{"paper_id":"2ebf7afd-5bcc-48c3-af35-ac568d0482ea","body_text":"Identification of GATA2-AS1- hsa-miR-125b-5p-PLAU ceRNA regulatory network as novel biomarkers for prethrombotic state in patients with coronary artery disease | 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 Article Identification of GATA2-AS1- hsa-miR-125b-5p-PLAU ceRNA regulatory network as novel biomarkers for prethrombotic state in patients with coronary artery disease Jinwen Luo, Jie Gao, Lei Zhang, Xuanqing Fan, Kangkang Wei, Min Liu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7671890/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Current diagnostic tools lack specificity for identifying the prethrombotic state (PTS), a critical precursor to coronary thrombosis in coronary artery disease (CAD). This study aimed to discover and validate specific multi-omics biomarkers for PTS detection to enable early intervention. Integrated transcriptomic and proteomic analyses were performed on peripheral blood mononuclear cells (PBMCs) and plasma from a discovery cohort (n = 28), including healthy controls, stable CAD patients without PTS (NPTS), with PTS, and acute myocardial infarction (AMI) patients. Multi-omics profiling revealed 43 proteins, 334 mRNAs, 27 miRNAs, 253 lncRNAs, and 8 circRNAs as candidate biomarkers, from which a complement and coagulation cascade–related ceRNA subnetwork was extracted. Within this network, nine hub RNAs and two proteins were prioritized, and validation in an independent cohort (n = 40) identified the GATA2-AS1/miR-125b-5p/PLAU axis together with FVIII protein as significantly dysregulated in PTS. These biomarkers showed strong diagnostic performance for distinguishing PTS from NPTS: FVIII (AUC 0.88, 95% CI 0.73-1.00), PLAU (AUC 0.95, 95% CI 0.86-1.00), hsa-miR-125b-5p (AUC 0.78, 95% CI 0.57–0.99), and GATA2-AS1 (AUC 0.89, 95% CI 0.74-1.00). The GATA2-AS1/miR-125b-5p/PLAU-FVIII panel enables accurate PTS detection for early intervention. Health sciences/Biomarkers Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics competing endogenous RNA prethrombotic state coronary artery disease biomarker multi-omics lncRNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Coronary artery disease (CAD) remains a leading cause of global mortality, with acute coronary thrombosis representing the critical terminal event precipitating acute myocardial infarction (AMI) 1 . A recent study showed that 50.5% of first-time AMI patients had no symptoms before the event 2 , highlighting the unpredictability and prevention challenges of AMI. The prethrombotic state (PTS), characterized by endothelial dysfunction, platelet hyperactivity and coagulation-anticoagulation imbalance, serves as critical precursor to coronary thrombosis 3 . Current diagnostic paradigms relying on conventional coagulation parameters lack specificity for detecting PTS in stable CAD patients, failing to identify high-risk individuals before thrombotic crises occur 4 . Recent advances in multi-omics technologies have revealed that diverse classes of non-coding RNAs (ncRNAs)—including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs)—serve as key regulators in thrombogenesis and related cardiovascular pathophysiological processes through epigenetic, transcriptional, and post-transcriptional mechanisms 5 . Notably, the competing endogenous RNA (ceRNA) hypothesis has emerged as an important regulatory mechanism, where lncRNAs, circRNAs, and other transcripts can act as molecular sponges for miRNAs, thereby modulating the expression of miRNA target genes such as protein-coding mRNAs 6 – 8 . Dysregulation of ceRNA networks has been implicated in thrombosis 9 , though their role in PTS remains underexplored. These multi-omics innovations have further enabled the discovery of novel CAD biomarkers. For instance, transcriptomic profiling identified monocyte-derived lncRNA OTTHUMT00000387022 as a circulating diagnostic marker for CAD 10 , while hsa_circ_0001879 and hsa_circ_0004104 have been identified as CAD-associated signatures 11 . Similarly, proteomic studies revealed hemopexin, leucine-rich α-2-glycoprotein, vitronectin, and fibronectin as key proteins associated with acute coronary syndromes 12 . Nevertheless, existing studies have predominantly focused on isolated disease states, typically comparing AMI or stable CAD to healthy controls 13 , 14 . A critical knowledge gap persists: the dynamic evolution of multi-omics across the disease continuum from health (HEALTH)→ stable CAD without PTS (NPTS)→ stable CAD with PTS → AMI remains uncharacterized, particularly during the clinically actionable yet diagnostically challenging PTS phase. Moreover, the potential of integrated RNA-protein signatures and ceRNA regulatory networks as PTS-specific biomarkers remains unexplored. To address these gaps, we employed a systems biology approach integrating transcriptomic (mRNA, lncRNA, miRNA, circRNA) and proteomic profiling across four clinical stages: HEALTH, NPTS, PTS, and AMI. This study aimed to discover and validate novel multi-omics biomarkers for the specific detection of PTS in stable CAD. Materials and methods Patients and study design This observational study was conducted at Xiyuan Hospital, China Academy of Chinese Medical Sciences, between July 2022 and October 2023. Participants were allocated into four groups based on clinical diagnosis and functional assessments: (1) healthy controls (HEALTH, n = 17): no psychiatric or physical disorders, nailfold microcirculation (NM) score < 4, and maximum platelet aggregation rate (MPAR) < 65%; (2) Stable CAD without PTS (NPTS, n = 17): meeting ESC guidelines 15 with MPAR < 65% and NM score < 4; (3) Stable CAD with PTS (PTS, n = 17): meeting ESC guidelines 15 with MPAR ≥ 65% and NM score ≥ 4 (aligns with the criteria in previous study 16 ); (4) AMI (n = 17): diagnosed according to the Fourth Universal Definition of MI 17 and representing acute thrombosis. Exclusion criteria: severe comorbidities (left ventricular ejection fraction < 35%, arrhythmia requiring intervention, liver/kidney dysfunction), rheumatoid arthritis, tuberculosis, malignancy, recent (< 6 months) stroke, active infections, and prior anticoagulant use. NM assessment is based on a published method 18 . Detailed methodologies are described in the Supplemental Materials . Eligible participants were stratified by diagnosis and randomly assigned to discovery or verification cohorts. The discovery cohort (n = 28, 7/group) underwent whole-transcriptome sequencing of peripheral blood mononuclear cells (PBMCs) and plasma proteomic profiling via data-independent acquisition (DIA). Four samples failed to extract sufficient PBMCs. The verification cohort (n = 40, 10/group) was used to validate candidate biomarkers by quantitative polymerase chain reaction (qPCR) and enzyme-linked immunosorbent assay (ELISA) (Fig. 1 ). Baseline characteristics of study participants are presented in Table 1. The study was approved by the Ethics Committee of Xiyuan Hospital of China Academy of Chinese Medical Sciences (2022XLA103-1). All methods in this study were performed in accordance with the Declaration of Helsinki. Written informed consents were obtained from all participants prior to the study's initiation. Sample collection and processing Peripheral venous blood (10 mL) was collected from fasting subjects (≥ 8 hours) using ethylene diamine tetraacetic acid (vacuum tubes. For CAD patients, blood was drawn within 1 hour before coronary angiography to preclude procedural interference. Plasma was isolated via centrifugation at 2,000 × g for 15 min at 4°C and stored at − 80°C. PBMCs were purified from the remaining blood by density gradient centrifugation using Ficoll-Paque™ PLUS (GE Healthcare, Sweden), according to the manufacturer’s protocol. PBMC pellets were immediately homogenized in TRIzol™ Reagent (Invitrogen, USA) at a 1:3 volumetric ratio (pellet:TRIzol) to stabilize RNA. All samples were aliquoted into cryovials and flash-frozen at − 80°C within 2 hours of collection. RNA sequencing analysis Whole-transcriptome RNA sequencing of PBMC-derived RNA was performed by Novogene Co., Ltd (Beijing, China). Total RNA was extracted using TRNzol Universal Reagent (Tiangen, China) following the manufacturer’s protocol. RNA quality control encompassed purity assessment via agarose gel electrophoresis, concentration measurement using a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, USA), and integrity evaluation with an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Qualified RNA samples underwent library preparation using the NEBNext Ultra™ Directional RNA Library Prep Kit (NEB #E7420, USA) for lncRNA and the NEBNext® Multiplex Small RNA Library Prep Set (NEB #E7300L, USA) for small RNA. Library quality was verified through insert size distribution analysis (Agilent 2100 Bioanalyzer) and effective concentration quantification (> 2 nM) via qPCR (KAPA Biosystems). Libraries were pooled and sequenced on an Illumina NovaSeq 6000 platform (Illumina, USA) with PE150 for lncRNA and SE50 for small RNA. Raw FASTQ files were preprocessed using in-house Perl scripts to remove adapters, filter poly-N reads, and discard low-quality reads. Clean data quality was assessed via Q20, Q30, and GC content metrics. CircRNAs were identified using find_circ and CIRI2 6,19 . Expression quantification was performed with Transcripts Per Million (TPM) for miRNA and circRNA, and Fragments Per Kilobase per Million mapped reads (FPKM) for lncRNA and mRNA. DIA proteomic analysis Proteomic analysis of plasma was performed by Novogene Co., Ltd. (Beijing, China). High-abundance proteins were depleted from plasma samples using a ProteoMiner™ Low Abundance Protein Enrichment Kit (Bio-Rad, #1633007). Proteins were lysed in 8 M urea/100 mM TEAB (pH 8.5), reduced with 10 mM DTT (Sigma, #D9163) at 56°C for 1 h, and alkylated with 20 mM iodoacetamide (IAM, Sigma, #I6125) in darkness for 1 h. Protein concentration was quantified via Bradford assay (Beyotime) with quality assessed by SDS-PAGE. After tryptic digestion (Promega, #V5280), peptides were desalted using C18 cartridges and lyophilized. Pooled peptides underwent high-pH fractionation (BEH C18 column, 4.6×250 mm) at 45°C. Resulting fractions were lyophilized and reconstituted in 0.1% formic acid. For DDA library construction and DIA analysis, 200 ng peptides were separated via nanoElute UHPLC (25 cm×75 µm column; Table 4 gradient) and analyzed on a timsTOF Pro 2 (PASEF mode; CaptiveSpray 1.5 kV; *m/z* 100–1700 scan). DDA library employed MS/MS topN fragmentation with a 1.17 sec cycle time and 2,500 intensity threshold. DIA acquisition used 25 Da mass isolation windows and two mobility windows. Proteins were identified and quantified using Spectronaut Pulsar (Biognosys, v16.0) against the UniProt database. Search parameters included a 10 ppm precursor mass tolerance, 0.02 Da fragment mass tolerance, fixed carbamidomethylation (cysteine), variable modifications including methionine oxidation and N-terminal acetylation, and a maximum of two missed cleavages, with false discovery rate (FDR) set at 1% for both peptide and protein levels. qPCR Total RNA was extracted from tissues, cells, or blood using an UltraPure RNA Kit (Tiangen) with homogenization in RLT buffer, chloroform phase separation, and column purification. RNA concentration/purity was measured via NanoDrop ND-2000 (A260/A280 ≥ 1.8). cDNA was synthesized from 1 µg RNA using HiScript III Reverse Transcriptase (Vazyme) with oligo (dT)/random hexamers, while miRNA cDNA utilized polyadenylation and miRNA-specific RT primers (Tiangen, KR201). qPCR reactions contained 10 µL 2×AceQ SYBR Master Mix (Vazyme), 0.4 µM primers, and 2 µL cDNA template in 20 µL volumes. Amplification proceeded on a real-time PCR system (95°C/10 min; 45 cycles: 95°C/15 s, 60°C/60 s) with melt curve analysis (60–99°C). Gene expression of mRNA, lncRNA and circRNA was normalized to GAPDH, miRNA expression was normalized to U6. All quantifications used the standard 2 − ΔΔCt method. The primers used in the qPCR of RNA were listed in Supplementary Table 2. All measurements were performed in triplicate. ELISA Plasma concentrations of coagulation factor VIII (FVIII), complement factor H (CFH), complement component 3a (C3a), complement component 5a (C5a), thrombin–antithrombin complex (TAT), and thrombus precursor protein (TpP) were quantified using commercial ELISA kits (Shanghai Enzyme-linked Biotechnology Co., China) according to the manufacturer’s instructions. Plasma samples were diluted five-fold in phosphate-buffered saline and analyzed using a Rayto RT-6100 microplate reader. Statistical and bioinformatics analyses Statistical analyses were performed using SPSS 27.0 (IBM) with visualization in GraphPad Prism 10.0.2. Normally distributed data were presented as mean ± standard deviation (SD) and analyzed by one-way ANOVA after confirmation of normality (Shapiro-Wilk test). Homoscedasticity was verified using Levene's test; post hoc analyses employed the LSD test for homoscedastic data and Tamhane's T2 for heteroscedastic data. Non-normally distributed data underwent Kruskal-Wallis testing with Dunn's post hoc comparisons. Extreme outliers (> 3 × interquartile range) were excluded from all groups, with sensitivity analyses confirming result robustness. All tests were two-tailed; p < 0.05 defined statistical significance. Diagnostic performance of biomarkers was assessed via receiver operating characteristic (ROC) curve analysis, reporting area under the curve (AUC). Transcriptomic and proteomic data were analyzed via principal component analysis (PCA) using Factoextra ((v1.0.7) R packages. Differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), lncRNAs (DElncRNAs), and circRNAs (DEcircRNAs) were identified using edgeR (v3.22.5) R package with significance threshold of p < 0.05. The protein quantitation results were statistically analyzed by T-test. The proteins whose quantitation significantly different between experimental and control groups ( p < 0.05) were defined as differentially expressed proteins (DEPs). Functional enrichment analyses for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using clusterProfiler (v4.10.0), along with Gene Set Enrichment Analysis (GSEA) using the KEGG gene sets. Procrustes analysis, a geometric method aligning multidimensional configurations via rotation/scaling/translation, minimizes the Procrustes distance to quantify structural concordance between datasets 20 , 21 . Implemented with the vegan R package (v2.6.4), Procrustes analysis was applied to transcriptomic and proteomic profiles to identify shared and divergent biological patterns across omics layers. ceRNA interactions were predicted using the ENCORI 22 , TarBase 9.0 23 , TargetScan 7.2 24 , PITA 25 , and miRanda 26 databases. Networks were visualized in Cytoscape (v3.8.2). Sankey diagram was drawn using the ggalluvial (v0.12.5) package in R. Results Transcriptomic and proteomic profiling PCA of transcriptomic and proteomic data demonstrated distinct clustering patterns across CAD stages (HEALTH, NPTS, PTS, AMI) (Fig. 2 A-E). proteins, mRNAs, miRNAs, and lncRNAs demonstrated distinct intergroup clustering with partial overlap (Fig. 2 A-D), while circRNAs exhibited minimal discriminatory capacity without significant clustering differences (Fig. 2 E). To identify stage-specific molecular alterations, cross-comparisons of transcriptomic and proteomic profiles were performed across four groups. Proteomic differential expression analysis revealed progressively increasing numbers of DEPs with CAD advancement (Supplementary Table 3): 61 DEPs in NPTS vs HEALTH, 122 in PTS vs HEALTH, and 152 in AMI vs HEALTH. This pattern indicates significant proteomic reprogramming during transition to the PTS phase, intensifying further upon infarction. Crucially, transcriptomic profiling of PTS vs HEALTH demonstrated extensive dysregulation, including 2,871 DEmRNAs (Fig. 2 G), 117 DEmiRNAs (Fig. 2 H), 1,925 DElncRNAs (Fig. 2 I), and 108 DEcircRNAs (Fig. 2 J). Volcano plots for other comparison groups are provided in Supplementary Fig. 1–4, with complete differential expression statistics in Supplementary Table 3. Functional dynamics across disease stages Functional analysis of multi-omics profiles revealed stage-specific pathway activation patterns during CAD progression. In NPTS, upregulated DEPs were predominantly enriched in complement and coagulation cascades (Fig. 3 A), while DEmRNAs featured fluid shear stress responses (Fig. 3 B) and DEncRNAs targeted vascular smooth muscle contraction and actin cytoskeleton regulation (Supplementary Fig. 6A). During PTS, upregulated DEPs again demonstrated complement and coagulation cascades pathway enrichment (Fig. 3 A), consistent with the GSEA results (NES = 1.83, p < 0.01; Supplementary Fig. 7B). Concurrent transcriptomic analyses revealed DEmRNAs associated with apoptosis and lipid metabolism (Fig. 3 B), and DEncRNAs implicated in cellular senescence (Supplementary Fig. 6B). At AMI, DEPs showed sustained complement and coagulation cascade activation (Fig. 3 A), corroborated by GSEA (NES = 2.22, p < 0.01; Supplementary Fig. 7C). Both DEmRNAs and DEncRNAs converged on nuclear factor-kappaB (NF-κB) signaling and tumor necrosis factor (TNF) signaling pathways (Fig. 3 B, Supplementary Fig. 6C). Plasma biomarker quantification confirmed progressive complement and coagulation system activation across stages (Fig. 3 C). Collectively, these findings indicate that thrombotic progression is driven by a shift from quantitative to qualitative alterations in these molecular responses. Procrustes analysis of transcriptome and proteome To investigate cross-omics coordination during thrombotic progression, we applied Procrustes analysis to RNA-RNA and RNA-protein expression matrices (Fig. 4 ). Significant alignment was observed between lncRNA and mRNA profiles (M² = 0.225, p = 0.001), indicating synchronized transcriptional regulation (Fig. 4 F). mRNA- miRNA (M² = 0.838, p = 0.03), mRNA-circRNA (M² = 0.776, p = 0.008), lncRNA- circRNA (M² = 0.703, p = 0.001), miRNA- lncRNA (M² = 0.801, p = 0.023), miRNA-circRNA (M² = 0.838, p = 0.044) also showed significant concordance (Fig. 4 E, G-J). In contrast, RNA–protein associations displayed relatively weak spatial alignment (Fig. 4 A-D). Although the protein-miRNA pair showed borderline significance (M² = 0.858, p = 0.049), protein-mRNA (M² = 0.856, p = 0.064), protein-lncRNA (M² = 0.89, p = 0.118), and protein-circRNA (M² = 0.935, p = 375) interactions were not statistically significant. Procrustes analysis revealed strong intra-transcriptomic coordination and relatively decoupled transcript–protein relationships, underscoring the complexity of regulatory control across molecular layers in thrombotic progression. Candidate biomarker discovery A multi-omics filtering strategy was implemented to identify PTS-specific biomarkers. Differentially expressed molecules common to both PTS vs HEALTH (Set A) and AMI vs HEALTH (Set B) were isolated, followed by systematic exclusion of those overlapping with NPTS vs HEALTH (Set C) to eliminate early atherosclerosis signatures. This approach yielded 43 proteins, 334 mRNAs, 27 miRNAs, 253 lncRNAs, and 8 circRNAs (Fig. 5 A-E; Supplementary Table 4–8). Hierarchical clustering confirmed their discriminative capacity for PTS versus NPTS stratification (Fig. 5 F-J). A ceRNA network was constructed from the prioritized transcripts (334 mRNAs, 27 miRNAs, 253 lncRNAs, and 8 circRNAs), with topological analysis identifying critical hub nodes (e.g., hsa-miR-185-3p, hsa-miR-23b-3p; Fig. 6 A, Supplementary Table 9). Given the role of complement and coagulation cascades in thrombotic progression (Fig. 3 ), pathway-associated ceRNA subnetwork were extracted and visualized as a Sankey diagram (Fig. 6 B). This analysis revealed hsa-miR-185-3p, hsa-miR-125b-5p, coagulation factor V (F5), and urokinase plasminogen activator (PLAU) as key regulatory elements. Among the 43 candidate proteins, five exhibited upregulation, including FVIII and complement factor H (CFH), known regulators of complement and coagulation cascades (Supplementary Table 4). Eleven hub molecules were prioritized for validation: two proteins (FVIII, CFH), three mRNAs (PLAU, F5, serpin family G member 1 [SERPING1]), three miRNAs (hsa-miR-185-3p, hsa-miR-125b-5p, hsa-miR-23b-3p), two lncRNAs (GATA2-AS1, PCBP1-AS1), and one circRNA (hsa_circ_0008240). Biomarker validation The diagnostic potential of eleven candidate biomarkers for PTS was validated in an independent cohort. Quantitative analyses confirmed significant elevation of PLAU, GATA2-AS1, and FVIII in PTS versus HEALTH ( p < 0.05), contrasting with downregulation of hsa-miR-125b-5p and CFH ( p < 0.05) (Fig. 7 A-D). Crucially, PLAU and GATA2-AS1 demonstrated stage-specific upregulation in PTS relative to NPTS patients ( p < 0.05). Sensitivity analyses further confirmed the robustness across comparisons (Supplementary Fig. 8). ROC evaluation revealed four biomarkers with superior discriminative capacity for PTS versus NPTS: FVIII (AUC 0.88, 95% CI 0.73-1.00; p = 0.004), PLAU (AUC 0.95, 95% CI 0.86-1.00; p < 0.001), hsa-miR-125b-5p (AUC 0.78, 95% CI 0.57–0.99; p = 0.034), and GATA2-AS1 (AUC 0.89, 95% CI 0.74-1.00; p = 0.003) (Fig. 7 E-H). Discussion Despite advances in cardiovascular medicine, diagnosing the PTS in stable CAD patients remains challenging. Defining the molecular dynamics underlying coronary thrombotic progression—particularly PTS—is essential to identify biomarkers. In the present study, we provided a comprehensive landscape of transcriptomic and proteomic alterations across different stage of thrombotic progression and identified a novel ceRNA axis (GATA2-AS1/miR-125b-5p/PLAU) and FVIII protein serving as diagnostic biomarkers in PTS with CAD. The combination of platelet aggregation testing and nailfold capillaroscopy for diagnosing PTS is grounded in Virchow's Triad 27 , which emphasizes hypercoagulability, endothelial injury, and stasis as fundamental mechanisms of thrombogenesis. MPAR is a pivotal indicator defining high platelet reactivity (HPR) and directly reflects hypercoagulability. High platelet reactivity (MPAR > 64.5%) has been strongly linked to adverse ischemic outcomes, including stent thrombosis and recurrent myocardial infarction 28 . Coronary microcirculation is an independent predictor of future cardiovascular events. Given the invasiveness and cost of coronary microcirculation assessments, peripheral microcirculation provides a feasible surrogate 29 . Nailfold capillaroscopy is a non-invasive and established technique for assessing peripheral microcirculation 30 , 31 . Erythrocyte aggregation, white microthrombi, and slowed flow via nailfold capillaroscopy reflect microcirculatory stasis. Furthermore, nailfold microcirculation correlates with serum biomarkers of endothelial injury, suggesting it may also reflect the endothelial dysfunction component of the triad 32 . Therefore, the combinative measurement of MPAR and NM serve as a considerable indicator of PTS. Our multi-omics profiling revealed a dynamic continuum of molecular reprogramming across CAD stages, characterized by progressively amplified complement and coagulation cascades during thrombotic advancement. This trajectory was quantified through DEPs enrichment and plasma biomarkers. This finding aligns with and extends prior mechanistic insights, where thrombin has been shown to directly cleave C5, bypassing the classical complement activation cascade, and mannose-binding lectin serine protease 1(MASP-1) can activate coagulation factors and modulate fibrinolytic balance 33 , 34 . Moreover, downstream complement effectors such as C5a and the membrane attack complex (MAC) have been implicated in promoting a prothrombotic microenvironment through tissue factor induction and platelet hyperactivation 35 – 37 . These molecular interactions form a feedforward loop amplifying complement and coagulation signaling. The importance of complement coagulopathy in thrombotic progression underscores its potential as a biomarker and therapeutic target. In the present study, FVIII, PLAU, hsa-miR-125b-5p, and GATA2-AS1 were identified as PTS-specific biomarkers. Critically, these molecules are mechanistically embedded within the pathophysiological processes driving PTS. FVIII levels were significantly elevated in both PTS and AMI patients compared with healthy controls, aligning with prior reports of increased FVIII level in AMI patients 38 . Multiple case–control studies demonstrated that elevated plasma FVIII was associated with increased venous thromboembolism (VTE) risk and was recognized as an independent risk factor for VTE 39 , 40 . Functionally, FVIII acts as a FIXa cofactor within the tenase complex, amplifying FX activation and accelerating thrombin and fibrin generation. Binding to von Willebrand factor (VWF) facilitates FVIII to localize and concentrate at injury sites via VWF-mediated platelet recruitment, thereby intensifying thrombus formation 41 , 42 . PLAU, encoding urokinase-type plasminogen activator (uPA), plays a central role in fibrinolysis by converting plasminogen to plasmin. In this study, we found markedly upregulated PLAU expression in PTS patients. Previous work by Nordeng et al 43 reported elevated PLAU levels in coronary thrombi of AMI patients, correlating with monocyte/macrophage and neutrophil markers and implicating it in acute thrombus remodeling and inflammation. Transcriptomic analyses have also identified PLAU as a hub gene upregulated in atherosclerotic plaque rupture 44 . Our findings extend these observations by showing its dysregulation at the PTS stage, suggesting a role in early thrombotic remodeling prior to clinical plaque rupture. hsa-miR-125b-5p was identified another PTS-associated biomarker, showing significant downregulation in both PTS and AMI patients relative to controls. This mirrors earlier reports of reduced circulating miR-125b in AMI 45 . Experimental data showed that miR-125b-5p expression was suppressed in both murine AMI models and hypoxic endothelial cells 46 . Mechanistically, miR-125b-5p exerts endothelial protection by suppressing inflammation and apoptosis through inhibition of the nuclear factor of activated T cells 1 (NFAT2)/coagulation factor II thrombin receptor like 2 (F2RL2) pathway 46 , while also attenuating tumor necrosis factor superfamily, member 4 (TNFSF4)/Toll-like receptor 4 (TLR4)/NF-κB signaling in oxidized low-density lipoprotein–injured endothelial cells 47 . Our data suggested the involvement of miR-125b-5p before acute ischemic events by demonstrating its dysregulation already at the PTS. GATA2-AS1 expression was significantly elevated in PTS patients, in line with previous study showing higher GATA2-AS1 levels in CAD patients than healthy individuals 48 . Genetic evidence showed single-nucleotide variants within the GATA2-AS1 locus were associated with premature CAD susceptibility 49 . Mechanistically, GATA2-AS1 enrichment in endothelial cells enhanced hypoxia-inducible factor 1α (HIF1-α)-mediated hypoxic signaling 48 , suggesting its potential role in vascular stress responses during thrombotic progression. Although experimental data support that both GATA2-AS1 and PLAU are direct targets of miR-125b-5p, it remains to be elucidated whether GATA2-AS1 competitively binds miR-125b-5p to regulate PLAU expression and what functional role this ceRNA mechanism plays in the pathogenesis of PTS. In this study, we found multi-omic coordination and decoupling in PTS. Procrustes analysis revealed significant spatial concordance among RNA subtypes, indicating extensive transcriptomic cross-talk. Strongest alignment occurred between lncRNAs and mRNAs (M² = 0.225, p = 0.001), suggesting co-transcriptional regulation or shared epigenetic programming. Significant coordination across other RNA pairs (circRNA-mRNA, miRNA-mRNA, lncRNA-miRNA, circRNA-miRNA) demonstrated a tightly integrated regulatory architecture. This interdependence manifests functionally in ceRNA networks, where lncRNAs/circRNAs competitively bind to miRNAs via miRNA response elements (MREs) to derepress target mRNAs 6 – 8 . For example, lncRNA metallothionein 1 pseudogene 3 (MT1P3) enhanced platelet activation via miR-126 sponging to upregulate P2Y12 9 . Conversely, Procrustes analysis revealed a clear transcript–protein discordance, a phenomenon increasingly reported across tissues and disease contexts and attributable to both biological regulation and technical factors 50 , 51 . Post-transcriptional mechanisms such as alternative splicing, miRNA/lncRNA regulation, and translation efficiency can alter the relationship between transcript and protein abundance. In addition, protein-specific factors including translation rate, post-translational modification, and differential protein turnover further modulate steady-state protein levels. These processes collectively contribute to mRNA–protein discordance, as demonstrated by recent integrative temporal and pan-cancer proteogenomic studies 50 , 52 . Several limitations merit consideration. First, the observational small-sample study limits causal inference and biomarker generalizability, necessitating future large-scale multi-center validation. Second, GATA2-AS1/miR-125b-5p/PLAU ceRNA network was identified based on computational prediction, and its molecular interactions lack experimental validation. Furthermore, the role of this axis in the pathophysiology of PTS remains to be fully elucidated. Conclusion This study identified and validated a functionally linked ceRNA network (GATA2-AS1/miR-125b-5p/PLAU) in conjunction with FVIII as a highly accurate biomarker panel for PTS. Data availability Transcriptomics data are available in the Gene Expression Omnibus (GEO) under accession GSE303517 for lncRNA and accession GSE302816 for miRNA. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( https://proteomecentral.proteomexchange.org ) via the iProX partner repository 53 , 54 with the dataset identifier PXD066378. Declarations Competing interest The authors declare no competing interest. Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Xiyuan Hospital of China Academy of Chinese Medical Sciences (2022XLA103-1). Written informed consents were obtained from all participants prior to the study's initiation. Supplementary Information Supplementary Material.docx. Supplementary Table 1. Scoring criteria for nailfold microcirculation. Supplementary Table 2. Primer sequences for candidate genes used in qPCR validation. Supplementary Table 3. The number of differentially expressed molecules. Supplementary Table 4. 43 candidate proteins. Supplementary Table 5. 334 candidate mRNAs. Supplementary Table 6. 27 candidate miRNAs. Supplementary Table 7. 253 candidate lncRNAs. Supplementary Table 8. 8 candidate circRNAs. Supplementary Table 9. The topology analysis of ceRNA network. Supplementary Table 10. Abbreviations. Funding This work was supported by grants from the National Natural Science Foundation of China (82174214 and 82074418), the Youth Fund Project of Hangzhou Red Cross Hospital (HHQN2024002), the Key Project of the Science and Technology Innovation Program of the China Academy of Chinese Medical Sciences (CI2021A00911), and the Development and Translation of Institution-Specific Formulations and New Traditional Chinese Medicines with Intellectual Property at Xiyuan Hospital, China Academy of Chinese Medical Sciences (XYZX0304-03). Author Contribution Jinwen Luo: Data curation, Writing - Original Draft, Formal analysis. Jie Gao: Methodology, Visualization. Lei Zhang: Resources, Investigation. Xuanqing Fan: Software, Formal analysis. Kangkang Wei: Software, Validation. Min Liu: Formal analysis. Min Li: Investigation. Lintong Yu: Investigation. Dazhuo Shi: Conceptualization, Review, Supervision, Funding acquisition. Xiaojuan Ma: Conceptualization, Review, Project administration. All authors approved the final manuscript. Data Availability Transcriptomics data are available in the Gene Expression Omnibus (GEO) under accession GSE303517 for lncRNA and accession GSE302816 for miRNA. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD066378. References Mensah, G. A., Fuster, V., Murray, C. J. L. & Roth, G. A. 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Procrustes Analysis for High-Dimensional Data. Psychometrika 87 , 1422–1438 (2022). Kotlov, N. et al. Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data. Commun. Biol. 7 , 392 (2024). Li, J. H., Liu, S., Zhou, H., Qu, L. H. & Yang, J. H. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 42 , D92–97 (2014). Papadopoulos, G. L., Reczko, M., Simossis, V. A., Sethupathy, P. & Hatzigeorgiou, A. G. The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res. 37 , D155–158 (2009). Agarwal, V., Bell, G. W. & Nam, J. W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. Elife 4 (2015). Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U. & Segal, E. The role of site accessibility in microRNA target recognition. Nat. Genet. 39 , 1278–1284 (2007). Chen, L. et al. miRToolsGallery: a tag-based and rankable microRNA bioinformatics resources database portal. Database (Oxford). (2018). (2018). Ding, W. Y., Gupta, D. & Lip, G. Y. H. Atrial fibrillation and the prothrombotic state: revisiting Virchow's triad in 2020. Heart 106 , 1463–1468 (2020). Breet, N. J. et al. Comparison of platelet function tests in predicting clinical outcome in patients undergoing coronary stent implantation. Jama 303 , 754–762 (2010). SenthilKumar, G. et al. Is the peripheral microcirculation a window into the human coronary microvasculature? J. Mol. Cell. Cardiol. 193 , 67–77 (2024). Roberts-Thomson, P. J., Patterson, K. A. & Walker, J. G. Clinical utility of nailfold capillaroscopy. Intern. Med. J. 53 , 671–679 (2023). Smith, V. et al. Nailfold capillaroscopy. Best Pract. Res. Clin. Rheumatol. 37 , 101849 (2023). Gorska, A. et al. Impairment of microcirculation in juvenile idiopathic arthritis - studies by nailfold videocapillaroscopy and correlation with serum levels of sICAM and VEGF. Folia Histochem. Cytobiol . 46 , 443–447 (2008). Huber-Lang, M. et al. Generation of C5a in the absence of C3: a new complement activation pathway. Nat. Med. 12 , 682–687 (2006). Hess, K. et al. Effects of MASP-1 of the complement system on activation of coagulation factors and plasma clot formation. PLoS One . 7 , e35690 (2012). Ritis, K. et al. A novel C5a receptor-tissue factor cross-talk in neutrophils links innate immunity to coagulation pathways. J. Immunol. 177 , 4794–4802 (2006). Mannes, M. et al. Complement and platelets: prothrombotic cell activation requires membrane attack complex-induced release of danger signals. Blood Adv. 7 , 6367–6380 (2023). Wu, F. et al. Complement component C3a plays a critical role in endothelial activation and leukocyte recruitment into the brain. J. Neuroinflammation . 13 , 23 (2016). He, M. et al. Observation on tissue factor pathway and some other coagulation parameters during the onset of acute cerebrocardiac thrombotic diseases. Thromb. Res. 107 , 223–228 (2002). Rietveld, I. M. et al. High levels of coagulation factors and venous thrombosis risk: strongest association for factor VIII and von Willebrand factor. J. Thromb. Haemost . 17 , 99–109 (2019). Hansen, E. S. et al. Combined effect of high factor VIII levels and high mean platelet volume on the risk of future incident venous thromboembolism. J. Thromb. Haemost . 21 , 2844–2853 (2023). Childers, K. C., Peters, S. C. & Spiegel, P. C. Jr. Structural insights into blood coagulation factor VIII: Procoagulant complexes, membrane binding, and antibody inhibition. J. Thromb. Haemost . 20 , 1957–1970 (2022). Tanaka, K. A., Terada, R., Butt, A. L., Mazzeffi, M. A. & McNeil, J. S. Factor VIII: A Dynamic Modulator of Hemostasis and Thrombosis in Trauma. Anesth. Analg . 136 , 894–904 (2023). Nordeng, J. et al. Gene expression of fibrinolytic markers in coronary thrombi. Thromb. J. 20 , 23 (2022). Zhu, G. et al. Comprehensive analysis of atherosclerotic plaques reveals crucial genes and molecular mechanisms associated with plaque progression and rupture. Front. Cardiovasc. Med. 10 , 951242 (2023). Huang, S. et al. Circulating MicroRNAs and the occurrence of acute myocardial infarction in Chinese populations. Circ. Cardiovasc. Genet. 7 , 189–198 (2014). Wu, Z. et al. miR-125b-5p alleviates the damage of myocardial infarction by inhibiting the NFAT2 to reduce F2RL2 expression. Regen Med. 18 , 543–559 (2023). He, W., Zhao, L., Wang, P., Ren, M. & Han, Y. MiR-125b-5p ameliorates ox-LDL-induced vascular endothelial cell dysfunction by negatively regulating TNFSF4/TLR4/NF-κB signaling. BMC Biotechnol. 25 , 11 (2025). Man, H. S. J. et al. Long noncoding RNA GATA2-AS1 augments endothelial hypoxia inducible factor 1-α induction and regulates hypoxic signaling. J. Biol. Chem. 299 , 103029 (2023). Connelly, J. J. et al. GATA2 is associated with familial early-onset coronary artery disease. PLoS Genet. 2 , e139 (2006). Weerakoon, H. et al. Integrative temporal multi-omics reveals uncoupling of transcriptome and proteome during human T cell activation. NPJ Syst. Biol. Appl. 10 , 21 (2024). Ramelow, C. C. et al. Simultaneous profiling of native-state proteomes and transcriptomes of neural cell types using proximity labeling. bioRxiv . Preprint (2025). Savage, S. R. et al. Pan-cancer proteogenomics expands the landscape of therapeutic targets. Cell 187 , 4389–4407 (2024). Ma, J. et al. iProX: an integrated proteome resource. Nucleic Acids Res. 47 , D1211–d1217 (2019). Chen, T. et al. iProX in 2021: connecting proteomics data sharing with big data. Nucleic Acids Res. 50 , D1522–d1527 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable4.xlsx SupplementaryTable3.xlsx SupplementaryTable5..xlsx SupplementaryTable6.xlsx SupplementaryTable8.xlsx SupplementaryTable10.xlsx SupplementaryTable9.xlsx SupplementaryTable7..xlsx SupplementaryMaterial.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7671890\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":580194441,\"identity\":\"f1569fe4-71ae-4dbe-b611-b2fd0872d361\",\"order_by\":0,\"name\":\"Jinwen Luo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xiyuan Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jinwen\",\"middleName\":\"\",\"lastName\":\"Luo\",\"suffix\":\"\"},{\"id\":580194442,\"identity\":\"1662eee9-aaa5-4bac-9dcb-14f033ebeb61\",\"order_by\":1,\"name\":\"Jie Gao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xiyuan Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jie\",\"middleName\":\"\",\"lastName\":\"Gao\",\"suffix\":\"\"},{\"id\":580194443,\"identity\":\"7f3c8f2b-5d43-4f94-9ded-a49a191821b3\",\"order_by\":2,\"name\":\"Lei Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xiyuan Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lei\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":580194444,\"identity\":\"fba8e967-7e2d-45ad-8c33-187a397273de\",\"order_by\":3,\"name\":\"Xuanqing Fan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hanghzou International Innovation Institute, Beihang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xuanqing\",\"middleName\":\"\",\"lastName\":\"Fan\",\"suffix\":\"\"},{\"id\":580194445,\"identity\":\"af28b313-7a5b-48b5-b02d-3468f618be4c\",\"order_by\":4,\"name\":\"Kangkang Wei\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Affiliated Hospital of Shandong University of Traditional Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kangkang\",\"middleName\":\"\",\"lastName\":\"Wei\",\"suffix\":\"\"},{\"id\":580194446,\"identity\":\"eb6e0596-b8c3-4b1e-99c4-c680eeb65e5e\",\"order_by\":5,\"name\":\"Min Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xiyuan Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Min\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":580194447,\"identity\":\"46908a7b-07a1-47d9-ba0e-4c5c8d5c2165\",\"order_by\":6,\"name\":\"Min Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xiyuan Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Min\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":580194448,\"identity\":\"b08147fb-7fda-42ea-a4fa-626886d74a30\",\"order_by\":7,\"name\":\"Lintong Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xiyuan Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lintong\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"},{\"id\":580194449,\"identity\":\"8d76c05b-7aef-4297-94aa-6b30fd81b9f6\",\"order_by\":8,\"name\":\"Dazhuo Shi\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYDACCcYGCRDNz8x8+AEJWhIYGCTb2dIMiNQCQkAtBud5FCSI0iE/u7nx5s8fdtHGh3kYDBhqbKIJajG4c7DZQiIhOXfbYd4DDxiOpeU2ENQikdgmYZBwAKiFL8GAseEwYS3yM4BaEoBaNjfzGEgQpYXhBlDLAaCWDczEajG4kdhs2ZCWnDvjMDCQE4jxi/yM9Ic3f9jY5fb3Hz784EONDREOQwEJpCkfBaNgFIyCUYALAABHZUCA/qUSnQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Xiyuan Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Dazhuo\",\"middleName\":\"\",\"lastName\":\"Shi\",\"suffix\":\"\"},{\"id\":580194450,\"identity\":\"fbdac07c-850c-4b91-9ec1-0b8e1be761c3\",\"order_by\":9,\"name\":\"Xiaojuan Ma\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xiyuan Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaojuan\",\"middleName\":\"\",\"lastName\":\"Ma\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-09-22 09:54:17\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7671890/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7671890/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":101363969,\"identity\":\"45a8a9f4-eeba-4d98-a5e4-145032572266\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":418345,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSchematic of multi-phase biomarker study integrating transcriptomic and proteomic analysis. Discovery Phase: Transcriptomic (RNAseq, PBMC total RNA) and proteomic (DIA, plasma total protein) analysis in four cohorts (HEALTH, NPTS, PTS, AMI; n=7/group). Integrative analysis identified eleven candidate biomarkers (nine RNA, two protein). Validation Phase: In an independent cohort (HEALTH, NPTS, PTS, AMI; n=10/group), nine RNA biomarkers (qPCR) and two protein biomarkers (ELISA) were validated, leading to four PTS biomarkers identified by ROC analysis. (Scientific illustration created with Figdraw, ID: IPAAW6baab).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/de0a1252b7adfc103b463493.png\"},{\"id\":101363963,\"identity\":\"28a86c2e-e087-4cbf-a25d-b8197641bbcb\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:07\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1147742,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePrincipal component analysis and differential expression analysis. (A-E) PCA plots of protein (A), mRNA (B), miRNA (C), lncRNA (D), and circRNA (E) expression profiles across four groups: HEALTH (blue), NPTS (green), PTS (orange), AMI (red). (F-J) Volcano plots of differentially expressed molecules between PTS and HEALTH groups: proteins (F), mRNAs (G), miRNAs (H), lncRNAs (I), circRNAs (J). Significantly upregulated (red) and downregulated (blue) molecules; non-significant changes (gray).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/da441da6163ed9441b91e27e.png\"},{\"id\":101398167,\"identity\":\"1534397f-4af3-401d-b1b5-c0add2fc8b3c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 09:39:55\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1328721,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKEGG analysis of differentially expressed molecules and the level of complement-coagulation markers. (A) KEGG pathway enrichment of DEPs. (B) KEGG pathway enrichment of DEmRNAs. (C) Plasma concentrations of C5a, C3a, TAT, and TpP in HEALTH, NPTS, PTS, and AMI groups. Data are presented as mean ± SD. (*\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.05; **\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.01; ***\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.001)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/603117d85a07e9d947b119ee.png\"},{\"id\":101363975,\"identity\":\"4d107972-c70d-43f3-9a6d-2d090e444509\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":790292,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eProcrustes analysis of transcriptome and proteome. \\u003c/strong\\u003e(A) protein-mRNA, (B) protein-miRNA, (C) protein-lncRNA, (D) protein-circRNA, (E) mRNA-miRNA, (F) mRNA-lncRNA, (G) mRNA-circRNA. (H) lncRNA-circRNA, (I) miRNA-circRNA, and (J) miRNA-lncRNA. M²: Procrustes sum of squared deviations (lower values indicate higher configuration concordance between molecular profiles). \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05 was considered statistically significant.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/c695296e37d5657873a0a016.png\"},{\"id\":101398208,\"identity\":\"268982fc-e5e1-48b3-98a6-ef480e0c286d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 09:40:14\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1243562,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIdentification of candidate PTS biomarkers. (A-E) Venn diagrams showing differentially expressed molecules across comparisons. (A) DEproteins, (B) DEmRNAs, (C) DEmiRNAs, (D) DElncRNAs, and (E) DEcircRNAs. (F-J) Heatmaps of differentially expressed molecules. (F) 43 DEproteins, (G) 334 DEmRNAs, (H) 27 DEmiRNAs, (I) 253 DElncRNAs, and (J) 8 DEcircRNAs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/cf51c7f598b4af337ddaa6c3.png\"},{\"id\":101363966,\"identity\":\"5558375d-b1b2-4b02-a157-7abc2e46b972\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1234711,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eceRNA regulatory network. (A) circRNA/lcnRNA-miRNA-mRNA network of candidate molecules visualized by Cytoscape. (B) Complement and coagulation cascades–associated ceRNA regulatory network visualized by a Sankey diagram.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/6a4e02412574343d92576897.png\"},{\"id\":101398378,\"identity\":\"865d85a7-4db1-4144-a94f-bf4b30e8a687\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 09:41:13\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1211890,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eValidation of candidate PTS biomarkers. (A-D) Expression levels of candidate (A) mRNA, (B) miRNA, (C) lncRNA and circRNA, and (D) protein biomarkers across study groups (HEALTH, NPTS, PTS, AMI). Data are presented as mean ± SEM. (*\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05; **\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.01; ***\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt; 0.001). (E-H) ROC curves showing the diagnostic performance of candidate (E) mRNA, (F) miRNA, (G) lncRNA and circRNA, and (H) protein biomarkers for distinguishing PTS from NPTS patients. AUC values are indicated.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/e35b22c8bb54029bf4e376d8.png\"},{\"id\":102294876,\"identity\":\"457f2f94-3663-49cf-94d8-730dd4fc7d22\",\"added_by\":\"auto\",\"created_at\":\"2026-02-10 10:02:50\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":8208395,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/afe822db-b033-41c2-bb9f-089291fa5662.pdf\"},{\"id\":101363962,\"identity\":\"d633af64-0b9a-48fe-b2d3-550aad245ede\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:07\",\"extension\":\"xlsx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":11253,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable1.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/c7575d51e326ca6e18b13290.xlsx\"},{\"id\":101363964,\"identity\":\"036366b9-f1f9-469c-8160-8ddeb5b5b51b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"xlsx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":10832,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable2.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/9aea104a003345cbe0bcff60.xlsx\"},{\"id\":101398228,\"identity\":\"2997a321-ec8b-4278-b4f9-7defa7a6bfb8\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 09:40:22\",\"extension\":\"xlsx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":19642,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable4.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/a8633ec0c234161787fc26eb.xlsx\"},{\"id\":101751297,\"identity\":\"be3e9bb2-5ef3-46d7-b2ad-3c9c8faa27d8\",\"added_by\":\"auto\",\"created_at\":\"2026-02-03 10:19:04\",\"extension\":\"xlsx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":9372,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable3.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/b6d5e03b01b59669b540366e.xlsx\"},{\"id\":101363977,\"identity\":\"04ed338c-e16d-4c03-a982-2213fea5886b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"xlsx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":70489,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable5..xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/bdb809aaf0e6e4ca03e1b491.xlsx\"},{\"id\":101363973,\"identity\":\"56dcd5b8-c0b0-434c-bc57-f72fbf4379f7\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"xlsx\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":14824,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable6.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/f8b094a0c739f5ee7d708c06.xlsx\"},{\"id\":101363974,\"identity\":\"e108a248-1e7b-4a6b-92de-305bcae4f1b3\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"xlsx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":11544,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable8.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/70211a4454c1527d1810c1ea.xlsx\"},{\"id\":101363980,\"identity\":\"60e0ab6a-e745-4954-8722-ace184e39a0e\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"xlsx\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":10679,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable10.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/4e431367924aef2f18384633.xlsx\"},{\"id\":101363978,\"identity\":\"9db75bdd-7ad2-4492-85ad-22c3f5e42a81\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"xlsx\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":24764,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable9.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/c6342675d6888a8f97387852.xlsx\"},{\"id\":101363979,\"identity\":\"1245d20c-7bc1-42c3-be0a-db7250bf162b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 00:44:08\",\"extension\":\"xlsx\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":61835,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable7..xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/03b463ea830bdf02545c207b.xlsx\"},{\"id\":101398175,\"identity\":\"9b5f0d94-4ccf-40e2-b8df-afa5f1080763\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 09:39:57\",\"extension\":\"pdf\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2604269,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryMaterial.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7671890/v1/1dcf76f70e5a837567c41b4a.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Identification of GATA2-AS1- hsa-miR-125b-5p-PLAU ceRNA regulatory network as novel biomarkers for prethrombotic state in patients with coronary artery disease\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eCoronary artery disease (CAD) remains a leading cause of global mortality, with acute coronary thrombosis representing the critical terminal event precipitating acute myocardial infarction (AMI)\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. A recent study showed that 50.5% of first-time AMI patients had no symptoms before the event\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e, highlighting the unpredictability and prevention challenges of AMI. The prethrombotic state (PTS), characterized by endothelial dysfunction, platelet hyperactivity and coagulation-anticoagulation imbalance, serves as critical precursor to coronary thrombosis\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. Current diagnostic paradigms relying on conventional coagulation parameters lack specificity for detecting PTS in stable CAD patients, failing to identify high-risk individuals before thrombotic crises occur\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eRecent advances in multi-omics technologies have revealed that diverse classes of non-coding RNAs (ncRNAs)\\u0026mdash;including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs)\\u0026mdash;serve as key regulators in thrombogenesis and related cardiovascular pathophysiological processes through epigenetic, transcriptional, and post-transcriptional mechanisms\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e. Notably, the competing endogenous RNA (ceRNA) hypothesis has emerged as an important regulatory mechanism, where lncRNAs, circRNAs, and other transcripts can act as molecular sponges for miRNAs, thereby modulating the expression of miRNA target genes such as protein-coding mRNAs\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. Dysregulation of ceRNA networks has been implicated in thrombosis\\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u003c/sup\\u003e, though their role in PTS remains underexplored. These multi-omics innovations have further enabled the discovery of novel CAD biomarkers. For instance, transcriptomic profiling identified monocyte-derived lncRNA OTTHUMT00000387022 as a circulating diagnostic marker for CAD\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e, while hsa_circ_0001879 and hsa_circ_0004104 have been identified as CAD-associated signatures\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. Similarly, proteomic studies revealed hemopexin, leucine-rich α-2-glycoprotein, vitronectin, and fibronectin as key proteins associated with acute coronary syndromes\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eNevertheless, existing studies have predominantly focused on isolated disease states, typically comparing AMI or stable CAD to healthy controls\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e. A critical knowledge gap persists: the dynamic evolution of multi-omics across the disease continuum from health (HEALTH)\\u0026rarr; stable CAD without PTS (NPTS)\\u0026rarr; stable CAD with PTS \\u0026rarr; AMI remains uncharacterized, particularly during the clinically actionable yet diagnostically challenging PTS phase. Moreover, the potential of integrated RNA-protein signatures and ceRNA regulatory networks as PTS-specific biomarkers remains unexplored.\\u003c/p\\u003e \\u003cp\\u003eTo address these gaps, we employed a systems biology approach integrating transcriptomic (mRNA, lncRNA, miRNA, circRNA) and proteomic profiling across four clinical stages: HEALTH, NPTS, PTS, and AMI. This study aimed to discover and validate novel multi-omics biomarkers for the specific detection of PTS in stable CAD.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatients and study design\\u003c/h2\\u003e \\u003cp\\u003eThis observational study was conducted at Xiyuan Hospital, China Academy of Chinese Medical Sciences, between July 2022 and October 2023. Participants were allocated into four groups based on clinical diagnosis and functional assessments: (1) healthy controls (HEALTH, n\\u0026thinsp;=\\u0026thinsp;17): no psychiatric or physical disorders, nailfold microcirculation (NM) score\\u0026thinsp;\\u0026lt;\\u0026thinsp;4, and maximum platelet aggregation rate (MPAR)\\u0026thinsp;\\u0026lt;\\u0026thinsp;65%; (2) Stable CAD without PTS (NPTS, n\\u0026thinsp;=\\u0026thinsp;17): meeting ESC guidelines\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e with MPAR\\u0026thinsp;\\u0026lt;\\u0026thinsp;65% and NM score\\u0026thinsp;\\u0026lt;\\u0026thinsp;4; (3) Stable CAD with PTS (PTS, n\\u0026thinsp;=\\u0026thinsp;17): meeting ESC guidelines\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e with MPAR\\u0026thinsp;\\u0026ge;\\u0026thinsp;65% and NM score\\u0026thinsp;\\u0026ge;\\u0026thinsp;4 (aligns with the criteria in previous study \\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e); (4) AMI (n\\u0026thinsp;=\\u0026thinsp;17): diagnosed according to the Fourth Universal Definition of MI\\u003csup\\u003e17\\u003c/sup\\u003e and representing acute thrombosis. Exclusion criteria: severe comorbidities (left ventricular ejection fraction\\u0026thinsp;\\u0026lt;\\u0026thinsp;35%, arrhythmia requiring intervention, liver/kidney dysfunction), rheumatoid arthritis, tuberculosis, malignancy, recent (\\u0026lt;\\u0026thinsp;6 months) stroke, active infections, and prior anticoagulant use. NM assessment is based on a published method\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e. Detailed methodologies are described in the \\u003cb\\u003eSupplemental Materials\\u003c/b\\u003e.\\u003c/p\\u003e \\u003cp\\u003eEligible participants were stratified by diagnosis and randomly assigned to discovery or verification cohorts. The discovery cohort (n\\u0026thinsp;=\\u0026thinsp;28, 7/group) underwent whole-transcriptome sequencing of peripheral blood mononuclear cells (PBMCs) and plasma proteomic profiling via data-independent acquisition (DIA). Four samples failed to extract sufficient PBMCs. The verification cohort (n\\u0026thinsp;=\\u0026thinsp;40, 10/group) was used to validate candidate biomarkers by quantitative polymerase chain reaction (qPCR) and enzyme-linked immunosorbent assay (ELISA) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Baseline characteristics of study participants are presented in Table\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e The study was approved by the Ethics Committee of Xiyuan Hospital of China Academy of Chinese Medical Sciences (2022XLA103-1). All methods in this study were performed in accordance with the Declaration of Helsinki. Written informed consents were obtained from all participants prior to the study's initiation.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eSample collection and processing\\u003c/h3\\u003e\\n\\u003cp\\u003ePeripheral venous blood (10 mL) was collected from fasting subjects (\\u0026ge;\\u0026thinsp;8 hours) using ethylene diamine tetraacetic acid (vacuum tubes. For CAD patients, blood was drawn within 1 hour before coronary angiography to preclude procedural interference. Plasma was isolated via centrifugation at 2,000 \\u0026times; g for 15 min at 4\\u0026deg;C and stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C. PBMCs were purified from the remaining blood by density gradient centrifugation using Ficoll-Paque\\u0026trade; PLUS (GE Healthcare, Sweden), according to the manufacturer\\u0026rsquo;s protocol. PBMC pellets were immediately homogenized in TRIzol\\u0026trade; Reagent (Invitrogen, USA) at a 1:3 volumetric ratio (pellet:TRIzol) to stabilize RNA. All samples were aliquoted into cryovials and flash-frozen at \\u0026minus;\\u0026thinsp;80\\u0026deg;C within 2 hours of collection.\\u003c/p\\u003e\\n\\u003ch3\\u003eRNA sequencing analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eWhole-transcriptome RNA sequencing of PBMC-derived RNA was performed by Novogene Co., Ltd (Beijing, China). Total RNA was extracted using TRNzol Universal Reagent (Tiangen, China) following the manufacturer\\u0026rsquo;s protocol. RNA quality control encompassed purity assessment via agarose gel electrophoresis, concentration measurement using a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, USA), and integrity evaluation with an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Qualified RNA samples underwent library preparation using the NEBNext Ultra\\u0026trade; Directional RNA Library Prep Kit (NEB #E7420, USA) for lncRNA and the NEBNext\\u0026reg; Multiplex Small RNA Library Prep Set (NEB #E7300L, USA) for small RNA. Library quality was verified through insert size distribution analysis (Agilent 2100 Bioanalyzer) and effective concentration quantification (\\u0026gt;\\u0026thinsp;2 nM) via qPCR (KAPA Biosystems). Libraries were pooled and sequenced on an Illumina NovaSeq 6000 platform (Illumina, USA) with PE150 for lncRNA and SE50 for small RNA. Raw FASTQ files were preprocessed using in-house Perl scripts to remove adapters, filter poly-N reads, and discard low-quality reads. Clean data quality was assessed via Q20, Q30, and GC content metrics. CircRNAs were identified using find_circ and CIRI2\\u003csup\\u003e6,19\\u003c/sup\\u003e. Expression quantification was performed with Transcripts Per Million (TPM) for miRNA and circRNA, and Fragments Per Kilobase per Million mapped reads (FPKM) for lncRNA and mRNA.\\u003c/p\\u003e\\n\\u003ch3\\u003eDIA proteomic analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eProteomic analysis of plasma was performed by Novogene Co., Ltd. (Beijing, China). High-abundance proteins were depleted from plasma samples using a ProteoMiner\\u0026trade; Low Abundance Protein Enrichment Kit (Bio-Rad, #1633007). Proteins were lysed in 8 M urea/100 mM TEAB (pH 8.5), reduced with 10 mM DTT (Sigma, #D9163) at 56\\u0026deg;C for 1 h, and alkylated with 20 mM iodoacetamide (IAM, Sigma, #I6125) in darkness for 1 h. Protein concentration was quantified via Bradford assay (Beyotime) with quality assessed by SDS-PAGE. After tryptic digestion (Promega, #V5280), peptides were desalted using C18 cartridges and lyophilized. Pooled peptides underwent high-pH fractionation (BEH C18 column, 4.6\\u0026times;250 mm) at 45\\u0026deg;C. Resulting fractions were lyophilized and reconstituted in 0.1% formic acid. For DDA library construction and DIA analysis, 200 ng peptides were separated via nanoElute UHPLC (25 cm\\u0026times;75 \\u0026micro;m column; Table\\u0026nbsp;4 gradient) and analyzed on a timsTOF Pro 2 (PASEF mode; CaptiveSpray 1.5 kV; *m/z* 100\\u0026ndash;1700 scan). DDA library employed MS/MS topN fragmentation with a 1.17 sec cycle time and 2,500 intensity threshold. DIA acquisition used 25 Da mass isolation windows and two mobility windows. Proteins were identified and quantified using Spectronaut Pulsar (Biognosys, v16.0) against the UniProt database. Search parameters included a 10 ppm precursor mass tolerance, 0.02 Da fragment mass tolerance, fixed carbamidomethylation (cysteine), variable modifications including methionine oxidation and N-terminal acetylation, and a maximum of two missed cleavages, with false discovery rate (FDR) set at 1% for both peptide and protein levels.\\u003c/p\\u003e\\n\\u003ch3\\u003eqPCR\\u003c/h3\\u003e\\n\\u003cp\\u003eTotal RNA was extracted from tissues, cells, or blood using an UltraPure RNA Kit (Tiangen) with homogenization in RLT buffer, chloroform phase separation, and column purification. RNA concentration/purity was measured via NanoDrop ND-2000 (A260/A280\\u0026thinsp;\\u0026ge;\\u0026thinsp;1.8). cDNA was synthesized from 1 \\u0026micro;g RNA using HiScript III Reverse Transcriptase (Vazyme) with oligo (dT)/random hexamers, while miRNA cDNA utilized polyadenylation and miRNA-specific RT primers (Tiangen, KR201). qPCR reactions contained 10 \\u0026micro;L 2\\u0026times;AceQ SYBR Master Mix (Vazyme), 0.4 \\u0026micro;M primers, and 2 \\u0026micro;L cDNA template in 20 \\u0026micro;L volumes. Amplification proceeded on a real-time PCR system (95\\u0026deg;C/10 min; 45 cycles: 95\\u0026deg;C/15 s, 60\\u0026deg;C/60 s) with melt curve analysis (60\\u0026ndash;99\\u0026deg;C). Gene expression of mRNA, lncRNA and circRNA was normalized to GAPDH, miRNA expression was normalized to U6. All quantifications used the standard 2\\u0026thinsp;\\u0026minus;\\u0026thinsp;ΔΔCt method. The primers used in the qPCR of RNA were listed in Supplementary Table\\u0026nbsp;2. All measurements were performed in triplicate.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eELISA\\u003c/h2\\u003e \\u003cp\\u003ePlasma concentrations of coagulation factor VIII (FVIII), complement factor H (CFH), complement component 3a (C3a), complement component 5a (C5a), thrombin\\u0026ndash;antithrombin complex (TAT), and thrombus precursor protein (TpP) were quantified using commercial ELISA kits (Shanghai Enzyme-linked Biotechnology Co., China) according to the manufacturer\\u0026rsquo;s instructions. Plasma samples were diluted five-fold in phosphate-buffered saline and analyzed using a Rayto RT-6100 microplate reader.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStatistical and bioinformatics analyses\\u003c/h3\\u003e\\n\\u003cp\\u003eStatistical analyses were performed using SPSS 27.0 (IBM) with visualization in GraphPad Prism 10.0.2. Normally distributed data were presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD) and analyzed by one-way ANOVA after confirmation of normality (Shapiro-Wilk test). Homoscedasticity was verified using Levene's test; post hoc analyses employed the LSD test for homoscedastic data and Tamhane's T2 for heteroscedastic data. Non-normally distributed data underwent Kruskal-Wallis testing with Dunn's post hoc comparisons. Extreme outliers (\\u0026gt;\\u0026thinsp;3 \\u0026times; interquartile range) were excluded from all groups, with sensitivity analyses confirming result robustness. All tests were two-tailed; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 defined statistical significance. Diagnostic performance of biomarkers was assessed via receiver operating characteristic (ROC) curve analysis, reporting area under the curve (AUC).\\u003c/p\\u003e \\u003cp\\u003eTranscriptomic and proteomic data were analyzed via principal component analysis (PCA) using Factoextra ((v1.0.7) R packages. Differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), lncRNAs (DElncRNAs), and circRNAs (DEcircRNAs) were identified using edgeR (v3.22.5) R package with significance threshold of \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. The protein quantitation results were statistically analyzed by T-test. The proteins whose quantitation significantly different between experimental and control groups (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) were defined as differentially expressed proteins (DEPs). Functional enrichment analyses for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using clusterProfiler (v4.10.0), along with Gene Set Enrichment Analysis (GSEA) using the KEGG gene sets. Procrustes analysis, a geometric method aligning multidimensional configurations via rotation/scaling/translation, minimizes the Procrustes distance to quantify structural concordance between datasets \\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. Implemented with the vegan R package (v2.6.4), Procrustes analysis was applied to transcriptomic and proteomic profiles to identify shared and divergent biological patterns across omics layers. ceRNA interactions were predicted using the ENCORI\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e, TarBase 9.0\\u003csup\\u003e23\\u003c/sup\\u003e, TargetScan 7.2\\u003csup\\u003e24\\u003c/sup\\u003e, PITA\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e, and miRanda\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e databases. Networks were visualized in Cytoscape (v3.8.2). Sankey diagram was drawn using the ggalluvial (v0.12.5) package in R.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTranscriptomic and proteomic profiling\\u003c/h2\\u003e \\u003cp\\u003ePCA of transcriptomic and proteomic data demonstrated distinct clustering patterns across CAD stages (HEALTH, NPTS, PTS, AMI) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA-E). proteins, mRNAs, miRNAs, and lncRNAs demonstrated distinct intergroup clustering with partial overlap (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA-D), while circRNAs exhibited minimal discriminatory capacity without significant clustering differences (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo identify stage-specific molecular alterations, cross-comparisons of transcriptomic and proteomic profiles were performed across four groups. Proteomic differential expression analysis revealed progressively increasing numbers of DEPs with CAD advancement (Supplementary Table\\u0026nbsp;3): 61 DEPs in NPTS vs HEALTH, 122 in PTS vs HEALTH, and 152 in AMI vs HEALTH. This pattern indicates significant proteomic reprogramming during transition to the PTS phase, intensifying further upon infarction. Crucially, transcriptomic profiling of PTS vs HEALTH demonstrated extensive dysregulation, including 2,871 DEmRNAs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eG), 117 DEmiRNAs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eH), 1,925 DElncRNAs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eI), and 108 DEcircRNAs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eJ). Volcano plots for other comparison groups are provided in Supplementary Fig.\\u0026nbsp;1\\u0026ndash;4, with complete differential expression statistics in Supplementary Table\\u0026nbsp;3.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFunctional dynamics across disease stages\\u003c/h2\\u003e \\u003cp\\u003eFunctional analysis of multi-omics profiles revealed stage-specific pathway activation patterns during CAD progression. In NPTS, upregulated DEPs were predominantly enriched in complement and coagulation cascades (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA), while DEmRNAs featured fluid shear stress responses (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB) and DEncRNAs targeted vascular smooth muscle contraction and actin cytoskeleton regulation (Supplementary Fig.\\u0026nbsp;6A). During PTS, upregulated DEPs again demonstrated complement and coagulation cascades pathway enrichment (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA), consistent with the GSEA results (NES\\u0026thinsp;=\\u0026thinsp;1.83, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; Supplementary Fig.\\u0026nbsp;7B). Concurrent transcriptomic analyses revealed DEmRNAs associated with apoptosis and lipid metabolism (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB), and DEncRNAs implicated in cellular senescence (Supplementary Fig.\\u0026nbsp;6B). At AMI, DEPs showed sustained complement and coagulation cascade activation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA), corroborated by GSEA (NES\\u0026thinsp;=\\u0026thinsp;2.22, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; Supplementary Fig.\\u0026nbsp;7C). Both DEmRNAs and DEncRNAs converged on nuclear factor-kappaB (NF-κB) signaling and tumor necrosis factor (TNF) signaling pathways (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB, Supplementary Fig.\\u0026nbsp;6C). Plasma biomarker quantification confirmed progressive complement and coagulation system activation across stages (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). Collectively, these findings indicate that thrombotic progression is driven by a shift from quantitative to qualitative alterations in these molecular responses.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eProcrustes analysis of transcriptome and proteome\\u003c/h2\\u003e \\u003cp\\u003eTo investigate cross-omics coordination during thrombotic progression, we applied Procrustes analysis to RNA-RNA and RNA-protein expression matrices (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Significant alignment was observed between lncRNA and mRNA profiles (M\\u0026sup2; = 0.225, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001), indicating synchronized transcriptional regulation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eF). mRNA- miRNA (M\\u0026sup2; = 0.838, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.03), mRNA-circRNA (M\\u0026sup2; = 0.776, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.008), lncRNA- circRNA (M\\u0026sup2; = 0.703, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001), miRNA- lncRNA (M\\u0026sup2; = 0.801, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.023), miRNA-circRNA (M\\u0026sup2; = 0.838, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.044) also showed significant concordance (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE, G-J). In contrast, RNA\\u0026ndash;protein associations displayed relatively weak spatial alignment (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA-D). Although the protein-miRNA pair showed borderline significance (M\\u0026sup2; = 0.858, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.049), protein-mRNA (M\\u0026sup2; = 0.856, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.064), protein-lncRNA (M\\u0026sup2; = 0.89, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.118), and protein-circRNA (M\\u0026sup2; = 0.935, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;375) interactions were not statistically significant. Procrustes analysis revealed strong intra-transcriptomic coordination and relatively decoupled transcript\\u0026ndash;protein relationships, underscoring the complexity of regulatory control across molecular layers in thrombotic progression.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCandidate biomarker discovery\\u003c/h2\\u003e \\u003cp\\u003eA multi-omics filtering strategy was implemented to identify PTS-specific biomarkers. Differentially expressed molecules common to both PTS vs HEALTH (Set A) and AMI vs HEALTH (Set B) were isolated, followed by systematic exclusion of those overlapping with NPTS vs HEALTH (Set C) to eliminate early atherosclerosis signatures. This approach yielded 43 proteins, 334 mRNAs, 27 miRNAs, 253 lncRNAs, and 8 circRNAs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA-E; Supplementary Table\\u0026nbsp;4\\u0026ndash;8). Hierarchical clustering confirmed their discriminative capacity for PTS versus NPTS stratification (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eF-J).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eA ceRNA network was constructed from the prioritized transcripts (334 mRNAs, 27 miRNAs, 253 lncRNAs, and 8 circRNAs), with topological analysis identifying critical hub nodes (e.g., hsa-miR-185-3p, hsa-miR-23b-3p; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA, Supplementary Table\\u0026nbsp;9). Given the role of complement and coagulation cascades in thrombotic progression (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), pathway-associated ceRNA subnetwork were extracted and visualized as a Sankey diagram (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB). This analysis revealed hsa-miR-185-3p, hsa-miR-125b-5p, coagulation factor V (F5), and urokinase plasminogen activator (PLAU) as key regulatory elements. Among the 43 candidate proteins, five exhibited upregulation, including FVIII and complement factor H (CFH), known regulators of complement and coagulation cascades (Supplementary Table\\u0026nbsp;4). Eleven hub molecules were prioritized for validation: two proteins (FVIII, CFH), three mRNAs (PLAU, F5, serpin family G member 1 [SERPING1]), three miRNAs (hsa-miR-185-3p, hsa-miR-125b-5p, hsa-miR-23b-3p), two lncRNAs (GATA2-AS1, PCBP1-AS1), and one circRNA (hsa_circ_0008240).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBiomarker validation\\u003c/h2\\u003e \\u003cp\\u003eThe diagnostic potential of eleven candidate biomarkers for PTS was validated in an independent cohort. Quantitative analyses confirmed significant elevation of PLAU, GATA2-AS1, and FVIII in PTS versus HEALTH (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), contrasting with downregulation of hsa-miR-125b-5p and CFH (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA-D). Crucially, PLAU and GATA2-AS1 demonstrated stage-specific upregulation in PTS relative to NPTS patients (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Sensitivity analyses further confirmed the robustness across comparisons (Supplementary Fig.\\u0026nbsp;8). ROC evaluation revealed four biomarkers with superior discriminative capacity for PTS versus NPTS: FVIII (AUC 0.88, 95% CI 0.73-1.00; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004), PLAU (AUC 0.95, 95% CI 0.86-1.00; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), hsa-miR-125b-5p (AUC 0.78, 95% CI 0.57\\u0026ndash;0.99; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.034), and GATA2-AS1 (AUC 0.89, 95% CI 0.74-1.00; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.003) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eE-H).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eDespite advances in cardiovascular medicine, diagnosing the PTS in stable CAD patients remains challenging. Defining the molecular dynamics underlying coronary thrombotic progression\\u0026mdash;particularly PTS\\u0026mdash;is essential to identify biomarkers. In the present study, we provided a comprehensive landscape of transcriptomic and proteomic alterations across different stage of thrombotic progression and identified a novel ceRNA axis (GATA2-AS1/miR-125b-5p/PLAU) and FVIII protein serving as diagnostic biomarkers in PTS with CAD.\\u003c/p\\u003e \\u003cp\\u003eThe combination of platelet aggregation testing and nailfold capillaroscopy for diagnosing PTS is grounded in Virchow's Triad\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e, which emphasizes hypercoagulability, endothelial injury, and stasis as fundamental mechanisms of thrombogenesis. MPAR is a pivotal indicator defining high platelet reactivity (HPR) and directly reflects hypercoagulability. High platelet reactivity (MPAR\\u0026thinsp;\\u0026gt;\\u0026thinsp;64.5%) has been strongly linked to adverse ischemic outcomes, including stent thrombosis and recurrent myocardial infarction\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e. Coronary microcirculation is an independent predictor of future cardiovascular events. Given the invasiveness and cost of coronary microcirculation assessments, peripheral microcirculation provides a feasible surrogate\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e. Nailfold capillaroscopy is a non-invasive and established technique for assessing peripheral microcirculation\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e. Erythrocyte aggregation, white microthrombi, and slowed flow via nailfold capillaroscopy reflect microcirculatory stasis. Furthermore, nailfold microcirculation correlates with serum biomarkers of endothelial injury, suggesting it may also reflect the endothelial dysfunction component of the triad\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, the combinative measurement of MPAR and NM serve as a considerable indicator of PTS.\\u003c/p\\u003e \\u003cp\\u003eOur multi-omics profiling revealed a dynamic continuum of molecular reprogramming across CAD stages, characterized by progressively amplified complement and coagulation cascades during thrombotic advancement. This trajectory was quantified through DEPs enrichment and plasma biomarkers. This finding aligns with and extends prior mechanistic insights, where thrombin has been shown to directly cleave C5, bypassing the classical complement activation cascade, and mannose-binding lectin serine protease 1(MASP-1) can activate coagulation factors and modulate fibrinolytic balance\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e. Moreover, downstream complement effectors such as C5a and the membrane attack complex (MAC) have been implicated in promoting a prothrombotic microenvironment through tissue factor induction and platelet hyperactivation\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR36\\\" citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. These molecular interactions form a feedforward loop amplifying complement and coagulation signaling. The importance of complement coagulopathy in thrombotic progression underscores its potential as a biomarker and therapeutic target.\\u003c/p\\u003e \\u003cp\\u003eIn the present study, FVIII, PLAU, hsa-miR-125b-5p, and GATA2-AS1 were identified as PTS-specific biomarkers. Critically, these molecules are mechanistically embedded within the pathophysiological processes driving PTS. FVIII levels were significantly elevated in both PTS and AMI patients compared with healthy controls, aligning with prior reports of increased FVIII level in AMI patients\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e. Multiple case\\u0026ndash;control studies demonstrated that elevated plasma FVIII was associated with increased venous thromboembolism (VTE) risk and was recognized as an independent risk factor for VTE\\u003csup\\u003e\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e. Functionally, FVIII acts as a FIXa cofactor within the tenase complex, amplifying FX activation and accelerating thrombin and fibrin generation. Binding to von Willebrand factor (VWF) facilitates FVIII to localize and concentrate at injury sites via VWF-mediated platelet recruitment, thereby intensifying thrombus formation \\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003ePLAU, encoding urokinase-type plasminogen activator (uPA), plays a central role in fibrinolysis by converting plasminogen to plasmin. In this study, we found markedly upregulated PLAU expression in PTS patients. Previous work by Nordeng et al\\u003csup\\u003e43\\u003c/sup\\u003e reported elevated PLAU levels in coronary thrombi of AMI patients, correlating with monocyte/macrophage and neutrophil markers and implicating it in acute thrombus remodeling and inflammation. Transcriptomic analyses have also identified PLAU as a hub gene upregulated in atherosclerotic plaque rupture\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e. Our findings extend these observations by showing its dysregulation at the PTS stage, suggesting a role in early thrombotic remodeling prior to clinical plaque rupture.\\u003c/p\\u003e \\u003cp\\u003ehsa-miR-125b-5p was identified another PTS-associated biomarker, showing significant downregulation in both PTS and AMI patients relative to controls. This mirrors earlier reports of reduced circulating miR-125b in AMI\\u003csup\\u003e\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e. Experimental data showed that miR-125b-5p expression was suppressed in both murine AMI models and hypoxic endothelial cells\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e. Mechanistically, miR-125b-5p exerts endothelial protection by suppressing inflammation and apoptosis through inhibition of the nuclear factor of activated T cells 1 (NFAT2)/coagulation factor II thrombin receptor like 2 (F2RL2) pathway\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e, while also attenuating tumor necrosis factor superfamily, member 4 (TNFSF4)/Toll-like receptor 4 (TLR4)/NF-κB signaling in oxidized low-density lipoprotein\\u0026ndash;injured endothelial cells\\u003csup\\u003e\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e. Our data suggested the involvement of miR-125b-5p before acute ischemic events by demonstrating its dysregulation already at the PTS.\\u003c/p\\u003e \\u003cp\\u003eGATA2-AS1 expression was significantly elevated in PTS patients, in line with previous study showing higher GATA2-AS1 levels in CAD patients than healthy individuals\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e. Genetic evidence showed single-nucleotide variants within the GATA2-AS1 locus were associated with premature CAD susceptibility\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e\\u003c/sup\\u003e. Mechanistically, GATA2-AS1 enrichment in endothelial cells enhanced hypoxia-inducible factor 1α (HIF1-α)-mediated hypoxic signaling\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e, suggesting its potential role in vascular stress responses during thrombotic progression. Although experimental data support that both GATA2-AS1 and PLAU are direct targets of miR-125b-5p, it remains to be elucidated whether GATA2-AS1 competitively binds miR-125b-5p to regulate PLAU expression and what functional role this ceRNA mechanism plays in the pathogenesis of PTS.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we found multi-omic coordination and decoupling in PTS. Procrustes analysis revealed significant spatial concordance among RNA subtypes, indicating extensive transcriptomic cross-talk. Strongest alignment occurred between lncRNAs and mRNAs (M\\u0026sup2; = 0.225, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001), suggesting co-transcriptional regulation or shared epigenetic programming. Significant coordination across other RNA pairs (circRNA-mRNA, miRNA-mRNA, lncRNA-miRNA, circRNA-miRNA) demonstrated a tightly integrated regulatory architecture. This interdependence manifests functionally in ceRNA networks, where lncRNAs/circRNAs competitively bind to miRNAs via miRNA response elements (MREs) to derepress target mRNAs\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. For example, lncRNA metallothionein 1 pseudogene 3 (MT1P3) enhanced platelet activation via miR-126 sponging to upregulate P2Y12\\u003csup\\u003e9\\u003c/sup\\u003e. Conversely, Procrustes analysis revealed a clear transcript\\u0026ndash;protein discordance, a phenomenon increasingly reported across tissues and disease contexts and attributable to both biological regulation and technical factors\\u003csup\\u003e\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e. Post-transcriptional mechanisms such as alternative splicing, miRNA/lncRNA regulation, and translation efficiency can alter the relationship between transcript and protein abundance. In addition, protein-specific factors including translation rate, post-translational modification, and differential protein turnover further modulate steady-state protein levels. These processes collectively contribute to mRNA\\u0026ndash;protein discordance, as demonstrated by recent integrative temporal and pan-cancer proteogenomic studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eSeveral limitations merit consideration. First, the observational small-sample study limits causal inference and biomarker generalizability, necessitating future large-scale multi-center validation. Second, GATA2-AS1/miR-125b-5p/PLAU ceRNA network was identified based on computational prediction, and its molecular interactions lack experimental validation. Furthermore, the role of this axis in the pathophysiology of PTS remains to be fully elucidated.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study identified and validated a functionally linked ceRNA network (GATA2-AS1/miR-125b-5p/PLAU) in conjunction with FVIII as a highly accurate biomarker panel for PTS.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData availability\\u003c/h2\\u003e \\u003cp\\u003eTranscriptomics data are available in the Gene Expression Omnibus (GEO) under accession GSE303517 for lncRNA and accession GSE302816 for miRNA. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://proteomecentral.proteomexchange.org\\u003c/span\\u003e\\u003cspan address=\\\"https://proteomecentral.proteomexchange.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) via the iProX partner repository\\u003csup\\u003e\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u003c/sup\\u003e with the dataset identifier PXD066378.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCompeting interest\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare no competing interest.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e \\u003cp\\u003e The study protocol was approved by the Ethics Committee of Xiyuan Hospital of China Academy of Chinese Medical Sciences (2022XLA103-1). Written informed consents were obtained from all participants prior to the study's initiation.\\u003c/p\\u003e \\u003c/p\\u003e\\u003cp\\u003e \\u003ch2\\u003eSupplementary Information\\u003c/h2\\u003e \\u003cp\\u003eSupplementary Material.docx. Supplementary Table\\u0026nbsp;1. Scoring criteria for nailfold microcirculation. Supplementary Table\\u0026nbsp;2. Primer sequences for candidate genes used in qPCR validation. Supplementary Table\\u0026nbsp;3. The number of differentially expressed molecules. Supplementary Table\\u0026nbsp;4. 43 candidate proteins. Supplementary Table\\u0026nbsp;5. 334 candidate mRNAs. Supplementary Table\\u0026nbsp;6. 27 candidate miRNAs. Supplementary Table\\u0026nbsp;7. 253 candidate lncRNAs. Supplementary Table\\u0026nbsp;8. 8 candidate circRNAs. Supplementary Table\\u0026nbsp;9. The topology analysis of ceRNA network. Supplementary Table\\u0026nbsp;10. Abbreviations.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis work was supported by grants from the National Natural Science Foundation of China (82174214 and 82074418), the Youth Fund Project of Hangzhou Red Cross Hospital (HHQN2024002), the Key Project of the Science and Technology Innovation Program of the China Academy of Chinese Medical Sciences (CI2021A00911), and the Development and Translation of Institution-Specific Formulations and New Traditional Chinese Medicines with Intellectual Property at Xiyuan Hospital, China Academy of Chinese Medical Sciences (XYZX0304-03).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eJinwen Luo: Data curation, Writing - Original Draft, Formal analysis. Jie Gao: Methodology, Visualization. Lei Zhang: Resources, Investigation. Xuanqing Fan: Software, Formal analysis. Kangkang Wei: Software, Validation. Min Liu: Formal analysis. Min Li: Investigation. Lintong Yu: Investigation. Dazhuo Shi: Conceptualization, Review, Supervision, Funding acquisition. Xiaojuan Ma: Conceptualization, Review, Project administration. All authors approved the final manuscript.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eTranscriptomics data are available in the Gene Expression Omnibus (GEO) under accession GSE303517 for lncRNA and accession GSE302816 for miRNA. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD066378.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eMensah, G. A., Fuster, V., Murray, C. J. L. \\u0026amp; Roth, G. A. Global Burden of Cardiovascular Diseases and Risks, 1990\\u0026ndash;2022. \\u003cem\\u003eJ. Am. Coll. 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Pan-cancer proteogenomics expands the landscape of therapeutic targets. \\u003cem\\u003eCell\\u003c/em\\u003e \\u003cb\\u003e187\\u003c/b\\u003e, 4389\\u0026ndash;4407 (2024).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMa, J. et al. iProX: an integrated proteome resource. \\u003cem\\u003eNucleic Acids Res.\\u003c/em\\u003e \\u003cb\\u003e47\\u003c/b\\u003e, D1211\\u0026ndash;d1217 (2019).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen, T. et al. iProX in 2021: connecting proteomics data sharing with big data. \\u003cem\\u003eNucleic Acids Res.\\u003c/em\\u003e \\u003cb\\u003e50\\u003c/b\\u003e, D1522\\u0026ndash;d1527 (2022).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"competing endogenous RNA, prethrombotic state, coronary artery disease, biomarker, multi-omics, lncRNA\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7671890/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7671890/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eCurrent diagnostic tools lack specificity for identifying the prethrombotic state (PTS), a critical precursor to coronary thrombosis in coronary artery disease (CAD). This study aimed to discover and validate specific multi-omics biomarkers for PTS detection to enable early intervention. Integrated transcriptomic and proteomic analyses were performed on peripheral blood mononuclear cells (PBMCs) and plasma from a discovery cohort (n\\u0026thinsp;=\\u0026thinsp;28), including healthy controls, stable CAD patients without PTS (NPTS), with PTS, and acute myocardial infarction (AMI) patients. Multi-omics profiling revealed 43 proteins, 334 mRNAs, 27 miRNAs, 253 lncRNAs, and 8 circRNAs as candidate biomarkers, from which a complement and coagulation cascade\\u0026ndash;related ceRNA subnetwork was extracted. Within this network, nine hub RNAs and two proteins were prioritized, and validation in an independent cohort (n\\u0026thinsp;=\\u0026thinsp;40) identified the GATA2-AS1/miR-125b-5p/PLAU axis together with FVIII protein as significantly dysregulated in PTS. These biomarkers showed strong diagnostic performance for distinguishing PTS from NPTS: FVIII (AUC 0.88, 95% CI 0.73-1.00), PLAU (AUC 0.95, 95% CI 0.86-1.00), hsa-miR-125b-5p (AUC 0.78, 95% CI 0.57\\u0026ndash;0.99), and GATA2-AS1 (AUC 0.89, 95% CI 0.74-1.00). The GATA2-AS1/miR-125b-5p/PLAU-FVIII panel enables accurate PTS detection for early intervention.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Identification of GATA2-AS1- hsa-miR-125b-5p-PLAU ceRNA regulatory network as novel biomarkers for prethrombotic state in patients with coronary artery disease\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-29 00:44:02\",\"doi\":\"10.21203/rs.3.rs-7671890/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"1b98f90c-9199-4fa3-bcea-b40fd6659207\",\"owner\":[],\"postedDate\":\"January 29th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":61723771,\"name\":\"Health sciences/Biomarkers\"},{\"id\":61723772,\"name\":\"Health sciences/Cardiology\"},{\"id\":61723773,\"name\":\"Biological sciences/Computational biology and bioinformatics\"},{\"id\":61723774,\"name\":\"Health sciences/Diseases\"},{\"id\":61723775,\"name\":\"Biological sciences/Genetics\"}],\"tags\":[],\"updatedAt\":\"2026-01-29T00:44:05+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-29 00:44:02\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7671890\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7671890\",\"identity\":\"rs-7671890\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}