Bioinformatics Analysis and Experimental Validation to Identify Hub Genes in Pulmonary Arterial Hypertension

Bioinformatics Analysis and Experimental Validation to Identify Hub Genes in Pulmonary Arterial Hypertension | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bioinformatics Analysis and Experimental Validation to Identify Hub Genes in Pulmonary Arterial Hypertension Ruohan Jia, Ke Wang, Yize Liu, Yuanying Ma, Yue Li, Mingwei Fan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7557161/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in European Journal of Medical Research → Version 1 posted 13 You are reading this latest preprint version Abstract Current pulmonary arterial hypertension (PAH) diagnostic approaches rely on right heart catheterization to measure the mean pulmonary artery pressure (≥ 20 mmHg), but limit early screening. Imaging techniques lack sensitivity for detecting early pulmonary pressure changes and are subject to variability, often resulting in diagnosis at an irreversible stage. The PAH pathogenesis remains incompletely understood, and improved diagnosis and treatment are urgently needed. In the present study, the Gene Expression Omnibus GSE113439 dataset underwent differential expression analysis of mRNA and Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using weighted gene co-expression network and protein–protein interaction network analyses. A hub gene-based PAH disease risk prediction model was constructed, followed by immune cell infiltration and correlation analyses. The hub gene expression was validated using qRT-PCR. PAH involved 547 differentially expressed genes. GO and KEGG enrichment analyses revealed that the focal adhesion, vascular smooth muscle contraction, RNA degradation, ferroptosis, and 2-oxocarboxylic acid metabolism pathways were closely associated with PAH development ( P < 0.05). PAH patients had significantly upregulated NOP58 , DDX21 , ABCE1 , CDC5L , and HSP90AA1 expression. Memory B cells, CD8 T cells, follicular helper T cells, activated natural killer cells, monocytes, activated mast cells, and neutrophils were significantly different between PAH patients and controls. Neutrophils, macrophages, and NOP58 expression were closely associated. NOP58 , DDX21 , ABCE1 , CDC5L , and HSP90AA1 may be novel PAH diagnostic and therapeutic targets. Their clinical applicability should be validated in larger-sample studies to explore gene-guided personalized therapies. Pulmonary arterial hypertension mRNA CIBERSORT weighted gene co-expression network analysis NOP58 diagnostic biomarker genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Pulmonary arterial hypertension (PAH) is a lethal condition characterized by progressive pulmonary vascular remodeling and right heart failure, defined by a mean pulmonary artery pressure (mPAP) ≥ 20 mmHg at rest 1 , 2 . Based on clinical, hemodynamic, and etiological characteristics, PAH is classified into subtypes: idiopathic PAH (IPAH), heritable PAH (HPAH), drug- and toxin-induced PAH, pulmonary veno-occlusive disease, pulmonary capillary hemangiomatosis, PAH with long-term response to calcium channel blockers, and PAH associated with other conditions such as connective tissue diseases (CTDs), congenital heart disease (CHD), human immunodeficiency virus infection, portal hypertension, and schistosomiasis 2 . Recent reports indicate an increasing incidence of PAH, particularly among women and older individuals. However, due to the non-specific early clinical manifestations of PAH and the reliance on invasive right heart catheterization for diagnosis, most patients are diagnosed at advanced, irreversible stages 3 , 4 . Advances in targeted therapies, including endothelin receptor antagonists, phosphodiesterase inhibitors, and prostacyclin analogs, have improved the 5-year survival rate of PAH from 34% to approximately 60% 5–7 . Nevertheless, these treatments primarily alleviate vasoconstriction and do not target the core mechanisms of proliferation and fibrosis. Increasing research efforts have focused on precision medicine for PAH to reduce diagnostic challenges and improve survival rates. Advancements in bioinformatics tools have enabled their increasing use to identify disease-related molecules and construct associated signaling pathways. For example, Jandl et al. (2022) demonstrated that the absence of natural killer (NK) T cells impaired the STAT1–CXCL9–CXCR3 axis in pulmonary hypertension (PH), suggesting that NKT cell activation could restore this axis as a potential therapeutic target for PAH 8 . Similarly, Piper et al. (2024) reported that RAB7 deficiency impaired cardiovascular cell function, leading to PH, indicating that RAB7 activation may represent a potential therapeutic strategy 9 . Zhou et al. (2024) used gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and machine learning to identify hub genes in PAH, confirming a strong association between MACC1 and the malignant features of PAH and highlighting MACC1 as a promising therapeutic target 10 . However, the molecular mechanisms underlying PAH development remain incompletely understood, and current studies often rely on bioinformatics predictions without experimental validation. Furthermore, the role of non-coding RNA interactions in PAH pathogenesis remains unclear, adding complexity to molecular research in this field. In the present study, the mRNA expression dataset GSE113439, derived from fresh frozen lung samples of PAH patients and healthy controls, was systematically analyzed to identify disease-associated genes. The gene enrichment analysis, WGCNA module construction, and protein–protein interaction (PPI) network analysis identified five hub genes ( NOP58 , DDX21 , ABCE1 , CDC5L , HSP90AA1 ). Previously, CIBERSORT has been successfully applied to estimate immune cell infiltration in cancer and endometriosis 11 , 12 . Similarly, immune cell infiltration analysis and correlation analysis were conducted in the present study, followed by qRT-PCR validation and the construction of a PAH disease risk prediction model. The technical workflow is illustrated in Fig. 1 . The present study is the first to report the roles of these five hub genes in PAH, and integrated multi-omics closed-loop design and receiver operating characteristic (ROC) curve analysis to evaluate their diagnostic value and function. Focus was placed on the key mRNA, NOP58 , and its involvement in the immune microenvironment, potentially offering insights into early molecular diagnosis and treatment strategies for PAH patients. Materials and Methods Acquisition and Preprocessing of Gene Expression Data The mRNA expression profiles of fresh frozen lung samples from 15 PAH patients and 11 healthy controls were obtained from the GSE113439 dataset in the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo/ ). The PAH group comprised six patients with IPAH, 4 with PAH secondary to CTD, four with PAH secondary to CHD, and one with chronic thromboembolic PH (CTEPH). Differential Gene Expression Analysis Differential expression analysis of the genes between PAH patients and healthy controls was performed using the R packages limma, atmap, and plot2. Genes with a threshold of |log2 fold change (FC)| >1 and adjusted P < 0.05 were identified as differentially expressed genes (DEGs). The DEGs were visualized using volcano plots and heatmaps. Construction of Co-Expression Network and Identification of Disease-Related Genes A weighted gene co-expression network was constructed using the R packages limma and WGCNA. The gene expression data underwent clustering analysis to group genes with similar expression patterns into modules, facilitating the visualization of co-expression relationships. Outlier samples were removed based on sample distance plots, and a sample clustering heatmap was generated to confirm the distinction between the control and PAH groups. The optimal soft-thresholding power was selected using the pickSoftThreshold function based on the fitting index and mean connectivity plots. A scale-free network was constructed, and an adjacency matrix was converted into a topological overlap matrix (TOM) to assess gene connectivity. Genes with similar expression patterns were assigned to distinct modules, with a minimum module size of 60 and a merge threshold of 0.25 for combining similar modules. A module-trait correlation heatmap was generated based on the Pearson correlation coefficient (Cor) and corresponding P -values. Module membership (MM) and gene significance (GS) were calculated for each module, and core genes were identified using the thresholds of MM > 0.8 and GS > 0.5. The blue module ( P = 3 × 10⁻⁹) was identified as the core module most significantly associated with PAH, and its core genes were designated as disease-related key genes. Identification of Intersection Genes The intersection genes between DEGs and PAH key genes were identified using the R package VennDiagram, generating a Venn diagram to visualize the intersection. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment Analysis The intersection genes underwent Gene Ontology (GO) analysis, encompassing biological process (BP), cellular component (CC), and molecular function (MF). GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to associate DEGs with known biological pathways, elucidating their synergistic roles in PAH pathogenesis and generating hypotheses for subsequent experimental validation. The GO and KEGG pathway analyses used the R packages clusterProfiler, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, enrichplot, ggplot2, circlize, RColorBrewer, dplyr, ggpubr, and ComplexHeatmap, with significance thresholds set at P < 0.05 and adjusted P < 1. PPI Network and Hub Gene Selection PPI network analysis was conducted to integrate DEGs into a functionally synergistic regulatory network, aiding the identification of hub genes and revealing multi-gene interactions. The PPI network was constructed using the STRING database ( https://string-db.org/ ) with a default confidence score > 0.4, and disconnected nodes were excluded. The topological scores for each gene in the PPI network were calculated using Cytoscape software and the CytoHubba plugin. The top 10 hub genes were selected based on degree scores, and ROC curves were generated for the top five hub genes using the R packages glmnet and pROC. qRT-PCR Validation Total RNA was extracted from human pulmonary artery smooth muscle cells (HPASMCs) using AG RNAex Pro reagent (Accurate Biology, China). The RNA concentration was measured at 260 nm and 280 nm using a spectrophotometer (NanoDrop 2000, Thermo Fisher Scientific, USA). The RNA was reverse-transcribed to complementary DNA using a SweScript All-in-One RT SuperMix for qPCR (with one-step gDNA removal) (Servicebio, China). Real-time PCR amplification was conducted using a SYBR Green Premix Pro Taq HS qPCR Kit (Rox Plus) (Accurate Biology) on a QuantStudio™ 5 Real-Time PCR System (Applied Biosystems™, Thermo Fisher Scientific). Relative RNA levels were normalized to β-actin using the comparative threshold cycle (2 −ΔΔCt ) method. PAH Prediction Model Construction A nomogram for the five validated hub genes was constructed using the R packages rms and rmda to predict disease risk. Calibration curves were generated to assess the accuracy of the prediction model. Multi-Omics-Driven Immune Microenvironment Analysis The relative abundance of immune cells in each sample from the GSE113439 dataset was estimated using the R packages e1071, BiocManager, and preprocessCore, with 1000 permutations and a significance threshold of P < 0.05. The immune cell infiltration results were visualized using heatmaps and correlation plots generated by the R packages pheatmap and corrplot. Immune cells with significant differences between PAH and control groups were identified by generating violin plots using the R package vioplot, with P < 0.05 indicating significance. Correlation analysis between immune cell infiltration and the hub gene with the highest degree score was performed using the R packages limma, reshape2, ggpubr, and ggExtra, with P < 0.05 indicating significant associations. Statistical Analysis All bioinformatics analyses were conducted using R version 4.3.0 or Strawberry Perl software. Results with P < 0.05 were considered statistically significant. Results Differential Expression Analysis The mRNA expression profiles from the fresh frozen lung samples of 15 PAH patients and 11 healthy controls were retrieved from the GSE113439 dataset in the GEO database. The data were merged, batch-normalized, and underwent differential expression analysis between the PAH and control groups. Using thresholds of |log2FC| >1 and adjusted P < 0.05, a total of 547 DEGs were identified: 453 upregulated and 94 downregulated (Fig. 2 A, 2 B). Construction of Co-Expression Network and Identification of Disease-Related Genes After filtering out fluctuating genes, 23,089 genes with a coefficient of variation (CV) > 0.1 were selected for WGCNA using R software. Outlier samples were removed, and a sample clustering heatmap was generated. The optimal soft-thresholding power was selected to construct a scale-free network (Fig. 3 A, 3 B), followed by gene clustering based on similar expression patterns (Fig. 3 C). Dynamic module identification grouped genes with similar expression into modules (Fig. 3 D), which were merged to yield 16 gene modules (Fig. 3 E). The pink and grey modules demonstrated positive correlations with PAH traits (Cor = 0.53 and 0.57, respectively), while the brown, black, and blue modules exhibited negative correlations (Cor = -0.64, -0.66, and − 0.88, respectively). Other modules had absolute Cor 0.8 and GS > 0.5, yielding 3,917 key genes. The intersection of these key genes with DEGs yielded 481 intersection genes (Fig. 3 F). These intersection genes exhibited significant expression changes in PAH patients and tight associations in the co-expression network, and likely contribute synergistically to the core pathological processes of PAH, providing clues for subsequent functional enrichment analysis. Functional Enrichment Analysis The 481 intersection genes underwent GO analysis. The top 10 enriched terms for BP, CC, and MF are presented in Fig. 4 A. The top three enriched terms were as follows: BP, ribonucleoprotein complex biogenesis, ribosome biogenesis, and rRNA metabolic process; CC, nuclear speck, chromosomal region, and secretory granule lumen; MF, ATP hydrolysis activity, GTPase binding, and another ATP hydrolysis activity term. The KEGG pathway analysis of the 481 intersection genes identified the 19 most significantly enriched pathways ( P < 0.05), including focal adhesion, vascular smooth muscle contraction, RNA degradation, ferroptosis, and 2-oxocarboxylic acid metabolism (Fig. 4 B). The supplementary materials present the detailed GO and KEGG analysis results. PPI Network and Hub Gene Selection The disease-driving mechanisms of PAH were elucidated from a systems biology perspective using PPI network analysis to convert discrete genes into a dynamic regulatory network, facilitating the identification of key targets. A PPI network for the intersection genes was constructed (Fig. 5 A). The top 10 hub genes based on degree scores were EPRS1 , NOP58 , DDX21 , ABCE1 , CDC5L , HSP90AA1 , MTREX , NCL , POLR2B , and DDX18 . A PPI network for these hub genes was generated (Fig. 5 B), and the top five hub genes ( NOP58 , DDX21 , ABCE1 , CDC5L , HSP90AA1 ) were selected for ROC curve analysis (Fig. 5 C). All genes exhibited area under the curve (AUC) values > 0.85: NOP58 , ABCE1 , CDC5L , and HSP90AA1 , AUC = 1.000; and DDX21 , AUC = 0.939 (all, P < 0.05). qRT-PCR Validation The expression of hub genes in PAH was validated by extracting total RNA from HPASMCs and analyzing them using qRT-PCR. Consistent with bioinformatics results, NOP58 , DDX21 , ABCE1 , CDC5L , and HSP90AA1 expression was significantly upregulated in PAH ( P < 0.001) (Fig. 6 ). These results suggested that these five hub genes may be potential biomarkers for PAH diagnosis, pending validation. PAH Prediction Model A nomogram was constructed using the five validated hub genes to predict PAH risk, and calibration curves were generated (Fig. 7 ). The close alignment of the nomogram and calibration curves indicated high predictive accuracy. Notably, NOP58 , ABCE1 , and CDC5L had higher composite scores, suggesting greater contributions to PAH risk and potential utility in diagnosis. Immune Microenvironment Analysis in PAH Immune cell infiltration was analyzed by calculating the relative abundance of immune cells in each sample from the GSE113439 dataset (Fig. 8 A), followed by correlation analysis (Fig. 8 B). Immune cells with significant differences between the PAH and control groups were identified by analyzing the infiltration results (Fig. 8 C), and cells with P < 0.05 were identified: memory B cells, CD8 T cells, follicular helper T cells, activated NK cells, monocytes, activated mast cells, and neutrophils. Correlations between hub genes and immune cells were explored by selecting the hub gene with the highest degree score, NOP58 (degree = 46) for correlation analysis (Fig. 8 D). Three immune cell types demonstrated significant correlations (Fig. 8 E–G) ( P < 0.05): neutrophils were positively correlated with NOP58 (R = 0.61, P = 0.017), while CD8 T cells and M2 macrophages were negatively correlated (CD8 T cells: R = -0.69, P = 0.0058; M2 macrophages: R = -0.67, P = 0.0077). Discussion PAH is a complex and lethal vascular disease characterized by pulmonary vascular remodeling, right heart failure, and immune microenvironment dysregulation 13 – 15 . PAH diagnosis primarily relies on invasive right heart catheterization, which significantly increases the challenge of timely diagnosis and treatment 2 . Recent bioinformatics studies have advanced our understanding of PAH. For example, Feng et al. (2024) identified downregulation of the m6A modification enzyme LRPPRC in a PAH rat model, which increased Cenpf mRNA expression and promoted smooth muscle cell proliferation 16 . However, the mechanisms underlying PAH development remain incompletely elucidated. The present study aimed to identify key mRNAs in PAH, investigate the immune microenvironment, and construct a disease prediction model to identify hub genes that could aid PAH diagnosis and treatment while clarifying its pathogenesis. Differential expression analysis of mRNA between PAH patients and healthy controls identified 547 DEGs: 453 upregulated and 94 downregulated. A co-expression network was constructed, and the hub module was intersected with the DEGs to obtain 481 intersection genes. GO analysis revealed the critical roles of ribosome biogenesis, metabolism, and related biological processes in PAH. KEGG analysis indicated that the focal adhesion, vascular smooth muscle contraction, RNA degradation, ferroptosis, and 2-oxocarboxylic acid metabolism pathways are closely associated with PAH progression. The protein interactions encoded by DEGs were explored by constructing a PPI network, which identified NOP58 , DDX21 , ABCE1 , CDC5L , and HSP90AA1 as key components of the upregulated module (hub genes). These hub genes were significantly upregulated in PAH patients, with ROC curve analysis showing AUC > 0.85, particularly AUC = 1.000 for NOP58 , ABCE1 , CDC5L , and HSP90AA1 , indicating high diagnostic specificity and sensitivity. NOP58 is a nucleolar protein and core component of the small nucleolar ribonucleoprotein (snoRNP) complex that provides a scaffold for snoRNP assembly and is critical in RNA processing and ribosome biogenesis 17 . The present study is the first to report its upregulation in PAH patients, suggesting a potential association with PAH development. Correlation analysis of NOP58 with immune cells revealed significant associations with neutrophils (positive), CD8 T cells (negative), and M2 macrophages (negative), indicating its potential role in regulating the immune microenvironment. Similarly, DDX21, an RNA helicase involved in ATP binding and RNA unwinding 18 , was upregulated in PAH. While Miao et al. (2023) reported its role in epidermal differentiation 19 , the DDX21 function in pulmonary artery endothelial cells remains unclear, and the present study provided new insights into its relevance in PAH. CDC5L is a cell cycle regulator involved in mitosis, and reduces tumor cell viability when downregulated 20 . Whether CDC5L promotes the abnormal proliferation of pulmonary vascular smooth muscle cells in PAH is unconfirmed, but its positive correlation with PAH in the present study offered a novel perspective. ABCE1 is an ATP-binding cassette transporter that regulates ribosome recycling and mitochondrial oxidative phosphorylation, and inhibits small cell lung cancer proliferation and invasion when silenced 21 . The ABCE1 upregulation in PAH suggested that silencing ABCE1 may mitigate PAH progression, providing a new research direction. HSP90AA1 encodes a heat shock protein critical for protein homeostasis, and is associated with cell death mechanisms, including ferroptosis and pyroptosis, which are pivotal in cellular stress and inflammatory responses 22 . The synergistic actions of these hub genes likely drive pulmonary vascular remodeling through the focal adhesion, ferroptosis, and 2-oxocarboxylic acid metabolism pathways, offering new insights into PAH molecular mechanisms. The CIBERSORT analysis revealed significant differences in the infiltration levels of memory B cells, CD8 T cells, follicular helper T cells, activated NK cells, monocytes, activated mast cells, and neutrophils between PAH patients and controls. Correlation analysis with NOP58 demonstrated a positive correlation with neutrophils and negative correlations with CD8 T cells and M2 macrophages. These results underscored the role of immune microenvironment dysregulation in PAH pathogenesis. Previous studies have highlighted inflammation and immune cell infiltration as key pathological features of PAH. For example, Rabinovitch et al. (2014) demonstrated the critical roles of macrophages and T cells in vascular remodeling by secreting inflammatory cytokines such as IL-6 and TNF-α, which promote smooth muscle cell proliferation 23 . The immune correlation analysis in the present study confirmed that NOP58 may influence the inflammatory microenvironment by regulating neutrophils, CD8 T cells, and M2 macrophages, which was consistent with the results by Jandl et al. (2022) on immune regulation in PAH 8 . Additionally, the ferroptosis pathway enrichment suggested that iron-dependent programmed cell death may contribute to the PAH immune microenvironment. Ferroptosis is linked to oxidative stress and inflammation and has been implicated in pulmonary artery endothelial cell dysfunction, smooth muscle cell proliferation, and right ventricular hypertrophy 24 , 25 . The present study provides multidimensional evidence for the complexity of the PAH immune microenvironment, and single-cell RNA sequencing could elucidate the functions of specific immune cell subsets. A PAH disease risk prediction model was constructed based on NOP58 , DDX21 , ABCE1 , CDC5L , and HSP90AA1 , with high accuracy validated by nomogram and calibration curves. NOP58 , ABCE1 , and CDC5L demonstrated higher contributions, suggesting their potential as biomarkers for early PAH diagnosis. Compared to traditional right heart catheterization, this model reduces diagnostic complexity through non-invasive gene expression analysis. Similar to Piper et al.'s (2024) results regarding RAB7 as a potential PH target 9 , the present study emphasizes the importance of molecular biomarkers in early disease screening. However, the clinical applicability of the model requires validation in larger, diverse PAH cohorts (e.g., IPAH, HPAH). The present study provided novel targets and diagnostic tools for precision PAH management through multi-omics analysis and experimental validation. The identification of hub genes such as NOP58 holds potential for developing targeted therapies, such as inhibiting NOP58 to modulate RNA metabolism and the immune microenvironment, thereby attenuating vascular remodeling. The prediction model based on these hub genes could facilitate early screening in high-risk populations, addressing the limitations of imaging and invasive diagnostics. However, the present study was subject to limitations. First, the sample size (15 PAH patients and 11 controls) may have limited its generalizability, necessitating validation in larger, multi-center cohorts. Second, the study focused on mRNA expression and did not analyze non-coding RNA interactions (e.g., miRNA, lncRNA) with hub genes. Finally, qRT-PCR validation was performed only in pulmonary artery endothelial cells; hence, future studies should validate these findings in other cell types (e.g., smooth muscle cells, fibroblasts) to comprehensively elucidate their roles in PAH pathogenesis. Conclusion The present study systematically analyzed the GSE113439 dataset, integrating WGCNA, PPI network analysis, and qRT-PCR validation to identify five hub genes ( NOP58 , DDX21 , ABCE1 , CDC5L , HSP90AA1 ). These hub genes were confirmed to be significantly upregulated in PAH and demonstrated potential as diagnostic biomarkers. GO and KEGG analyses revealed their close associations with the focal adhesion, vascular smooth muscle contraction, RNA degradation, and ferroptosis pathways, providing novel insights into the molecular mechanisms of PAH. Immune microenvironment analysis indicated significant correlations between NOP58 and the infiltration levels of neutrophils, CD8 T cells, and M2 macrophages, suggesting its potential role in modulating inflammation and immune responses in PAH pathogenesis. A disease risk prediction model constructed based on the five hub genes exhibited high accuracy, providing a non-invasive tool for early PAH diagnosis. Future studies should focus on large-scale clinical validation and multi-omics approaches to explore the therapeutic potential of these hub genes, advancing the development of precision medicine for PAH. Declarations Ethics Approval and Consent to Participate This study was approved by the Academic Advisory Board of the First Affiliated Hospital of Zhengzhou University (approval no. 2024-KY-0468). All participants provided their written informed consent. Data Sharing Statement All data involved in this study are available from the corresponding author on request. The original GEO dataset (GSE113439) used for analysis in this study is available in the GEO database ( http://www.ncbi.nlm.nih.gov/geo ). Competing Interests The authors report no conflicts of interest in this work. Consent to Publish Declaration Not applicable. Funding This study was supported by the Key Scientific Research Project of Science and Technology Department of Henan Province [grant number 231111310800]. Author Contribution Z. C designed the experiments. R.J, K.W, Y.L, Y. M, and Y.Z.L analyzed the data and wrote the manuscript. P.L, Y.W, T.J, and L.D provided helpful discussions and reviewed the manuscript. All authors reviewed the manuscript. Acknowledgement We thank all patients who participated in this study for their cooperation. We also acknowledge the Henan Key Laboratory of Pharmacology for Liver Diseases for providing experimental platform support. Data Availability All data involved in this study are available from the corresponding author on request. The original GEO dataset (GSE113439) used for analysis in this study is available in the GEO database (http://www.ncbi.nlm.nih.gov/geo). References Hassoun PM (2021) Pulmonary Arterial Hypertension. N Engl J Med 385(25):2361-2376. doi:10.1056/NEJMra2000348 Humbert M, Kovacs G, Hoeper MM, et al. (2022) 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J 43(38):3618-3731. doi:10.1093/eurheartj/ehac237 Wu X, Suo S, Su X, et al. Trends in pulmonary arterial hypertension: insights from Global Burden of Disease 1990-2021. BMJ Open . Mar 18 2025;15(3):e095348. doi:10.1136/bmjopen-2024-095348 Simonneau G, Montani D, Celermajer DS, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. Eur Respir J . Jan 2019;53(1)doi:10.1183/13993003.01913-2018 D'Alonzo GE, Barst RJ, Ayres SM, et al. Survival in patients with primary pulmonary hypertension. Results from a national prospective registry. Ann Intern Med . Sep 1 1991;115(5):343-9. doi:10.7326/0003-4819-115-5-343 Boucly A, Savale L, Jaïs X, et al. Association between Initial Treatment Strategy and Long-Term Survival in Pulmonary Arterial Hypertension. Am J Respir Crit Care Med . Oct 1 2021;204(7):842-854. doi:10.1164/rccm.202009-3698OC Zolty R. Advances in the discovery of drugs that treat pulmonary arterial hypertension. Expert Opin Drug Discov . Apr 2023;18(4):445-466. doi:10.1080/17460441.2023.2192919 Jandl K, Marsh LM, Mutgan AC, et al. Impairment of the NKT-STAT1-CXCL9 Axis Contributes to Vessel Fibrosis in Pulmonary Hypertension Caused by Lung Fibrosis. Am J Respir Crit Care Med . Oct 15 2022;206(8):981-998. doi:10.1164/rccm.202201-0142OC Piper B, Bogamuwa S, Hossain T, et al. RAB7 deficiency impairs pulmonary artery endothelial function and promotes pulmonary hypertension. J Clin Invest . Feb 1 2024;134(3)doi:10.1172/jci169441 Zhou X, Liang B, Lin W, Zha L. Identification of MACC1 as a potential biomarker for pulmonary arterial hypertension based on bioinformatics and machine learning. Comput Biol Med . May 2024;173:108372. doi:10.1016/j.compbiomed.2024.108372 Gentles AJ, Newman AM, Liu CL, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med . Aug 2015;21(8):938-945. doi:10.1038/nm.3909 Wu XG, Chen JJ, Zhou HL, et al. Identification and Validation of the Signatures of Infiltrating Immune Cells in the Eutopic Endometrium Endometria of Women With Endometriosis. Front Immunol . 2021;12:671201. doi:10.3389/fimmu.2021.671201 Lannan KL, Phipps RP, White RJ. Thrombosis, platelets, microparticles and PAH: more than a clot. Drug Discov Today . Aug 2014;19(8):1230-5. doi:10.1016/j.drudis.2014.04.001 Ruopp NF, Cockrill BA. Diagnosis and Treatment of Pulmonary Arterial Hypertension: A Review. Jama . Apr 12 2022;327(14):1379-1391. doi:10.1001/jama.2022.4402 Ferrian S, Cao A, McCaffrey EF, et al. Single-Cell Imaging Maps Inflammatory Cell Subsets to Pulmonary Arterial Hypertension Vasculopathy. Am J Respir Crit Care Med . Jan 15 2024;209(2):206-218. doi:10.1164/rccm.202209-1761OC Feng Y, Yu Z, Tang M, et al. Transcriptome-Wide N6-Methyladenosine Alternations in Pulmonary Arteries of Monocrotaline-Induced Pulmonary Arterial Hypertension in Rats and Novel Therapeutic Targets. Biomedicines . Feb 4 2024;12(2)doi:10.3390/biomedicines12020364 Jackson GD, Cooper GN. Immune responses of rats to live Vibrio cholerae: secretion of antibodies in bile. Parasite Immunol . Summer 1981;3(2):127-35. doi:10.1111/j.1365-3024.1981.tb00390.x Song C, Hotz-Wagenblatt A, Voit R, Grummt I. SIRT7 and the DEAD-box helicase DDX21 cooperate to resolve genomic R loops and safeguard genome stability. Genes Dev . Jul 1 2017;31(13):1370-1381. doi:10.1101/gad.300624.117 Miao W, Porter DF, Lopez-Pajares V, et al. Glucose dissociates DDX21 dimers to regulate mRNA splicing and tissue differentiation. Cell . Jan 5 2023;186(1):80-97.e26. doi:10.1016/j.cell.2022.12.004 Mu R, Wang YB, Wu M, et al. Depletion of pre-mRNA splicing factor Cdc5L inhibits mitotic progression and triggers mitotic catastrophe. Cell Death Dis . Mar 27 2014;5(3):e1151. doi:10.1038/cddis.2014.117 Huang B, Gao Y, Tian D, Zheng M. A small interfering ABCE1-targeting RNA inhibits the proliferation and invasiveness of small cell lung cancer. Int J Mol Med . May 2010;25(5):687-93. Liu S, Xu Y, Yao X, et al. Perillaldehyde ameliorates sepsis-associated acute kidney injury via inhibiting HSP90AA1-mediated ferroptosis and pyroptosis: Molecular structure and protein interaction of HSP90AA1. Int J Biol Macromol . Apr 2025;304(Pt 2):140954. doi:10.1016/j.ijbiomac.2025.140954 Rabinovitch M, Guignabert C, Humbert M, Nicolls MR. Inflammation and immunity in the pathogenesis of pulmonary arterial hypertension. Circ Res . Jun 20 2014;115(1):165-75. doi:10.1161/circresaha.113.301141 Zhang L, Jia R, Li H, et al. Insight into the Double-Edged Role of Ferroptosis in Disease. Biomolecules . Nov 30 2021;11(12)doi:10.3390/biom11121790 Ge Q, Zhang T, Yu J, et al. A new perspective on targeting pulmonary arterial hypertension: Programmed cell death pathways (Autophagy, Pyroptosis, Ferroptosis). Biomed Pharmacother . Dec 2024;181:117706. doi:10.1016/j.biopha.2024.117706 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Feb, 2026 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 14 Nov, 2025 Reviews received at journal 03 Nov, 2025 Reviews received at journal 24 Oct, 2025 Reviews received at journal 23 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 12 Sep, 2025 First submitted to journal 07 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7557161","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524932558,"identity":"97083698-5857-45e6-a6ea-b247b107fd1c","order_by":0,"name":"Ruohan Jia","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ruohan","middleName":"","lastName":"Jia","suffix":""},{"id":524932559,"identity":"1ad0dee8-720f-4cd5-babd-0395dd292458","order_by":1,"name":"Ke Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou 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(F) Venn diagram of the intersection of DEGs and key PAH genes\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557161/v1/7307f9ae2428206d0948d595.jpg"},{"id":93028280,"identity":"f4c4fe06-ff66-4349-8867-5ad840bb9db8","added_by":"auto","created_at":"2025-10-08 09:52:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":242823,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of intersection genes in key modules. (A) GO analysis of intersection genes demonstrating their BP, CC, and MF. (B) KEGG analysis of hub genes identified the top 19 pathways. The bar plot length reflects the number of genes associated with relative pathways, and bar plot colors indicate the \u003cem\u003eP\u003c/em\u003e-values.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557161/v1/e9ed6acd4a7cee38c797488a.jpg"},{"id":93029784,"identity":"6c3b578a-1074-4021-890f-bc78ef6bc18f","added_by":"auto","created_at":"2025-10-08 10:00:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":404074,"visible":true,"origin":"","legend":"\u003cp\u003ePPI construction of intersection genes in key modules. (A) PPI network of intersection genes constructed using the STRING database. (B) PPI network of hub genes based on the number of protein–protein acting nodes shown in the network (only the top 10 are shown). (C) ROC curves of the top 5 hub genes with the highest degree scores\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557161/v1/56ac26b4ed3f6438222fde46.jpg"},{"id":93028291,"identity":"caa519b6-a67c-46f6-b125-cd016836d0d3","added_by":"auto","created_at":"2025-10-08 09:52:19","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":188837,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of hub genes in the cell model. (A) RT-qPCR verification of \u003cem\u003eNOP58\u003c/em\u003eexpression in the cell model. (B) RT-qPCR verification of \u003cem\u003eDDX21\u003c/em\u003eexpression in the cell model. (C) RT-qPCR verification of \u003cem\u003eABCE1\u003c/em\u003eexpression in the cell model. (D) RT-qPCR verification of \u003cem\u003eCDC5L\u003c/em\u003eexpression in the cell model. (E) RT-qPCR verification of \u003cem\u003eHSP90AA1\u003c/em\u003eexpression RT-qPCR in the cell model. ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557161/v1/287da77a19a757d84c0f9346.jpg"},{"id":93029798,"identity":"3bd78cfb-1a22-46bb-ae75-d037c92adb1b","added_by":"auto","created_at":"2025-10-08 10:00:19","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":101873,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction model for PAH. (A) Nomogram of the risks associated with PAH for five hub genes. (B) Calibration curve of PAH risk prediction model\u003c/p\u003e","description":"","filename":"Figure7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557161/v1/8a3b8a71628284ae824bf999.jpg"},{"id":93028288,"identity":"ee5139ae-2e67-40a2-b2f0-d91a139a657b","added_by":"auto","created_at":"2025-10-08 09:52:18","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":520703,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-omics data-driven analysis of the immune microenvironment. (A) Immune cell infiltration results of samples from the GSE113439 dataset. (B) Correlation analysis of immune cell infiltration in the GSE113439 dataset. (C) Violin plot of the differential analysis of immune cells between the control and PAH groups. (D) Correlation analysis between the hub gene \u003cem\u003eNOP58\u003c/em\u003e (degree = 46) and immune cells in the control and PAH groups. (E) Correlation analysis between the hub gene \u003cem\u003eNOP58 \u003c/em\u003eand neutrophils. (F) Correlation analysis between the hub gene \u003cem\u003eNOP58 \u003c/em\u003eand CD8 T cells. (G) Correlation analysis between the hub gene \u003cem\u003eNOP58\u003c/em\u003e and M2 macrophages\u003c/p\u003e","description":"","filename":"Figure8.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557161/v1/50f2583047fabe256a3cf703.jpg"},{"id":103251203,"identity":"bf31f986-6106-43b3-b893-4a88cbadce87","added_by":"auto","created_at":"2026-02-23 16:06:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3317472,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7557161/v1/2af46815-0965-4bc3-a4e6-d93f92ab759a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics Analysis and Experimental Validation to Identify Hub Genes in Pulmonary Arterial Hypertension","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary arterial hypertension (PAH) is a lethal condition characterized by progressive pulmonary vascular remodeling and right heart failure, defined by a mean pulmonary artery pressure (mPAP)\u0026thinsp;\u0026ge;\u0026thinsp;20 mmHg at rest\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Based on clinical, hemodynamic, and etiological characteristics, PAH is classified into subtypes: idiopathic PAH (IPAH), heritable PAH (HPAH), drug- and toxin-induced PAH, pulmonary veno-occlusive disease, pulmonary capillary hemangiomatosis, PAH with long-term response to calcium channel blockers, and PAH associated with other conditions such as connective tissue diseases (CTDs), congenital heart disease (CHD), human immunodeficiency virus infection, portal hypertension, and schistosomiasis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Recent reports indicate an increasing incidence of PAH, particularly among women and older individuals. However, due to the non-specific early clinical manifestations of PAH and the reliance on invasive right heart catheterization for diagnosis, most patients are diagnosed at advanced, irreversible stages\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdvances in targeted therapies, including endothelin receptor antagonists, phosphodiesterase inhibitors, and prostacyclin analogs, have improved the 5-year survival rate of PAH from 34% to approximately 60%\u003csup\u003e5\u0026ndash;7\u003c/sup\u003e. Nevertheless, these treatments primarily alleviate vasoconstriction and do not target the core mechanisms of proliferation and fibrosis. Increasing research efforts have focused on precision medicine for PAH to reduce diagnostic challenges and improve survival rates. Advancements in bioinformatics tools have enabled their increasing use to identify disease-related molecules and construct associated signaling pathways. For example, Jandl et al. (2022) demonstrated that the absence of natural killer (NK) T cells impaired the STAT1\u0026ndash;CXCL9\u0026ndash;CXCR3 axis in pulmonary hypertension (PH), suggesting that NKT cell activation could restore this axis as a potential therapeutic target for PAH\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Similarly, Piper et al. (2024) reported that RAB7 deficiency impaired cardiovascular cell function, leading to PH, indicating that RAB7 activation may represent a potential therapeutic strategy\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eZhou et al. (2024) used gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and machine learning to identify hub genes in PAH, confirming a strong association between \u003cem\u003eMACC1\u003c/em\u003e and the malignant features of PAH and highlighting \u003cem\u003eMACC1\u003c/em\u003e as a promising therapeutic target\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, the molecular mechanisms underlying PAH development remain incompletely understood, and current studies often rely on bioinformatics predictions without experimental validation. Furthermore, the role of non-coding RNA interactions in PAH pathogenesis remains unclear, adding complexity to molecular research in this field.\u003c/p\u003e\u003cp\u003eIn the present study, the mRNA expression dataset GSE113439, derived from fresh frozen lung samples of PAH patients and healthy controls, was systematically analyzed to identify disease-associated genes. The gene enrichment analysis, WGCNA module construction, and protein\u0026ndash;protein interaction (PPI) network analysis identified five hub genes (\u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, \u003cem\u003eHSP90AA1\u003c/em\u003e). Previously, CIBERSORT has been successfully applied to estimate immune cell infiltration in cancer and endometriosis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Similarly, immune cell infiltration analysis and correlation analysis were conducted in the present study, followed by qRT-PCR validation and the construction of a PAH disease risk prediction model. The technical workflow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The present study is the first to report the roles of these five hub genes in PAH, and integrated multi-omics closed-loop design and receiver operating characteristic (ROC) curve analysis to evaluate their diagnostic value and function. Focus was placed on the key mRNA, \u003cem\u003eNOP58\u003c/em\u003e, and its involvement in the immune microenvironment, potentially offering insights into early molecular diagnosis and treatment strategies for PAH patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAcquisition and Preprocessing of Gene Expression Data\u003c/h2\u003e\u003cp\u003eThe mRNA expression profiles of fresh frozen lung samples from 15 PAH patients and 11 healthy controls were obtained from the GSE113439 dataset in the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The PAH group comprised six patients with IPAH, 4 with PAH secondary to CTD, four with PAH secondary to CHD, and one with chronic thromboembolic PH (CTEPH).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDifferential Gene Expression Analysis\u003c/h3\u003e\n\u003cp\u003eDifferential expression analysis of the genes between PAH patients and healthy controls was performed using the R packages limma, atmap, and plot2. Genes with a threshold of |log2 fold change (FC)| \u0026gt;1 and adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as differentially expressed genes (DEGs). The DEGs were visualized using volcano plots and heatmaps.\u003c/p\u003e\n\u003ch3\u003eConstruction of Co-Expression Network and Identification of Disease-Related Genes\u003c/h3\u003e\n\u003cp\u003eA weighted gene co-expression network was constructed using the R packages limma and WGCNA. The gene expression data underwent clustering analysis to group genes with similar expression patterns into modules, facilitating the visualization of co-expression relationships. Outlier samples were removed based on sample distance plots, and a sample clustering heatmap was generated to confirm the distinction between the control and PAH groups. The optimal soft-thresholding power was selected using the pickSoftThreshold function based on the fitting index and mean connectivity plots. A scale-free network was constructed, and an adjacency matrix was converted into a topological overlap matrix (TOM) to assess gene connectivity. Genes with similar expression patterns were assigned to distinct modules, with a minimum module size of 60 and a merge threshold of 0.25 for combining similar modules. A module-trait correlation heatmap was generated based on the Pearson correlation coefficient (Cor) and corresponding \u003cem\u003eP\u003c/em\u003e-values. Module membership (MM) and gene significance (GS) were calculated for each module, and core genes were identified using the thresholds of MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and GS\u0026thinsp;\u0026gt;\u0026thinsp;0.5. The blue module (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 \u0026times; 10⁻⁹) was identified as the core module most significantly associated with PAH, and its core genes were designated as disease-related key genes.\u003c/p\u003e\n\u003ch3\u003eIdentification of Intersection Genes\u003c/h3\u003e\n\u003cp\u003eThe intersection genes between DEGs and PAH key genes were identified using the R package VennDiagram, generating a Venn diagram to visualize the intersection.\u003c/p\u003e\n\u003ch3\u003eGene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eThe intersection genes underwent Gene Ontology (GO) analysis, encompassing biological process (BP), cellular component (CC), and molecular function (MF). GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to associate DEGs with known biological pathways, elucidating their synergistic roles in PAH pathogenesis and generating hypotheses for subsequent experimental validation. The GO and KEGG pathway analyses used the R packages clusterProfiler, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, enrichplot, ggplot2, circlize, RColorBrewer, dplyr, ggpubr, and ComplexHeatmap, with significance thresholds set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePPI Network and Hub Gene Selection\u003c/h2\u003e\u003cp\u003ePPI network analysis was conducted to integrate DEGs into a functionally synergistic regulatory network, aiding the identification of hub genes and revealing multi-gene interactions. The PPI network was constructed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a default confidence score\u0026thinsp;\u0026gt;\u0026thinsp;0.4, and disconnected nodes were excluded. The topological scores for each gene in the PPI network were calculated using Cytoscape software and the CytoHubba plugin. The top 10 hub genes were selected based on degree scores, and ROC curves were generated for the top five hub genes using the R packages glmnet and pROC.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eqRT-PCR Validation\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from human pulmonary artery smooth muscle cells (HPASMCs) using AG RNAex Pro reagent (Accurate Biology, China). The RNA concentration was measured at 260 nm and 280 nm using a spectrophotometer (NanoDrop 2000, Thermo Fisher Scientific, USA). The RNA was reverse-transcribed to complementary DNA using a SweScript All-in-One RT SuperMix for qPCR (with one-step gDNA removal) (Servicebio, China). Real-time PCR amplification was conducted using a SYBR Green Premix Pro Taq HS qPCR Kit (Rox Plus) (Accurate Biology) on a QuantStudio\u0026trade; 5 Real-Time PCR System (Applied Biosystems\u0026trade;, Thermo Fisher Scientific). Relative RNA levels were normalized to β-actin using the comparative threshold cycle (2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e) method.\u003c/p\u003e\n\u003ch3\u003ePAH Prediction Model Construction\u003c/h3\u003e\n\u003cp\u003eA nomogram for the five validated hub genes was constructed using the R packages rms and rmda to predict disease risk. Calibration curves were generated to assess the accuracy of the prediction model.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMulti-Omics-Driven Immune Microenvironment Analysis\u003c/h2\u003e\u003cp\u003eThe relative abundance of immune cells in each sample from the GSE113439 dataset was estimated using the R packages e1071, BiocManager, and preprocessCore, with 1000 permutations and a significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The immune cell infiltration results were visualized using heatmaps and correlation plots generated by the R packages pheatmap and corrplot. Immune cells with significant differences between PAH and control groups were identified by generating violin plots using the R package vioplot, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significance. Correlation analysis between immune cell infiltration and the hub gene with the highest degree score was performed using the R packages limma, reshape2, ggpubr, and ggExtra, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significant associations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll bioinformatics analyses were conducted using R version 4.3.0 or Strawberry Perl software. Results with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDifferential Expression Analysis\u003c/h2\u003e\u003cp\u003eThe mRNA expression profiles from the fresh frozen lung samples of 15 PAH patients and 11 healthy controls were retrieved from the GSE113439 dataset in the GEO database. The data were merged, batch-normalized, and underwent differential expression analysis between the PAH and control groups. Using thresholds of |log2FC| \u0026gt;1 and adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, a total of 547 DEGs were identified: 453 upregulated and 94 downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of Co-Expression Network and Identification of Disease-Related Genes\u003c/h2\u003e\u003cp\u003eAfter filtering out fluctuating genes, 23,089 genes with a coefficient of variation (CV)\u0026thinsp;\u0026gt;\u0026thinsp;0.1 were selected for WGCNA using R software. Outlier samples were removed, and a sample clustering heatmap was generated. The optimal soft-thresholding power was selected to construct a scale-free network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), followed by gene clustering based on similar expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Dynamic module identification grouped genes with similar expression into modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), which were merged to yield 16 gene modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The pink and grey modules demonstrated positive correlations with PAH traits (Cor\u0026thinsp;=\u0026thinsp;0.53 and 0.57, respectively), while the brown, black, and blue modules exhibited negative correlations (Cor = -0.64, -0.66, and \u0026minus;\u0026thinsp;0.88, respectively). Other modules had absolute Cor\u0026thinsp;\u0026lt;\u0026thinsp;0.5. The blue module had the highest module-trait relationship (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 \u0026times; 10⁻⁹) and was selected as the hub module. Core genes in the blue module were screened using the thresholds of MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and GS\u0026thinsp;\u0026gt;\u0026thinsp;0.5, yielding 3,917 key genes. The intersection of these key genes with DEGs yielded 481 intersection genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). These intersection genes exhibited significant expression changes in PAH patients and tight associations in the co-expression network, and likely contribute synergistically to the core pathological processes of PAH, providing clues for subsequent functional enrichment analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e\u003cp\u003eThe 481 intersection genes underwent GO analysis. The top 10 enriched terms for BP, CC, and MF are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The top three enriched terms were as follows: BP, ribonucleoprotein complex biogenesis, ribosome biogenesis, and rRNA metabolic process; CC, nuclear speck, chromosomal region, and secretory granule lumen; MF, ATP hydrolysis activity, GTPase binding, and another ATP hydrolysis activity term. The KEGG pathway analysis of the 481 intersection genes identified the 19 most significantly enriched pathways (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including focal adhesion, vascular smooth muscle contraction, RNA degradation, ferroptosis, and 2-oxocarboxylic acid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The supplementary materials present the detailed GO and KEGG analysis results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePPI Network and Hub Gene Selection\u003c/h2\u003e\u003cp\u003eThe disease-driving mechanisms of PAH were elucidated from a systems biology perspective using PPI network analysis to convert discrete genes into a dynamic regulatory network, facilitating the identification of key targets. A PPI network for the intersection genes was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The top 10 hub genes based on degree scores were \u003cem\u003eEPRS1\u003c/em\u003e, \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, \u003cem\u003eHSP90AA1\u003c/em\u003e, \u003cem\u003eMTREX\u003c/em\u003e, \u003cem\u003eNCL\u003c/em\u003e, \u003cem\u003ePOLR2B\u003c/em\u003e, and \u003cem\u003eDDX18\u003c/em\u003e. A PPI network for these hub genes was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), and the top five hub genes (\u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, \u003cem\u003eHSP90AA1\u003c/em\u003e) were selected for ROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). All genes exhibited area under the curve (AUC) values\u0026thinsp;\u0026gt;\u0026thinsp;0.85: \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, and \u003cem\u003eHSP90AA1\u003c/em\u003e, AUC\u0026thinsp;=\u0026thinsp;1.000; and \u003cem\u003eDDX21\u003c/em\u003e, AUC\u0026thinsp;=\u0026thinsp;0.939 (all, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eqRT-PCR Validation\u003c/h2\u003e\u003cp\u003eThe expression of hub genes in PAH was validated by extracting total RNA from HPASMCs and analyzing them using qRT-PCR. Consistent with bioinformatics results, \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, and \u003cem\u003eHSP90AA1\u003c/em\u003e expression was significantly upregulated in PAH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These results suggested that these five hub genes may be potential biomarkers for PAH diagnosis, pending validation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePAH Prediction Model\u003c/h2\u003e\u003cp\u003eA nomogram was constructed using the five validated hub genes to predict PAH risk, and calibration curves were generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The close alignment of the nomogram and calibration curves indicated high predictive accuracy. Notably, \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, and \u003cem\u003eCDC5L\u003c/em\u003e had higher composite scores, suggesting greater contributions to PAH risk and potential utility in diagnosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eImmune Microenvironment Analysis in PAH\u003c/h2\u003e\u003cp\u003eImmune cell infiltration was analyzed by calculating the relative abundance of immune cells in each sample from the GSE113439 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), followed by correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Immune cells with significant differences between the PAH and control groups were identified by analyzing the infiltration results (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), and cells with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified: memory B cells, CD8 T cells, follicular helper T cells, activated NK cells, monocytes, activated mast cells, and neutrophils. Correlations between hub genes and immune cells were explored by selecting the hub gene with the highest degree score, \u003cem\u003eNOP58\u003c/em\u003e (degree\u0026thinsp;=\u0026thinsp;46) for correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Three immune cell types demonstrated significant correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE\u0026ndash;G) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05): neutrophils were positively correlated with NOP58 (R\u0026thinsp;=\u0026thinsp;0.61, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), while CD8 T cells and M2 macrophages were negatively correlated (CD8 T cells: R = -0.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0058; M2 macrophages: R = -0.67, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0077).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePAH is a complex and lethal vascular disease characterized by pulmonary vascular remodeling, right heart failure, and immune microenvironment dysregulation\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. PAH diagnosis primarily relies on invasive right heart catheterization, which significantly increases the challenge of timely diagnosis and treatment\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Recent bioinformatics studies have advanced our understanding of PAH. For example, Feng et al. (2024) identified downregulation of the m6A modification enzyme LRPPRC in a PAH rat model, which increased \u003cem\u003eCenpf\u003c/em\u003e mRNA expression and promoted smooth muscle cell proliferation\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, the mechanisms underlying PAH development remain incompletely elucidated.\u003c/p\u003e\u003cp\u003eThe present study aimed to identify key mRNAs in PAH, investigate the immune microenvironment, and construct a disease prediction model to identify hub genes that could aid PAH diagnosis and treatment while clarifying its pathogenesis. Differential expression analysis of mRNA between PAH patients and healthy controls identified 547 DEGs: 453 upregulated and 94 downregulated. A co-expression network was constructed, and the hub module was intersected with the DEGs to obtain 481 intersection genes. GO analysis revealed the critical roles of ribosome biogenesis, metabolism, and related biological processes in PAH. KEGG analysis indicated that the focal adhesion, vascular smooth muscle contraction, RNA degradation, ferroptosis, and 2-oxocarboxylic acid metabolism pathways are closely associated with PAH progression. The protein interactions encoded by DEGs were explored by constructing a PPI network, which identified \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, and \u003cem\u003eHSP90AA1\u003c/em\u003e as key components of the upregulated module (hub genes). These hub genes were significantly upregulated in PAH patients, with ROC curve analysis showing AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.85, particularly AUC\u0026thinsp;=\u0026thinsp;1.000 for \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, and \u003cem\u003eHSP90AA1\u003c/em\u003e, indicating high diagnostic specificity and sensitivity.\u003c/p\u003e\u003cp\u003eNOP58 is a nucleolar protein and core component of the small nucleolar ribonucleoprotein (snoRNP) complex that provides a scaffold for snoRNP assembly and is critical in RNA processing and ribosome biogenesis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The present study is the first to report its upregulation in PAH patients, suggesting a potential association with PAH development. Correlation analysis of NOP58 with immune cells revealed significant associations with neutrophils (positive), CD8 T cells (negative), and M2 macrophages (negative), indicating its potential role in regulating the immune microenvironment. Similarly, DDX21, an RNA helicase involved in ATP binding and RNA unwinding\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, was upregulated in PAH. While Miao et al. (2023) reported its role in epidermal differentiation\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, the DDX21 function in pulmonary artery endothelial cells remains unclear, and the present study provided new insights into its relevance in PAH.\u003c/p\u003e\u003cp\u003eCDC5L is a cell cycle regulator involved in mitosis, and reduces tumor cell viability when downregulated\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Whether CDC5L promotes the abnormal proliferation of pulmonary vascular smooth muscle cells in PAH is unconfirmed, but its positive correlation with PAH in the present study offered a novel perspective. ABCE1 is an ATP-binding cassette transporter that regulates ribosome recycling and mitochondrial oxidative phosphorylation, and inhibits small cell lung cancer proliferation and invasion when silenced\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The ABCE1 upregulation in PAH suggested that silencing ABCE1 may mitigate PAH progression, providing a new research direction. HSP90AA1 encodes a heat shock protein critical for protein homeostasis, and is associated with cell death mechanisms, including ferroptosis and pyroptosis, which are pivotal in cellular stress and inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The synergistic actions of these hub genes likely drive pulmonary vascular remodeling through the focal adhesion, ferroptosis, and 2-oxocarboxylic acid metabolism pathways, offering new insights into PAH molecular mechanisms.\u003c/p\u003e\u003cp\u003eThe CIBERSORT analysis revealed significant differences in the infiltration levels of memory B cells, CD8 T cells, follicular helper T cells, activated NK cells, monocytes, activated mast cells, and neutrophils between PAH patients and controls. Correlation analysis with NOP58 demonstrated a positive correlation with neutrophils and negative correlations with CD8 T cells and M2 macrophages. These results underscored the role of immune microenvironment dysregulation in PAH pathogenesis. Previous studies have highlighted inflammation and immune cell infiltration as key pathological features of PAH. For example, Rabinovitch et al. (2014) demonstrated the critical roles of macrophages and T cells in vascular remodeling by secreting inflammatory cytokines such as IL-6 and TNF-α, which promote smooth muscle cell proliferation\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The immune correlation analysis in the present study confirmed that NOP58 may influence the inflammatory microenvironment by regulating neutrophils, CD8 T cells, and M2 macrophages, which was consistent with the results by Jandl et al. (2022) on immune regulation in PAH\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, the ferroptosis pathway enrichment suggested that iron-dependent programmed cell death may contribute to the PAH immune microenvironment. Ferroptosis is linked to oxidative stress and inflammation and has been implicated in pulmonary artery endothelial cell dysfunction, smooth muscle cell proliferation, and right ventricular hypertrophy\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The present study provides multidimensional evidence for the complexity of the PAH immune microenvironment, and single-cell RNA sequencing could elucidate the functions of specific immune cell subsets.\u003c/p\u003e\u003cp\u003eA PAH disease risk prediction model was constructed based on \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, and \u003cem\u003eHSP90AA1\u003c/em\u003e, with high accuracy validated by nomogram and calibration curves. \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, and \u003cem\u003eCDC5L\u003c/em\u003e demonstrated higher contributions, suggesting their potential as biomarkers for early PAH diagnosis. Compared to traditional right heart catheterization, this model reduces diagnostic complexity through non-invasive gene expression analysis. Similar to Piper et al.'s (2024) results regarding RAB7 as a potential PH target\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, the present study emphasizes the importance of molecular biomarkers in early disease screening. However, the clinical applicability of the model requires validation in larger, diverse PAH cohorts (e.g., IPAH, HPAH).\u003c/p\u003e\u003cp\u003eThe present study provided novel targets and diagnostic tools for precision PAH management through multi-omics analysis and experimental validation. The identification of hub genes such as \u003cem\u003eNOP58\u003c/em\u003e holds potential for developing targeted therapies, such as inhibiting \u003cem\u003eNOP58\u003c/em\u003e to modulate RNA metabolism and the immune microenvironment, thereby attenuating vascular remodeling. The prediction model based on these hub genes could facilitate early screening in high-risk populations, addressing the limitations of imaging and invasive diagnostics. However, the present study was subject to limitations. First, the sample size (15 PAH patients and 11 controls) may have limited its generalizability, necessitating validation in larger, multi-center cohorts. Second, the study focused on mRNA expression and did not analyze non-coding RNA interactions (e.g., miRNA, lncRNA) with hub genes. Finally, qRT-PCR validation was performed only in pulmonary artery endothelial cells; hence, future studies should validate these findings in other cell types (e.g., smooth muscle cells, fibroblasts) to comprehensively elucidate their roles in PAH pathogenesis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study systematically analyzed the GSE113439 dataset, integrating WGCNA, PPI network analysis, and qRT-PCR validation to identify five hub genes (\u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, \u003cem\u003eHSP90AA1\u003c/em\u003e). These hub genes were confirmed to be significantly upregulated in PAH and demonstrated potential as diagnostic biomarkers. GO and KEGG analyses revealed their close associations with the focal adhesion, vascular smooth muscle contraction, RNA degradation, and ferroptosis pathways, providing novel insights into the molecular mechanisms of PAH. Immune microenvironment analysis indicated significant correlations between \u003cem\u003eNOP58\u003c/em\u003e and the infiltration levels of neutrophils, CD8 T cells, and M2 macrophages, suggesting its potential role in modulating inflammation and immune responses in PAH pathogenesis. A disease risk prediction model constructed based on the five hub genes exhibited high accuracy, providing a non-invasive tool for early PAH diagnosis. Future studies should focus on large-scale clinical validation and multi-omics approaches to explore the therapeutic potential of these hub genes, advancing the development of precision medicine for PAH.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003cp\u003eThis study was approved by the Academic Advisory Board of the First Affiliated Hospital of Zhengzhou University (approval no. 2024-KY-0468). All participants provided their written informed consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eData Sharing Statement\u003c/h2\u003e\u003cp\u003eAll data involved in this study are available from the corresponding author on request. The original GEO dataset (GSE113439) used for analysis in this study is available in the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Publish Declaration\u003c/strong\u003e\u003cp\u003e\u003cb\u003e\u003c/b\u003e Not applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the Key Scientific Research Project of Science and Technology Department of Henan Province [grant number 231111310800].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ. C designed the experiments. R.J, K.W, Y.L, Y. M, and Y.Z.L analyzed the data and wrote the manuscript. P.L, Y.W, T.J, and L.D provided helpful discussions and reviewed the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all patients who participated in this study for their cooperation. We also acknowledge the Henan Key Laboratory of Pharmacology for Liver Diseases for providing experimental platform support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data involved in this study are available from the corresponding author on request. The original GEO dataset (GSE113439) used for analysis in this study is available in the GEO database (http://www.ncbi.nlm.nih.gov/geo).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHassoun PM (2021) Pulmonary Arterial Hypertension. N Engl J Med 385(25):2361-2376. doi:10.1056/NEJMra2000348\u003c/li\u003e\n\u003cli\u003eHumbert M, Kovacs G, Hoeper MM, et al. (2022) 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J 43(38):3618-3731. doi:10.1093/eurheartj/ehac237\u003c/li\u003e\n\u003cli\u003eWu X, Suo S, Su X, et al. Trends in pulmonary arterial hypertension: insights from Global Burden of Disease 1990-2021. \u003cem\u003eBMJ Open\u003c/em\u003e. Mar 18 2025;15(3):e095348. doi:10.1136/bmjopen-2024-095348\u003c/li\u003e\n\u003cli\u003eSimonneau G, Montani D, Celermajer DS, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. \u003cem\u003eEur Respir J\u003c/em\u003e. Jan 2019;53(1)doi:10.1183/13993003.01913-2018\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Alonzo GE, Barst RJ, Ayres SM, et al. Survival in patients with primary pulmonary hypertension. Results from a national prospective registry. \u003cem\u003eAnn Intern Med\u003c/em\u003e. Sep 1 1991;115(5):343-9. doi:10.7326/0003-4819-115-5-343\u003c/li\u003e\n\u003cli\u003eBoucly A, Savale L, Ja\u0026iuml;s X, et al. Association between Initial Treatment Strategy and Long-Term Survival in Pulmonary Arterial Hypertension. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e. Oct 1 2021;204(7):842-854. doi:10.1164/rccm.202009-3698OC\u003c/li\u003e\n\u003cli\u003eZolty R. Advances in the discovery of drugs that treat pulmonary arterial hypertension. \u003cem\u003eExpert Opin Drug Discov\u003c/em\u003e. Apr 2023;18(4):445-466. doi:10.1080/17460441.2023.2192919\u003c/li\u003e\n\u003cli\u003eJandl K, Marsh LM, Mutgan AC, et al. Impairment of the NKT-STAT1-CXCL9 Axis Contributes to Vessel Fibrosis in Pulmonary Hypertension Caused by Lung Fibrosis. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e. Oct 15 2022;206(8):981-998. doi:10.1164/rccm.202201-0142OC\u003c/li\u003e\n\u003cli\u003ePiper B, Bogamuwa S, Hossain T, et al. RAB7 deficiency impairs pulmonary artery endothelial function and promotes pulmonary hypertension. \u003cem\u003eJ Clin Invest\u003c/em\u003e. Feb 1 2024;134(3)doi:10.1172/jci169441\u003c/li\u003e\n\u003cli\u003eZhou X, Liang B, Lin W, Zha L. Identification of MACC1 as a potential biomarker for pulmonary arterial hypertension based on bioinformatics and machine learning. \u003cem\u003eComput Biol Med\u003c/em\u003e. May 2024;173:108372. doi:10.1016/j.compbiomed.2024.108372\u003c/li\u003e\n\u003cli\u003eGentles AJ, Newman AM, Liu CL, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. \u003cem\u003eNat Med\u003c/em\u003e. Aug 2015;21(8):938-945. doi:10.1038/nm.3909\u003c/li\u003e\n\u003cli\u003eWu XG, Chen JJ, Zhou HL, et al. Identification and Validation of the Signatures of Infiltrating Immune Cells in the Eutopic Endometrium Endometria of Women With Endometriosis. \u003cem\u003eFront Immunol\u003c/em\u003e. 2021;12:671201. doi:10.3389/fimmu.2021.671201\u003c/li\u003e\n\u003cli\u003eLannan KL, Phipps RP, White RJ. Thrombosis, platelets, microparticles and PAH: more than a clot. \u003cem\u003eDrug Discov Today\u003c/em\u003e. Aug 2014;19(8):1230-5. doi:10.1016/j.drudis.2014.04.001\u003c/li\u003e\n\u003cli\u003eRuopp NF, Cockrill BA. Diagnosis and Treatment of Pulmonary Arterial Hypertension: A Review. \u003cem\u003eJama\u003c/em\u003e. Apr 12 2022;327(14):1379-1391. doi:10.1001/jama.2022.4402\u003c/li\u003e\n\u003cli\u003eFerrian S, Cao A, McCaffrey EF, et al. Single-Cell Imaging Maps Inflammatory Cell Subsets to Pulmonary Arterial Hypertension Vasculopathy. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e. Jan 15 2024;209(2):206-218. doi:10.1164/rccm.202209-1761OC\u003c/li\u003e\n\u003cli\u003eFeng Y, Yu Z, Tang M, et al. Transcriptome-Wide N6-Methyladenosine Alternations in Pulmonary Arteries of Monocrotaline-Induced Pulmonary Arterial Hypertension in Rats and Novel Therapeutic Targets. \u003cem\u003eBiomedicines\u003c/em\u003e. Feb 4 2024;12(2)doi:10.3390/biomedicines12020364\u003c/li\u003e\n\u003cli\u003eJackson GD, Cooper GN. Immune responses of rats to live Vibrio cholerae: secretion of antibodies in bile. \u003cem\u003eParasite Immunol\u003c/em\u003e. Summer 1981;3(2):127-35. doi:10.1111/j.1365-3024.1981.tb00390.x\u003c/li\u003e\n\u003cli\u003eSong C, Hotz-Wagenblatt A, Voit R, Grummt I. SIRT7 and the DEAD-box helicase DDX21 cooperate to resolve genomic R loops and safeguard genome stability. \u003cem\u003eGenes Dev\u003c/em\u003e. Jul 1 2017;31(13):1370-1381. doi:10.1101/gad.300624.117\u003c/li\u003e\n\u003cli\u003eMiao W, Porter DF, Lopez-Pajares V, et al. Glucose dissociates DDX21 dimers to regulate mRNA splicing and tissue differentiation. \u003cem\u003eCell\u003c/em\u003e. Jan 5 2023;186(1):80-97.e26. doi:10.1016/j.cell.2022.12.004\u003c/li\u003e\n\u003cli\u003eMu R, Wang YB, Wu M, et al. Depletion of pre-mRNA splicing factor Cdc5L inhibits mitotic progression and triggers mitotic catastrophe. \u003cem\u003eCell Death Dis\u003c/em\u003e. Mar 27 2014;5(3):e1151. doi:10.1038/cddis.2014.117\u003c/li\u003e\n\u003cli\u003eHuang B, Gao Y, Tian D, Zheng M. A small interfering ABCE1-targeting RNA inhibits the proliferation and invasiveness of small cell lung cancer. \u003cem\u003eInt J Mol Med\u003c/em\u003e. May 2010;25(5):687-93. \u003c/li\u003e\n\u003cli\u003eLiu S, Xu Y, Yao X, et al. Perillaldehyde ameliorates sepsis-associated acute kidney injury via inhibiting HSP90AA1-mediated ferroptosis and pyroptosis: Molecular structure and protein interaction of HSP90AA1. \u003cem\u003eInt J Biol Macromol\u003c/em\u003e. Apr 2025;304(Pt 2):140954. doi:10.1016/j.ijbiomac.2025.140954\u003c/li\u003e\n\u003cli\u003eRabinovitch M, Guignabert C, Humbert M, Nicolls MR. Inflammation and immunity in the pathogenesis of pulmonary arterial hypertension. \u003cem\u003eCirc Res\u003c/em\u003e. Jun 20 2014;115(1):165-75. doi:10.1161/circresaha.113.301141\u003c/li\u003e\n\u003cli\u003eZhang L, Jia R, Li H, et al. Insight into the Double-Edged Role of Ferroptosis in Disease. \u003cem\u003eBiomolecules\u003c/em\u003e. Nov 30 2021;11(12)doi:10.3390/biom11121790\u003c/li\u003e\n\u003cli\u003eGe Q, Zhang T, Yu J, et al. A new perspective on targeting pulmonary arterial hypertension: Programmed cell death pathways (Autophagy, Pyroptosis, Ferroptosis). \u003cem\u003eBiomed Pharmacother\u003c/em\u003e. Dec 2024;181:117706. doi:10.1016/j.biopha.2024.117706\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary arterial hypertension, mRNA, CIBERSORT, weighted gene co-expression network analysis, NOP58, diagnostic biomarker genes","lastPublishedDoi":"10.21203/rs.3.rs-7557161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7557161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent pulmonary arterial hypertension (PAH) diagnostic approaches rely on right heart catheterization to measure the mean pulmonary artery pressure (\u0026ge;\u0026thinsp;20 mmHg), but limit early screening. Imaging techniques lack sensitivity for detecting early pulmonary pressure changes and are subject to variability, often resulting in diagnosis at an irreversible stage. The PAH pathogenesis remains incompletely understood, and improved diagnosis and treatment are urgently needed. In the present study, the Gene Expression Omnibus GSE113439 dataset underwent differential expression analysis of mRNA and Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using weighted gene co-expression network and protein\u0026ndash;protein interaction network analyses. A hub gene-based PAH disease risk prediction model was constructed, followed by immune cell infiltration and correlation analyses. The hub gene expression was validated using qRT-PCR. PAH involved 547 differentially expressed genes. GO and KEGG enrichment analyses revealed that the focal adhesion, vascular smooth muscle contraction, RNA degradation, ferroptosis, and 2-oxocarboxylic acid metabolism pathways were closely associated with PAH development (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). PAH patients had significantly upregulated \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, and \u003cem\u003eHSP90AA1\u003c/em\u003e expression. Memory B cells, CD8 T cells, follicular helper T cells, activated natural killer cells, monocytes, activated mast cells, and neutrophils were significantly different between PAH patients and controls. Neutrophils, macrophages, and \u003cem\u003eNOP58\u003c/em\u003e expression were closely associated. \u003cem\u003eNOP58\u003c/em\u003e, \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eABCE1\u003c/em\u003e, \u003cem\u003eCDC5L\u003c/em\u003e, and \u003cem\u003eHSP90AA1\u003c/em\u003e may be novel PAH diagnostic and therapeutic targets. Their clinical applicability should be validated in larger-sample studies to explore gene-guided personalized therapies.\u003c/p\u003e","manuscriptTitle":"Bioinformatics Analysis and Experimental Validation to Identify Hub Genes in Pulmonary Arterial Hypertension","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:52:13","doi":"10.21203/rs.3.rs-7557161/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-14T16:44:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T20:53:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T05:53:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T15:38:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277076612647723456719346571580122336173","date":"2025-10-22T21:53:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T18:06:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79908483989627613882117346579875264045","date":"2025-10-21T06:18:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315005287149916953945468467920915481078","date":"2025-10-21T05:40:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1732602924772210943320790518688864093","date":"2025-09-25T15:01:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T14:58:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T16:20:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T14:56:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-09-07T15:04:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1cba703d-2fd7-4eb8-99f9-bc32e949bd19","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:03:01+00:00","versionOfRecord":{"articleIdentity":"rs-7557161","link":"https://doi.org/10.1186/s40001-026-04013-8","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-02-21 15:57:29","publishedOnDateReadable":"February 21st, 2026"},"versionCreatedAt":"2025-10-08 09:52:13","video":"","vorDoi":"10.1186/s40001-026-04013-8","vorDoiUrl":"https://doi.org/10.1186/s40001-026-04013-8","workflowStages":[]},"version":"v1","identity":"rs-7557161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7557161","identity":"rs-7557161","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}