs) in KEGG pathways, with P values <0.05 indicating significant enrichment. Based on the GTRD database the relationships between transcription factors and their target genes were predicted.

Statistical analysis

Differential gene expression analysis was conducted using the DESeq2 R package (1.26.0), which employs a negative binomial distribution model to assess the significance of differences in gene expression levels. A significance threshold of P value <0.05 and |log₂FC| ≥0.58 were set to determine DEGs.

Results

Clinical and demographic characteristics

The demographic and clinical characteristics of the children in the two groups are presented in Table 1.

The distribution of FPKM values in the healthy control group (HC) and PAH groups showed high correlation, especially within the same group (Figure S1A,S1B). PCA revealed distinct differences in gene expression between the two groups, with the separation of samples on the PCA plot indicating that the variation explained, respectively, and 60.9% of the variation was explained by the differences between the two groups (Figure S1C).

Differential gene expression analysis

A total of 1,629 DEGs were identified between the HC and PAH groups, with 802 upregulated and 827 downregulated genes in the PAH group compared to the control group (Figure 1A, available online: https://cdn.amegroups.cn/static/public/tp-2025-370-1.xlsx). A heatmap of the 30 most significantly DEGs (Figure 1B) showed that some genes were significantly upregulated in the PAH group, such as PPP1R15A, PSPC1, and EPC2, while others were significantly downregulated, including MT-CO3 and HNRNPA1. Functional annotation of these DEGs combined with GO analysis further revealed the potential molecular mechanisms underlying PAH (Figure 1C). Upregulated genes were mainly enriched in cell cycle regulation, cellular stress response, and RNA modification processes, suggesting that cell cycle disruption and cellular stress response may play important roles in the pathogenesis of PAH. For example, the upregulation of CALHM2, a calcium channel protein, may be associated with calcium homeostasis imbalance, thereby affecting intracellular signal transduction and cell function. Downregulated genes were primarily enriched in mitochondrial function and energy metabolism processes, indicating that mitochondrial dysfunction and energy metabolism defects are significant pathological features of PAH. For instance, the downregulation of MT-CO3 may be related to mitochondrial dysfunction (16), while the downregulation of HNRNPA1 may affect RNA metabolism and cellular stress response (17). These gene expression changes suggest that PAH patients may have energy metabolism defects that impact normal cell function. These findings indicate that the gene expression changes in PAH patients involve multiple aspects, including cell cycle regulation, cellular stress response, mitochondrial dysfunction, and energy metabolism defects, which may collectively contribute to the development and progression of PAH (Figure 1D).

Figure 1 Integrated analysis of differentially expressed genes. (A) Volcano plot of differentially expressed genes. The plot displays the distribution of upregulated genes (n=802) and downregulated genes (n=827). (B) Heatmap of gene expression patterns. The heatmap shows the clustering analysis results of gene expression levels across different samples, with color intensity indicating the level of gene expression. (C) Bar chart of GO enrichment analysis for upregulated genes. The length of the bars represents the degree of enrichment, with functional abbreviations as follows: BP, MF, and CC. (D) Bar chart of GO enrichment analysis for downregulated genes. The length of the bars represents the degree of enrichment, with functional abbreviations as follows: BP, MF, and CC. BP, biological process; CC, cellular component; FC, fold change; GO, Gene Ontology; HC, healthy control; MF, molecular function; PAH, pulmonary arterial hypertension.

Non-coding RNA influence on gene expression

A total of 1,183 non-coding DEGs were identified between the HC and PAH groups, with 468 upregulated and 714 downregulated genes. Additionally, 4,403 genes were found to be influenced by lncRNAs, among which 1,300 DEGs were affected by lncRNAs (Figure 2A,2B, available online: https://cdn.amegroups.cn/static/public/tp-2025-370-2.xlsx). In the RNA sequencing analysis of PAH patients compared to normal children, the top 200 DEGs were analyzed for lncRNA influence, with 9 out of the top 30 DEGs being affected by lncRNAs (Figure 3). In the upregulated genes, significant lncRNA-influenced genes included ZSCAN22, ZNF740, ZBTB6, GLI1, AIRE, KLHL21, PGBD2, and CDK2AP2 (Figure 3A). The upregulation of these genes is associated with cell cycle regulation, cellular stress response, and RNA modification functions in PAH patients. For example, the upregulation of GLI1 may be related to cell proliferation and differentiation (18), while the upregulation of C-X-C chemokine receptor 4 (CXCR4) may enhance the cellular response to chemokines, further affecting cell migration and inflammatory response (19). In the downregulated genes, significant lncRNA-influenced genes included ZNF639, NFIL3, KLF5, JUNB, FOXP1, FOS, EGR3, and EGR1 (Figure 3B). The downregulation of these genes is closely related to mitochondrial dysfunction and energy metabolism defects in PAH patients, such as the downregulation of superoxide dismutase 2 (SOD2), which may indicate enhanced oxidative stress response (20). These functional annotations reveal the important roles of cell cycle regulation, oxidative metabolism, and cellular signal transduction changes in the pathogenesis of PAH.

Figure 2 Overview of differentially expressed lncRNAs. (A) Volcano plot of differentially expressed lncRNAs. The plot displays the distribution of upregulated lncRNAs (n=468) and downregulated lncRNAs (n=714). In the figure, “up” indicates upregulated genes (high expression), “down” indicates downregulated genes (low expression), and “none” indicates genes with no significant difference in expression. (B) Heatmap of differentially expressed lncRNAs. The heatmap illustrates the expression patterns of specific lncRNAs in normal and PAH samples. Color intensity in the heatmap indicates the level of lncRNA expression, highlighting the differences in expression among samples. FC, fold change; HC, healthy control; PAH, pulmonary arterial hypertension.

Figure 3 Transcription factor-regulated networks of differentially expressed genes. (A) Network diagram of upregulated genes influenced by transcription factors. The diagram illustrates the regulatory relationships between multiple upregulated genes (e.g., ZSCAN22, ZNF740, ZBTB6) and transcription factors. Nodes represent genes, and edges indicate regulatory interactions between them. (B) Network diagram of downregulated genes influenced by transcription factors. The diagram illustrates the regulatory relationships between multiple downregulated genes (e.g., GLI1, AIRE, KLHL21) and transcription factors. Nodes represent genes, and edges indicate regulatory interactions between them.

Protein interaction network analysis

The complex interactions between key intracellular proteins were further analyzed (Figure 4). The network included multiple nodes involving cell cycle regulatory factors (e.g., CDK4, CDK5R1, CDC25A), DNA repair-related proteins (e.g., FEN1, MCM6, MCM10), cytoskeleton-associated proteins (e.g., KIF14, DYNLL1), and transcriptional regulatory factors (e.g., KLF4, KLF5, GLI1, JUN). Additionally, several proteins related to cellular stress response and signal transduction were identified in the network, such as IL1B, TLR4, TP53, and S100A12, indicating the network’s potential role in cellular stress response and inflammatory signal transduction. Further analysis revealed that TP53, as a core node in the network, interacted directly or indirectly with multiple cell cycle regulatory proteins (e.g., CDKN2D, CCNH, CCND2) and stress response proteins (e.g., GADD45A, GADD45B), highlighting its key role in cell cycle regulation and stress response. Moreover, RPS family proteins (e.g., RPS3, RPS20, RPS15) frequently appeared in the network, likely associated with ribosomal function and protein synthesis regulation (21). Notably, dual-specificity phosphatase 7 (DUSP7) interacted with multiple signal transduction-related proteins in the network, potentially regulating the activity of the MAPK signaling pathway, thereby affecting cell proliferation and stress response (22). KLF5, as a transcription factor, co-regulated gene expression with KLF4, likely playing a synergistic role in cell differentiation and inflammatory response. Additionally, KLF5’s indirect connection with TP53 suggests a possible interaction between the TP53 pathway and KLF5 in cell cycle regulation and stress response (23).

Figure 4 The diagram illustrates a PPI network, depicting the interplay among a diverse array of proteins. Nodes represent proteins, and edges between nodes signify the interactions between these proteins. The color of the nodes indicates the degree of correlation, with deeper shades of purple-red representing higher correlation. PPI, protein-protein interaction.

Metabolomic profiling and pathway analysis

Metabolomic analysis of blood samples from PAH patients and healthy individuals systematically revealed significant differences in metabolite levels and their potential biological implications. The metabolite levels were found to differ significantly between PAH patients and healthy individuals. In PAH patients, 30 metabolites were significantly upregulated and 29 metabolites were significantly downregulated compared to healthy individuals (available online: https://cdn.amegroups.cn/static/public/tp-2025-370-3.xlsx). These differential metabolites were selected based on log₂FC and significance level (−log₁₀ P value), indicating significant alterations in the metabolite profiles of PAH patients. KEGG pathway enrichment analysis further elucidated the metabolic pathways involved in these differential metabolites (Figure 5A). The results showed significant enrichment of metabolic pathways in PAH patients, including amino acid metabolism (e.g., D-amino acid metabolism, alanine-aspartate-glutamate metabolism) and energy metabolism (e.g., glycolysis/gluconeogenesis, pentose phosphate pathway). These enriched pathways suggest widespread metabolic disturbances in PAH patients, involving energy metabolism, amino acid metabolism, and lipid metabolism. Heatmap analysis of differential metabolites further validated the expression differences between PAH patients and healthy individuals (Figure 5B). The heatmap clearly displayed the differences in metabolite levels between the two groups, with upregulated metabolites (e.g., α-ketoglutarate, 4-methyl-5-nitrocatechol) showing significantly higher levels in PAH patients. In contrast, downregulated metabolites (e.g., L-glutamine, indole-3-acetic acid, N-acetylglucosamine-1-phosphate) exhibited significantly lower levels in PAH patients.

Figure 5 Metabolite expression and KEGG pathway enrichment in PAH. (A) Metabolite expression heatmap: this heatmap illustrates the expression patterns of metabolites in normal and PAH samples. The intensity of the colors indicates the levels of metabolite expression, with darker shades representing higher expression levels. The heatmap highlights significant metabolic differences between the two groups, particularly in pathways related to amino acid metabolism and energy metabolism. (B) KEGG pathway enrichment analysis: the bubble chart displays the results of KEGG pathway enrichment analysis related to metabolites. The y-axis represents different metabolic pathways, while the x-axis indicates the enrichment score (−log₁₀ P value). The size of the bubbles corresponds to the number of metabolites within each pathway. Pathways such as glycolysis/gluconeogenesis, alanine-aspartate-glutamate metabolism, and ABC transporters show significant enrichment, indicating their potential roles in the pathogenesis of PAH. HC, healthy control; KEGG, Kyoto Encyclopedia of Genes and Genomes; PAH, pulmonary arterial hypertension.

Gene-metabolite correlation analysis

KEGG functional annotation comparison elucidated the intricate relationship between transcription factor-regulated DEGs and metabolite expression. In PAH, the primary metabolic pathways involved include bile secretion, type 2 diabetes mellitus, valine, leucine and isoleucine degradation, insulin signaling pathway, pentose phosphate pathway, glycolysis/gluconeogenesis, alanine, aspartate and glutamate metabolism, ABC transporters, and central carbon metabolism in cancer (Figure 6A). Further analysis revealed that four key genes, LDHB, IRS2, SOCS3, and PIK3R1, were significantly downregulated in PAH, while PIK3R3 was significantly upregulated. These genes are regulated by 15 transcription factors (Figure 6B). Notably, transcription factors such as JUNB and ZNF281 are downregulated in PAH, and these two transcription factors have a significant impact on gene expression, significantly affecting the expression of the aforementioned genes. The regulatory networks of JUNB and ZNF281 with their positively correlated target genes are shown in Figures S2,S3. At the metabolite level, glucose, l-glutamine, and oxaloacetic acid were downregulated in PAH, triggering a cascade of reactions that impacted related metabolic processes and were functionally associated with the aforementioned pathways (Figure 6C).

Figure 6 Integrated analysis of key transcription factor regulation and core metabolic pathways in PAH. (A) The figure shows the KEGG functional intersection of differentially expressed RNA and metabolites between PAH and HC. The vertical axis indicates metabolic pathways, and the horizontal axis represents enrichment significance (−log₁₀ P value), with higher values indicating more significant pathway enrichment. (B) The diagram illustrates the regulatory network of genes and transcription factors that overlap with metabolite functions. These genes and transcription factors (e.g., ZSCAN22, ZNF740, ZBTB6) are significantly upregulated in PAH and closely related to metabolic processes. Nodes represent genes or transcription factors, and edges indicate regulatory interactions between them. (C) Scatter plots showing the expression levels of key genes and metabolites identified in the KEGG functional intersection of differentially expressed RNA and metabolites between PAH patients (N=5) and HC (N=5). The gene PIK3R3 was significantly upregulated in PAH (P<0.001), while other genes (IRS2, JUNB, LDHB, PIK3R1, ZNF281, SOCS3) were significantly downregulated (P<0.001). Metabolites (glucose, L-glutamine, oxaloacetic acid) were also significantly downregulated in PAH (P=0.01). HC, healthy control; KEGG, Kyoto Encyclopedia of Genes and Genomes; PAH, pulmonary arterial hypertension.

Discussion

Pediatric PH is associated with various diseases and can occur at any age. Currently, pediatric PH is defined as an mPAP ≥25 mmHg, PAWP ≤15 mmHg, and PVR >3 Wood units, as measured by standard right heart catheterization at rest under sea-level conditions, three months after birth (9). Over the past two decades, improvements in the recognition of PH, targeted drug therapy, and better management of right heart failure have increased the median survival time of PAH patients from 2.8 to 6 years, and the 1-year survival rate has risen from 65% to 86–90% (24). However, the 5-year overall survival rate for PAH patients remains only 59%. Moreover, data indicate that many PAH patients with right heart failure will die within 2–3 years after diagnosis if not treated promptly. In China, the 5-year survival rate for pediatric PAH patients is only 37.5%, significantly lower than that in Western countries. To address this challenge, an increasing number of studies are focusing on genomics, transcriptomics, and metabolomics to identify new abnormal metabolic pathways and characteristic metabolites as predictive markers and therapeutic targets. Assessing the transcriptomic and metabolomic profiles associated with PAH can provide a comprehensive understanding of its molecular mechanisms, improve current diagnostic or therapeutic strategies, determine disease subphenotypes, and propose new therapeutic targets.

In this study, the transcriptomic analysis reveals that, regardless of transcription factor influence, the upregulated genes in the PAH group are mainly enriched in processes related to cell cycle regulation, cellular stress response, and RNA modification, suggesting that cell cycle disorder and cellular stress response may play a significant role in the pathogenesis of PAH. Among the DEGs, the upregulation of CALHM2, a calcium ion channel protein, is likely associated with calcium homeostasis disruption. When proteins enter the mitochondria through the CALHM2 channel, they participate in the regulation of fatty acid metabolism. Fatty acid metabolism, a series of complex biochemical reactions involving the synthesis and breakdown of fatty acids, is crucial for maintaining normal cellular physiological functions and overall energy balance in the organism (25). The downregulated genes are primarily enriched in processes related to mitochondrial function and energy metabolism, indicating that mitochondrial dysfunction and energy metabolism defects are significant pathological features of PAH. For example, MT-CO3, as an essential component of cytochrome c oxidase (COX), maintains the stability and integrity of the COX complex and is involved in mitochondrial respiratory chain function (26).

Transcription factors play a crucial role in the synthesis of coding RNA (mRNA) by binding to gene promoters or other regulatory elements to initiate or modulate the transcription process. They can function as activators to promote gene transcription or as repressors to inhibit it. Moreover, transcription factors respond to intracellular and extracellular signals to regulate the spatiotemporal specificity of gene expression, participating in cell differentiation, development, and stress responses (27). Additionally, transcription factors form complex networks that work together to regulate gene expression (28). Among the genes regulated by transcription factors, upregulated genes are closely related to protein phosphorylation and dephosphorylation, key components of cellular signal transduction. The dual-specificity phosphatases (DUSPs), particularly the MAP kinase phosphatase (MKPs) subfamily, can remove phosphate groups from proteins and are involved in mitogen-activated protein kinase (MAPK) signaling pathways (29). DUSP7 (an MKPs subfamily member, also known as MKPX or Pyst2) regulates the activity of ERK1/2, JNK, and p38 kinases. In cancer, DUSP7 inhibits the activity of MAPK family members through dephosphorylation and is considered a potential tumor suppressor (30).

DUSP7 interacts with multiple intracellular signaling proteins. TP53, a core network node, interacts with cell cycle regulators and stress response proteins (31). The TP53 gene family (TP53, TP63, TP73), which share highly similar sequences, function significantly as transcription factors (32). In cancer, DUSP7 expression is regulated by Ras and TGF-β signaling as well as mutant p53, while enhanced p63 activity can upregulate DUSP7 expression (33). In the PAH group, KLF5 is downregulated and indirectly connected to TP53, suggesting potential interactions with the TP53 pathway in cell cycle regulation and stress response. Krüppel-like factors (KLFs) are an important family of transcription factors involved in various biological processes and diseases (34). KLF5 plays a significant role in the development and progression of cardiovascular diseases. It participates in multiple oncogenic signaling pathways, including Wnt, RTK, hormone, TGF-β, Hippo, NOTCH, NF-κB, and Hedgehog, and forms complexes with various transcription factors to regulate the expression of target genes (such as Cyclin D1, p27, Nanog, and Slug), thereby influencing cell stemness, proliferation, apoptosis, autophagy, and migration (35). In heterozygous KLF5 knockout mice [KLF5(+/−)], there is thinning and dilation of the aortic wall, and impaired activation, proliferation, inflammation, and angiogenesis of fibroblasts and smooth muscle cells after vascular injury (36). Moreover, KLF5 expression is elevated in human lung biopsy tissues and cultured human PASMCs, correlating with the proliferative phenotype of PASMCs and contributing to vascular remodeling in hypoxic PH via HIF-1α (37). Additionally, KLF5 inhibits TP53 through SIN3A and HDAC2, thereby promoting cell proliferation (23).

Metabolites are crucial in biological processes and can serve as disease biomarkers and therapeutic targets. Diseases like cancer and PAH are linked to metabolic and epigenetic changes (38). In PAH, the shift from mitochondrial oxidative phosphorylation to aerobic glycolysis is a key metabolic change. Early PAH metabolite studies focused on this shift. Increased glucose and mitochondrial oxidation in PAH reduce pyruvate dehydrogenase phosphorylation, and inhibiting pyruvate dehydrogenase kinase (PDK) can improve pulmonary hypertension in susceptible individuals (39). The glycolysis/gluconeogenesis pathway is significantly affected in PAH, with up to 83.05% of genes regulated in the hypoxia model (HM) model. Pathways like alanine, aspartate, and glutamate metabolism, which are involved in antioxidant capacity and metabolic reprogramming, are also implicated in PAH (40). ABC transporters show significant expression changes in PAH and regulate ion channel function and cellular metabolism (41). These metabolic pathway changes reflect PAH’s pathophysiology and offer potential therapeutic targets.

In PAH, DEGs related to metabolic pathways, including LDHB, IRS2, PIK3R3, SOCS3, and PIK3R1, are regulated by 15 transcription factors, with ZNF281 being particularly influential. SOCS3 and IRS2 play crucial roles in PAH pathogenesis. SOCS3 expression, enhanced by promoter hypomethylation, inhibits inflammatory signaling pathways and correlates with PAH severity, potentially serving as a prognostic biomarker (42). IRS2 regulates the Akt-FOXO1 signaling pathway to suppress inflammation; its absence exacerbates inflammation and pulmonary vascular remodeling. IRS2 is also closely related to metabolic regulation and signaling, and its expression changes may reflect disease severity (43). ZNF281 is a multifunctional transcription factor involved in pluripotency, stem cell properties, epithelial-mesenchymal transition (EMT), and DNA damage response (44). It can inhibit inflammatory responses, regulate cell proliferation and migration, and affect metabolic processes, contributing to disease development (45). Its role in EMT may promote phenotypic transformation of pulmonary vascular smooth muscle cells, impacting vascular remodeling. Additionally, its function in the DNA damage response may influence cellular tolerance and repair capabilities, collectively potentially driving the pathological progression of PAH (46). Given its roles in PAH, ZNF281 may be a potential therapeutic target, as its modulation could affect pulmonary vascular remodeling and disease progression. The identified altered genes and metabolites suggest potential diagnostic or therapeutic applications for PAH.

In the current study, we did not conduct functional experiments to verify the roles of key genes and metabolites in PAH. Future studies will focus on these experiments to provide a more comprehensive understanding.

Conclusions

This study uncovers the strong link between transcription factor (e.g., JUNB ZNF281)-regulated gene expression and metabolite alterations in PAH. Abnormalities in key genes (e.g., LDHB, IRS2, PSOCS3, PIK3R1, PIK3R3) and metabolites (e.g., glucose, L-glutamine, oxaloacetic acid) are closely tied to PAH’s pathological process. These findings highlight that PAH’s metabolic disruption involves complex interactions among multiple genes and metabolites, calling for further investigation into the precise regulatory mechanisms to offer new therapeutic insights for PAH.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-370/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-370/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-370/prf

Funding: This work was supported by the National Natural Science Foundation of China (NSFC) (Nos. 81900437, 82370296), Shanghai science and technology committee (No. 23Y31900604), and Shanghai Municipal Health Commission Medical New Technology Research and Transformation Seed Program (No. 2024ZZ1024).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-370/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study has been reviewed and approved by the Ethics Committee of Shanghai Children’s Hospital (No. 2022d122-E02). Written informed consent was obtained from the parents or legal guardians of all participants prior to the enrollment of this study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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