Integrated Genomic and Transcriptomic Analysis of Thrombosis-Associated Pathways in Acute Myeloid Leukemia

Integrated Genomic and Transcriptomic Analysis of Thrombosis-Associated Pathways in Acute Myeloid Leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Genomic and Transcriptomic Analysis of Thrombosis-Associated Pathways in Acute Myeloid Leukemia Oluwaseyi Eunice Bamisaye, Ayodeji Bright Ajileye, David Bolaji Akinbo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9241751/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 Background Thromboembolic complications represent a significant clinical challenge in acute myeloid leukemia (AML), contributing to morbidity and complicating disease management. Although cancer-associated thrombosis (CAT) is well established,the molecular mechanisms linking thrombosis-associated pathways with AML biology has limited information and poorly understood. Aim : This study aimed to investigate the genomic and transcriptomic landscape of thrombosis-associated genes in AML and their potential biological interaction with leukemogenic pathways. Methods Genomic and clinical data from The Cancer Genomic Atlas (TCGA) AML cohort of 200 patients were analyzed using cBioPortal. A panel of 27 thrombosis-associated genes involved in coagulation, platelet activation, endothelial regulation, and fibrinolysis was identified and selected. Genomic alterations were assessed and mutation co-occurrence with AML driver genes was evaluated using OncoPrint. Protein–protein interaction (PPI) networks were constructed with STRING, and functional enrichment analysis was performed by Enrichr. Gene expression validation was conducted using GEPIA, integrating TCGA and GTEx datasets. Survival analysis was performed using Kaplan–Meier estimation and p-value< 0.05 was statistically significant. Results Thrombosis-associated genes demonstrated measurable genomic alterations, including mRNA expression changes and copy number variations majorly in genes F2,F3,F10,P2RY12,and SERPINE1. However, these genes showed limited statistically significant co-occurrence with canonical AML driver mutations with SERPINE1 and FLT3 (log2 odds ratio =0.918;p >0.05) and P2RY12 and RUNX1 (log2 odds ratio =0.272;p>0.001).The PPI network analysis revealed two distinct clusters corresponding to thrombosis-related and leukemogenic genes, with limited bridging interactions involving RUNX1,FLT3 and platelet-associated genes ITGA2B,ITGB3 and ITGA2B respectively. Functional enrichment analysis identified significant pathways related to complement and coagulation cascades, platelet activation, and fibrin clot formation. The gene expression analysis revealed low expression of classical coagulation factors (F2,F10) and moderate expression of platelet activation, endothelial interaction, and fibrinolysis-related genes. Survival analysis showed no significant association between thrombosis gene alterations and overall survival. Conclusion Thrombosis-associated genes in AML form a functionally coherent network that operates largely independently of canonical driver mutations but may contribute to a pro-thrombotic microenvironment initiation through platelet activation, endothelial signaling, and fibrinolysis regulation. These findings showed the importance of tumor–microenvironment interactions in AML-associated thrombosis and provide a basis for future studies exploring predictive biomarkers and therapeutic targets. Acute Myeloid Leukemia Cancer-Associated Thrombosis TCGA Gene Expression AML-Thrombosis Genomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Thromboembolic complications are a major challenge in cancer management to the extent that venous thromboembolism (VTE) is the second leading cause of morbidity and mortality among cancer patients. Cancer-associated thrombosis (CAT) arises from complex interactions between tumor cells, the vascular endothelium, and the hemostatic system, with an estimated 10–15% of patients developing thrombotic events during disease progression. The incidence varies widely depending on tumor type, stage, and treatment modalities [ 1 – 3 ]. In hematologic malignancies such as acute myeloid leukemia (AML), thrombosis, which manifests as deep vein thrombosis or pulmonary embolism, remains a significant complication, despite being historically under-recognized. Although AML management has focused on improving survival through targeted therapies, however, investigative and supportive care addressing complications like thrombosis remains limited. Notably, thrombocytopenia, which is an hallmark of AML due to replacement of normal bone marrow by malignant blasts, increases bleeding risk but does not protect against thrombosis thereby highlighting the complex dysregulation of hemostasis in this disease[ 4 ]. Thrombotic events in AML have been reported in both newly diagnosed and treated patients, with understanding of mechanisms thought to involve leukemic cell-induced procoagulant activity, endothelial dysfunction, inflammatory signaling, and treatment-related effects [ 5 ]. However, compared to some solid tumors, the molecular basis underlying thrombosis in AML remains less clearly defined. Even though the specific cytogenetic and molecular abnormalities have been extensively studied in relation to leukemogenesis and prognosis, their relationship with thrombosis-associated pathways is still not fully understood. Recent advances in high-throughput sequencing and the availability of large-scale cancer genomics datasets, such as The Cancer Genome Atlas (TCGA), have provided opportunities to explore the molecular landscape of AML beyond traditional driver mutations [ 6 ]. These datasets enable the integrative analysis of gene expression, genomic alterations, and pathway interactions, allowing for the identification of biological networks that may contribute to disease complications, including thrombosis. In particular, genes involved in coagulation, platelet activation, endothelial interaction, and fibrinolysis which represent critical components of the hemostatic system that may play a role in modulating thrombotic risk in AML patients. Studies have suggested that cancer-associated thrombotic complications are not solely driven by the mutated cancer genes but may also arise from dysregulation of hemostatic pathways and tumor–microenvironment interactions [ 7 , 8 ]. However, there is need for a comprehensive analysis integrating thrombosis-associated gene alterations, functional network interactions, and transcriptional activity within the AML genomic, which still remains limited. Furthermore, the extent to which these pathways interact with or operate independently of canonical AML driver mutations has not been fully elucidated. In this study, we performed an integrated genomic and transcriptomic analysis of thrombosis-associated genes in the TCGA AML cohort using a combination of bioinformatics analysis. Specifically, we examined the genomic alteration landscape of thrombosis-related genes, evaluated their co-occurrence with established AML driver mutations, analyzed protein–protein interaction networks, and conducted pathway enrichment analysis to identify key biological processes involved. Additionally, we performed gene expression validation using the GEPIA platform to assess transcriptional activity in AML compared to normal tissues. Through these analyses, we aimed to elucidate the functional relevance of thrombosis-associated pathways in AML disease severity and to explore potential mechanistic links between leukemogenesis and contributors to thrombotic complications. To address this gap, this study aimed to perform an integrated genomic and transcriptomic analysis of thrombosis-associated genes in AML using the TCGA cohort. By combining genomic alteration profiling, protein–protein interaction network analysis, pathway enrichment, and gene expression validation using independent datasets, we sought to elucidate the biological relevance and mechanistic interplay between thrombosis-related pathways and leukemogenic processes. Methods Study Design and Data Source This study investigated thrombosis-associated genes in Acute Myeloid Leukemia (AML), genomic and clinical data were obtained from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia cohort through the cBioPortal for Cancer Genomics platform ( https://www.cbioportal.org ) (accessed 06 February 2026). The dataset used was Acute Myeloid Leukemia (TCGA, PanCancer Atlas), which contains data from approximately 200 AML patients [ 9 ] with available genomic alteration data, copy number variation data, mRNA expression profiles and clinical informations. The workflow pattern that guided this study is represented in Fig. 1 . Selection of Thrombosis-Associated Genes A panel of twenty-seven (27) thrombosis-associated genes were curated based on known involvement in coagulation, platelet activation, and fibrinolysis pathways reported via literature and biological pathway databases such as National Center for Biotechnology Information ( https://www.ncbi.nlm.nih.gov/gene ); Kyoto Encyclopedia of Genes and Genomes (KEGG) ( https://www.genome.jp/kegg )) [ 10 , 11 ]. The genes analyzed are categorized based on the mechanism of action of the proteins they code for and these are (Supplementary file 1 contains the full gene names by category): Coagulation cascade genes- F2, F3, F5, F7 F8 F9 F10 F11 F12 F13A1 Natural anticoagulant System- PROC, PROS1, SERPINC1, THBD, PROCR Fibrinolysis - PLAT, PLG, SERPINE1, SERPINF2 Platelet activation and adhesion genes- ITGA2B, ITGB3, GP1BA, P2RY12 Endothelial-Thrombosis Regulation- VWF, ADAMTS13, SELE, ICAM1 Genomic Alteration Analysis Genomic alterations in the 27 carefully selected thrombosis-associated genes were analyzed using the OncoPrint tool within cBioPortal ( https://www.cbioportal.org ). The generated OncoPrint was used to assess the frequency and distribution of genetic alterations including mutations, copy number variations, and mRNA expression changes across AML samples. The percentage of patients exhibiting alterations in each gene was calculated based on the TCGA-AML dataset. AML Driver Mutation Visualization The genomic landscape of AML driver mutations was analyzed on the cBioPortal for TCGA Acute Myeloid Leukemia (PanCancer Atlas), and 12 commonly mutated genes were identified based on their frequency in the cohort. The genomic alterations including somatic mutations and copy number variations visualized using the OncoPrint and they include FLT3, NPM1, DNMT3A, IDH1, IDH2, RUNX1, TET2, ASXL1, ETV6, BCORL1, SETBP1, and ZRSR2 (refer to Supplementary1 document for the names). Mutation Co-occurrence Analysis This study further investigated potential relationships between thrombosis-associated genes and AML driver mutations whereby mutation co-occurrence analysis was performed using the Mutual Exclusivity module in the cBioPortal platform. This analysis evaluates whether gene alterations tend to occur concurrently (co-occurrence) or rarely appear together (mutual exclusivity) across patient samples. Statistical significance was assessed using Fisher’s exact test to generate p-value, and the strength of association was represented by the log2 odds ratio. False discovery rate correction was applied to generate q-values for multiple testing. Survival Analysis Kaplan–Meier survival analysis was performed using the Comparison/Survival tool in cBioPortal platform. The patients were grouped based on the presence or absence of genomic alterations in the queried genes and overall survival was defined as the time from diagnosis to death or last follow-up available in the dataset. Survival differences between groups were evaluated using the log-rank test, and p-values less than 0.05 were considered statistically significant. Protein–Protein Interaction Network Analysis The functional relationships among the selected genes was further investigated for the protein–protein interaction (PPI) analysis using the STRING database (version 12.0) ( https://version-12-0.string-db.org/ ). The selected thrombosis-associated and major AML driver genes set was uploaded to STRING with the organism set to Homo sapiens to generate interaction networks at a minimum interaction confidence score of 0.7 (high confidence). This yielded a visualized network of functional associations between proteins based on experimental evidence, curated databases, co-expression, and text mining. Then, the network topology was analyzed to identify clusters and potential hub proteins within the thrombosis and AML mutation pathways. Functional Enrichment Analysis The functional enrichment analysis was performed using the Enrichr ( https://maayanlab.cloud/Enrichr/ ) [ 12 ] to identify specific biological pathways associated with the thrombosis gene panel. Gene lists were analyzed against multiple pathway databases -KEGG Pathway Database, Reactome Pathway Database and Gene Ontology Biological Processes. The platform was used to determine the enrichment significance using adjusted p-values derived from Fisher’s exact test with multiple testing correction. Pathways with adjusted p-values < 0.05 were considered significantly enriched. Then, the Enrichment results were visualized using bar plots representing the most significantly enriched pathways. Gene Expression Validation using GEPIA The Gene Expression Profiling Interactive Analysis (GEPIA) platform ( http://gepia2.cancer-pku.cn ) was used to validate the transcriptional expression of thrombosis-associated genes in the Acute Myeloid Leukemia (AML) cohort since it integrates RNA sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The expression profiles of selected thrombosis-related genes including F2, F3, F10, P2RY12, SERPINE1, PROCR, VWF, ITGA2B, and ITGB3 (based on their expression frequencies) were analyzed across the TCGA AML dataset (LAML) and corresponding normal tissue samples from GTEx. Expression comparisons between tumor and normal samples were performed using the box plot module, gene expression values were normalized and presented as log2 (TPM + 1), where TPM represents transcripts per million. Box plots were generated for each gene to visualize expression differences between AML tumor samples and normal tissues. The number of samples included in the analysis was automatically defined by the GEPIA platform based on available TCGA and GTEx data, with approximately 173 AML samples and 70 normal samples included in the analysis. Statistical Analysis All statistical analyses were performed using tools integrated within cBioPortal and Enrichr. Fisher’s exact test was used to evaluate co-occurrence and mutual exclusivity between gene alterations for mutation association analysis. Survival analysis was conducted using Kaplan–Meier estimation with log-rank testing. Statistical significance between tumor and normal expression levels was evaluated on the GEPIA default statistical framework, which employs analysis of variance (ANOVA) for differential expression analysis. A p-value < 0.05 was considered statistically significant. Results Genomic Landscape of Thrombosis-Associated Genes in AML OncoPrint analysis demonstrated that a subset of AML patients exhibited genomic alterations in thrombosis-associated genes, including mRNA expression changes, copy number variations, and occasional mutations. Among the analyzed genes, F2, F3, F10, P2RY12, and SERPINE1 demonstrated relatively higher alteration frequencies compared to other members of the panel and these alterations were distributed across multiple AML samples. Figure 2a-e demonstrate the oncoprint analysis of the various hemostasis associated genes, segregated based on their functions, with their genomic alterations in the AML cohort. Genomic Landscape of AML Driver Mutations Analysis of the TCGA AML dataset revealed recurrent alterations in several key leukemia-associated genes, including FLT3, NPM1, DNMT3A, IDH1, IDH2, RUNX1, and TET2 (Fig. 3 ). Among these genes, FLT3 (31%) and NPM1 (27%) exhibited the highest mutation frequencies, consistent with previously reported molecular profiles of AML. Mutations in DNMT3A were also commonly observed, while IDH1, IDH2, RUNX1, and TET2 displayed moderate mutation frequencies across the cohort (Fig. 3 ). Other less frequent but present mutations in the cohort include ASXL1, ETV6, BCORL1, SETBP1, and ZRSR2. These characteristic mutation spectrum of AML genes generated from the cohort provided a genomic alteration basis for subsequent analyses of thrombosis-associated genes within the same patient population. Mutation Co-occurrence Analysis with AML Driver Genes When the thrombosis gene panel was analyzed alongside the assessed common AML driver genes, FLT3, NPM1, DNMT3A, IDH1, IDH2, RUNX1, and TET2; significant co-occurrence patterns were observed among several AML driver genes (Table 1 ). Categorizing based on co-occurrence with the most significant relationship (p < 0.001), FLT3 and NPM1 mutations demonstrated strong co-occurrence at log2 odds ratio = 2.097 and p < 0.001. Similarly, NPM1 and DNMT3A mutations showed significant co-occurrence (log2 odds ratio = 1.748; p = 0.001). Conversely, several AML mutation pairs displayed mutual exclusivity, including FLT3 and RUNX1 (log2 odds ratio = − 2.726, p < 0.001) and NPM1 and RUNX1 (log2 odds ratio = − 2.457, p = 0.003), all of which are consistent with known genomic relationships among AML driver mutations. In contrast, thrombosis-associated genes demonstrated limited statistically significant co-occurrence with AML driver mutations, the SERPINE1 and FLT3 showed insignificant co-occurrence of log2 odds ratio = 0.918; p > 0.05; P2RY12 and RUNX1 at log2 odds ratio = 0.272; p > 0.001 while F3 and F10 had similar insignificant relationship (p > 0.001) at log2 odds ratio = 0.051. Also, there was a statistically insignificant mutual exclusivity between the F10 and NPM1 as well as P2RY12 and IDH2 genes (p > 0.05). Survival Analysis of Thrombosis-Associated Gene Alterations Kaplan–Meier survival analysis demonstrated no statistically significant difference in overall survival between patients with and without alterations in thrombosis-associated genes (log-rank p = 0.201) (Fig. 4 ). Similar findings were observed when individual genes were evaluated separately, indicating that genomic alterations in thrombosis-related genes were not significantly associated with overall survival in the TCGA AML cohort. Protein–Protein Interaction Network Analysis The resulting interaction network revealed the thrombosis and AML driver genes functional clusters (Fig. 5 ). The first cluster consisted of the thrombosis-associated genes involved in coagulation and platelet activation- F2, F3, F5, F7, F10, VWF, SERPINE1, PLAT, PROC, PROS1, SERPINC1, ITGA2B, ITGB3, and P2RY12- whose proteins formed a highly interconnected network representing key components of the hemostatic system. The second cluster comprised the AML driver genes NPM1, DNMT3A, FLT3, IDH1, IDH2, TET2, RUNX1, ASXL1, BCORL1, SETBP1, and ZRSR2 which formed a dense interaction network associated with leukemogenic signaling and epigenetic regulation. Interestingly, limited interconnectivity between two clusters was observed between RUNX1 and platelet-associated genes ITGA2B and ITGB3, as well as FLT3 and ITGA2B. Both interconnectivities appeared to form potential bridging interactions. Functional Enrichment Analysis The KEGG pathway analysis revealed that the most significantly enriched pathways included complement and coagulation cascades, platelet activation, fluid shear stress and atherosclerosis, ECM–receptor interaction, and integrin signaling pathways. Among these pathways, the complement and coagulation cascade pathway exhibited the strongest enrichment, confirming the central role of the selected genes in thrombin generation and fibrin clot formation (Fig. 6 ). The reactome pathway enrichment analysis further highlighted pathways directly involved in coagulation and hemostasis to include formation of fibrin clot (clotting cascade),hemostasis, common pathway of fibrin clot formation, intrinsic pathway of fibrin clot formation and the platelet activation signaling. These pathways collectively represent the sequential processes involved in thrombus formation and stabilization (Fig. 6 ). Gene Ontology biological process analysis demonstrated strong enrichment in processes related to coagulation and fibrinolysis, including: positive regulation of blood coagulation, negative regulation of blood coagulation, regulation of fibrinolysis, negative regulation of hemostasis and the regulation of blood coagulation, which indicates that the presence of both pro-coagulant and anticoagulant regulators in the selected panel (Fig. 6 ). Transcriptional Expression Validation of Thrombosis-Associated Genes in AML and Normal Tissues Analysis of the coagulation cascade genes F2, F3, and F10 revealed generally low transcriptional expression in AML samples compared with normal tissues in the GEPIA platform (Fig. 7 a-i). In particular, F2 and F10 demonstrated minimal transcriptional expression across AML samples while F3 (tissue factor) exhibited moderate expression variability across AML samples. Evaluation of genes associated with platelet activation pathways revealed measurable expression levels in AML samples. The platelet receptor genes ITGA2B and ITGB3, which encode the integrin αIIbβ3 complex responsible for platelet aggregation, displayed moderate expression across the AML cohort. Similarly, P2RY12, a key ADP receptor involved in platelet activation, showed detectable expression in a subset of AML samples. Furthermore, genes involved in endothelial interaction and fibrinolysis regulation also demonstrated variable expression patterns. VWF, which mediates platelet adhesion to vascular endothelium, exhibited moderate transcriptional activity across AML samples. Additionally, SERPINE1, a major inhibitor of fibrinolysis, showed heterogeneous expression among AML patients while, the anticoagulant pathway regulator, PROCR demonstrated moderate expression variability within the AML cohort. Generally, the GEPIA expression analysis indicates that while classical coagulation factors such as F2 and F10 exhibit limited transcriptional activity in AML cells, several genes involved in platelet activation, endothelial interaction, and fibrinolysis regulation are actively expressed in AML samples. Mechanistic Model linking AML genomic alterations to thrombosis-associated pathways. The analyses in this study support a model illustrated in Fig. 8 which explains series of events leading to generation of prothrombotic state in AML. Genomic alterations in AML driver genes disrupt hematopoietic transcriptional programs, particularly through the RUNX1-mediated regulation of megakaryocyte differentiation. This dysregulation may influence platelet activation pathways involving ITGA2B, ITGB3, P2RY12, and VWF. These pathways interact with coagulation cascade components including F3, F10, and F2, leading to thrombin generation and fibrin clot formation. Concurrent regulation of fibrinolysis by SERPINE1 and PROCR may further promote clot stabilization. Together, these molecular processes contribute to a pro-thrombotic microenvironment in AML. Discussion Thrombotic complications remain a significant clinical challenge in the management of acute myeloid leukemia (AML), often contributing to increased morbidity and complexity of treatment. Despite advances in leukemia care, there is still limited mechanistic understanding linking thromboembolic events directly to the underlying pathology of AML. In many cases, thrombotic manifestations are detected at advanced stages of the disease, thereby complicating therapeutic decision-making and frequently necessitating reliance on empirical clinical judgment. This gap in mechanistic insight hinders the development of precise, evidence-based strategies and could pose a barrier to the realization of the needed personalized medicine in AML care. Consequently, there is a critical need to identify robust predictive biomarkers and molecular determinants that can guide clinicians in the early detection and management of thrombotic risk in AML patients [ 5 ] . Findings from this study demonstrated that thrombosis-associated genes exhibit measurable genomic alterations in a subset of AML patients, including mRNA expression changes and copy number variations. Although the alteration frequency of individual genes were modest, their collective presence suggests that dysregulation of hemostatic pathways may occur alongside leukemogenic processes. Importantly, these alterations may not function as primary drivers of thrombotic complications but rather act as modulatory factors influencing the thrombotic phenotype in AML. This is because reports have indicated that cancer-associated thrombosis (CAT) arises not solely from genetic mutations but also from complex interactions involving gene expression changes and tumor–host dynamics [ 7 ]. Indeed, non-specific factors such as the tumor microenvironment and chemotherapy have been implicated in the pathogenesis of CAT. Inflammatory cytokines such as interleukin-6 (IL-6), interleukin-1 (IL-1), and tumor necrosis factor-alpha (TNF-α) promote a procoagulant state by stimulating tissue factor expression on monocytes and endothelial cells while suppressing thrombomodulin activity. In addition, tumor-derived microvesicles containing podoplanin (PDPN) can directly activate factor X or platelets, thereby enhancing thrombus formation [ 13 , 14 ]. Chemotherapeutic agents, including cisplatin and anthracyclines, further contribute to this process by inducing endothelial injury, exposing the subendothelial matrix, triggering apoptosis, and releasing prothrombotic particles from both malignant and non-malignant cells. Moreover, these agents can increase tissue factor expression, reduce natural anticoagulants such as protein C, protein S, and antithrombin, activate platelets, and elevate circulating clotting factor levels, collectively promoting a hypercoagulable state [ 15 – 17 ]. Furthermore, oncogenic pathways driven by genetic mutations or epigenetic modifications, such as DNA methylation, may influence the procoagulant properties of cancer cells, thereby modulating the risk of thromboembolic events. Consequently, the diversity of oncogenic drivers and their downstream effector mechanisms may contribute to the heterogeneity observed in CAT across different cancer types, molecular subtypes, and individual patients, resulting in either thrombosis-promoting or protective effects [ 7 , 18 ]. The genomic landscape of AML driver mutations observed in this study, including frequent alterations in FLT3, NPM1, and DNMT3A, aligns with established molecular classifications of AML, and this forms the basis of targeted therapy in the patients [ 19 ] .The co-occurrence analysis further confirmed known relationships among AML driver genes, such as the strong co-occurrence between FLT3 and NPM1 and the mutual exclusivity between FLT3 and RUNX1 as observed by Nong et al .(2024) [ 3 ]. Importantly, this analysis revealed that thrombosis-associated genes demonstrated limited statistically significant co-occurrence with AML driver mutations, suggesting that these pathways may operate largely independently of the core leukemogenic mutation network. This supports the model that thrombosis in AML arises from a complex interplay of leukemic biology, microenvironmental interactions, and systemic factors rather than direct canonical driver mutations. This dissociation may be explained by the multifactorial nature of thrombotic complications in AML, which are driven by interactions between leukemic cells and the vascular microenvironment, including tissue factor expression, release of procoagulant microparticles, inflammatory signaling, and endothelial activation, as well as the effects of therapeutic interventions [ 4 , 19 ]. Protein–protein interaction network analysis provided further insight into the functional organization of these gene sets. Two major clusters were identified: one comprising thrombosis-associated genes involved in coagulation, platelet activation, and fibrinolysis, and another consisting of AML driver genes associated with transcriptional regulation and epigenetic control. Notably, limited interconnectivity between these clusters was observed, with RUNX1 (Runt-Related Transcription Factor 1) and FLT3 (Fms-like tyrosine kinase 3 internal tandem duplication) of the AML, and platelet-related genes ITGA2B and ITGB3 forming potential bridging nodes. Given the well-established role of RUNX1 in megakaryocyte differentiation and platelet biology, this finding suggests a plausible mechanistic link between leukemogenic transcriptional dysregulation and thrombosis-related pathways. And this could be because transcriptional and epigenetic dysregulation associated with AML driver mutations, particularly involving factors such as RUNX1, may indirectly influence platelet-related and hemostatic pathways by impairing megakaryocyte differentiation, maturation, and platelet function [ 20 , 21 ] . In addition, the interaction between FLT3 and ITGA2B in AML is a complex mechanism recently elaborated more to contribute to hypercoagulable states and thrombotic complications [ 22 ]. FLT3-ITD, a common mutation causing constitutive signaling has been seen to promote an aberrant, immature megakaryocytic phenotype in AML blasts, leading to increased adhesion and activation of platelets and megakaryocytes through ITGA2B [ 23 ], thus enhancing thrombogenicity The functional enrichment analysis reinforced the biological relevance of the thrombosis gene panel. KEGG and Reactome pathway analyses identified significant enrichment in complement and coagulation cascades, platelet activation, and fibrin clot formation pathways, while Gene Ontology analysis highlighted processes such as regulation of blood coagulation and fibrinolysis. These results confirm that the selected genes are functionally coherent and collectively participate in key pathways governing thrombus formation and hemostasis. Notably, the enrichment of both pro-coagulant and anticoagulant processes reflects the complex regulatory balance of the hemostatic system. Gene expression validation using the GEPIA platform provided additional insight into the transcriptional activity of these genes in AML. Classical coagulation factors such as F2 and F10 exhibited low expression levels in AML samples which is consistent with their primary synthesis in hepatic tissues. In contrast, genes involved in platelet activation (ITGA2B, ITGB3, P2RY12), endothelial interaction (VWF), and fibrinolysis regulation (SERPINE1, PROCR) demonstrated measurable and variable expression across AML samples. These findings suggest that thrombosis in AML may be driven not by direct expression of coagulation factors within leukemic cells, but rather through interactions of the cells in platelet activation, endothelial signaling, and regulation of fibrinolysis within the tumor microenvironment. This stems from studies demonstrating that leukemic cells have capacity to influence surrounding vascular and hematopoietic components by promoting platelet activation pathways, enhancing endothelial dysfunction, and altering the balance between procoagulant and anticoagulant systems, thereby creating a pro-thrombotic milieu [ 24 , 25 ]. Therefore, this iterates the importance of tumor–microenvironment interactions in mediating thrombotic risk in AML and stresses that thromboembolic complications are likely a consequence of coordinated cellular and molecular crosstalk except for the link of specific driver genes, RUNX and FLT3, with platelet differentiation Based on these observations, we propose a mechanistic model in which AML driver mutations disrupt hematopoietic transcriptional programs, particularly through RUNX1-mediated regulation of megakaryocyte differentiation. This dysregulation may influence platelet activation pathways, which in turn interact with coagulation cascade components and fibrinolysis regulators, ultimately promoting a pro-thrombotic microenvironment. This provides a biologically probable framework linking leukemogenesis with thrombosis-associated pathways via a mechanistic model as similarly described by Szarpak et al. , (2026) [ 26 ]. Despite these findings, some limitations are acknowledged in this study. Firstly, it is based on retrospective bioinformatics analysis of publicly available datasets, and therefore lacks experimental validation. Secondly, the TCGA AML cohort has limited normal control samples, necessitating the use of GTEx data for comparison in expression analyses. Thirdly, the study does not include clinical variables such as treatment regimens or comorbidities that may influence thrombosis risk. Future studies incorporating experimental validation, larger cohorts, and clinical correlation are needed to further elucidate the mechanisms underlying thrombosis in AML. In conclusion, this study demonstrates that thrombosis-associated genes in AML form a functionally enriched network involved in coagulation, platelet activation, and fibrinolysis pathways. While these genes appear to operate largely independently of canonical AML driver mutations, potential interactions through transcriptional regulators, RUNX1 and FLT3, suggest a link between leukemogenesis and thrombosis-related processes. Therefore, future studies incorporating experimental validation and external cohorts are required for information. Also, these findings provide new insights into the molecular basis of thrombosis in AML, therefore, informed future studies aimed at identifying biomarkers or therapeutic targets for managing thrombotic complications in leukemia patients. Declarations Acknowledgement We appreciate the GENOMAC OMICS Hub for their technical expertise in the course of this study. Funding This study received no external funding Data availability All data created or examined in this study are included in this published article and its supplementary information files. Authors’ Contribution OEB conceptualized and designed the study, coordinated the collaboration, drafted the initial version of the manuscript and performed the statistical analyses. OEB, ABA and DBA were involved in data validation and acquisition, interpreted analyzed data. OEB and LS contributed to clinical interpretation of the technicalities and biological relevance of the study. ABA and DBA contributed to critical revision of the manuscript. All authors read and approved the final manuscript. Clinical Trial Number Not Applicable Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests We hereby disclose no conflicts of interest in this work. Data Availability All data generated or analysed during this study are publicly available and their links included in this published article [and its supplementary information files]. References Tsantes AG, Petrou E, Tsante KA, Sokou R, Frantzeskaki F, Domouchtsidou A, et al. 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Predictors of early thrombotic events in AML patients. Cancers (Basel). 2022;14(22):5640. Morgan NV, Daly ME. RUNX1 mutations and inherited bleeding. Platelets. 2017;28(2):208–10. Yokota A, Huo L, Lan F, Wu J, Huang G. RUNX1 mutations in hematological malignancies. Mol Cells. 2020;43(2):145–52. Carbonell D, Chicano M, Cardero AJ, Gómez-Centurión I, Bailén R, Oarbeascoa G, et al. FLT3-ITD as a biomarker in AML. Cancers (Basel). 2022;14(16):4006. Molina-Ortiz P, Polizzi S, Ramery E, Gayral S, Delierneux C, Oury C, et al. Rasa3 regulates megakaryocyte differentiation. PLoS Genet. 2014;10(6):e1004420. Fodil S, Arnaud M, Vaganay C, Puissant A, Lengline E, Mooney N, et al. Endothelial cells in acute myeloid leukemia. Blood Rev. 2022;54:100932. Xu C, Lu T, Lv X, Cheng T, Cheng H. Bone marrow vascular niche in AML. Blood Sci. 2023;5(2):92–100. Szarpak L, Jach ME, Skoczylas M, Radej S, Pruc M. Clonal hematopoiesis in pulmonary embolism. Int J Mol Sci. 2026;27(6):2750. Additional Declarations No competing interests reported. Supplementary Files Supplementary1Alteration.xlsx SUPPLEMENTARYMATERIALS.docx 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-9241751","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615910300,"identity":"a3b3b852-f056-4f18-8a6e-f5f3b520dead","order_by":0,"name":"Oluwaseyi Eunice Bamisaye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACAyA+AOMc+AAk2NhJ0XJwBkgLMxFa4ICZB0wS0GLOfjrx4I+aw3Ly7T2Gh21+bZPnY2Zg/PAxB7cWy57cDYd5jh02NjhzxuBwbt9twzZmBmbJmdvwOOwAUAsD2+HEDRI5QC09txmBWtiYefFpOf92w8Ef/w7Xz5//xuCwZc9te8JabuRuOMDbdjiB4QaPwWGGH7cTCWqxnPF2w2HevnTDDWfSCg72NtxObmNmbMbrF3P+3M0ff3yzlpdvP7z5w48/t23ntzcf/PARjxYoaAZiDgMGxjYQh7GBoHogqANi9gcMDH+IUTwKRsEoGAUjDQAAX5RbtJZjfu4AAAAASUVORK5CYII=","orcid":"","institution":"University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Oluwaseyi","middleName":"Eunice","lastName":"Bamisaye","suffix":""},{"id":615910301,"identity":"9ccf55fc-6db4-4ea2-ba7f-9278ab991a09","order_by":1,"name":"Ayodeji Bright Ajileye","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Ayodeji","middleName":"Bright","lastName":"Ajileye","suffix":""},{"id":615910302,"identity":"5ef05217-9592-4c7b-8069-db032e957df6","order_by":2,"name":"David Bolaji Akinbo","email":"","orcid":"","institution":"University of Iowa","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"Bolaji","lastName":"Akinbo","suffix":""},{"id":615910303,"identity":"82c35585-41f2-45f1-86df-b38d993e8477","order_by":3,"name":"Lindiwe Sibanda","email":"","orcid":"","institution":"Sefako Makgatho Health Sciences University","correspondingAuthor":false,"prefix":"","firstName":"Lindiwe","middleName":"","lastName":"Sibanda","suffix":""}],"badges":[],"createdAt":"2026-03-27 07:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9241751/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9241751/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105985994,"identity":"99013580-6b34-48da-afe7-8280ea399831","added_by":"auto","created_at":"2026-04-02 07:27:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Work Flow for the assessment of coagulation pathway genes alteration in Acute Myeloid Leukemia using the TCGA dataset\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/1238661c267016bb7b412c44.png"},{"id":105985971,"identity":"a90c5724-e4b4-4438-86d1-db7593ea5246","added_by":"auto","created_at":"2026-04-02 07:26:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176955,"visible":true,"origin":"","legend":"\u003cp\u003eOncoPrint of the thrombosis-associated genes alterations in the AML population. a. Expression of mRNA alteration of the Coagulation cascade genes F2, F3, F5, F7, F8, F9, F10, F11, F12 and F13A1 ; b. Natural anticoagulant genes PROC, PROS1, SERPINC1, THBD and PROCR; c. Fibrinolysis genes PLAT, PLG, SERPINE1 and SERPINF2; d. Platelet activation genes ITGA2B, ITGB3, GP1BA and P2RY12; e. Endothelial thrombosis genes VWF, ADAMTS13, SELE and ICAM1.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/54ab5d195cb655bdb0af23c7.png"},{"id":105985990,"identity":"2abce8ce-ef9e-432b-9e10-d2247b1c467a","added_by":"auto","created_at":"2026-04-02 07:27:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe OncoPrint visualization of AML driver mutations in the TCGA AML cohort.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/0e1e2aecc0417089b14a0ea6.png"},{"id":105986085,"identity":"c3894627-c0d6-4f74-936e-18d26690c946","added_by":"auto","created_at":"2026-04-02 07:27:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival Analysis of the thrombosis associated genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/7339e68278a0ee1d122cc164.png"},{"id":105986070,"identity":"1fea90fd-5f0e-48f3-9fa7-4c34d830be66","added_by":"auto","created_at":"2026-04-02 07:27:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":279729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein Interaction network between the thrombosis and AML driver genes functional clusters\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/4a7af9f99ab963291fb669bf.png"},{"id":105986005,"identity":"3d1fb628-af60-4d30-98a2-d0e79d7dcb7e","added_by":"auto","created_at":"2026-04-02 07:27:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":175292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of thrombosis-associated genes. (a) KEGG pathways; (b) Reactome pathways; (c) Gene Ontology biological processes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/86ccd57d4dc6f4ae75019f24.png"},{"id":105985947,"identity":"73a97806-f116-49c0-9df8-032b4dcb94e1","added_by":"auto","created_at":"2026-04-02 07:26:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":137008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of transcriptional activity of the thrombosis-associated genes in AML. (a) F2; (b) F3; (c) F10; (d) P2RY12; (e) SERPINE1; (f) PROCR; (g) VWF; (h) ITGA2B; (i) ITGB3.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/2993874e9e00874d02f34086.png"},{"id":105985961,"identity":"05897919-ef0f-4c6d-a2e3-3d2abf5d9322","added_by":"auto","created_at":"2026-04-02 07:26:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":367224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed mechanistic model linking AML genomic alterations to thrombosis-associated pathways.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/ed0c31905d941f38529bc687.png"},{"id":105986298,"identity":"a11128c2-0772-44b6-be91-8b8bb3debabf","added_by":"auto","created_at":"2026-04-02 07:28:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2562641,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/7202576b-d8b0-49cc-86f0-95ba7cbfd6f8.pdf"},{"id":105985945,"identity":"ec238fe1-4956-4550-894f-ecb8d1cc94e4","added_by":"auto","created_at":"2026-04-02 07:26:45","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":66580,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1Alteration.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/746caa21831475813a9b3263.xlsx"},{"id":105985985,"identity":"0f906bf7-ccf8-46c0-9241-bce3bbbf3a14","added_by":"auto","created_at":"2026-04-02 07:27:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17843,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALS.docx","url":"https://assets-eu.researchsquare.com/files/rs-9241751/v1/4f0d953174febe88042e52f5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Genomic and Transcriptomic Analysis of Thrombosis-Associated Pathways in Acute Myeloid Leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThromboembolic complications are a major challenge in cancer management to the extent that venous thromboembolism (VTE) is the second leading cause of morbidity and mortality among cancer patients. Cancer-associated thrombosis (CAT) arises from complex interactions between tumor cells, the vascular endothelium, and the hemostatic system, with an estimated 10\u0026ndash;15% of patients developing thrombotic events during disease progression. The incidence varies widely depending on tumor type, stage, and treatment modalities [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn hematologic malignancies such as acute myeloid leukemia (AML), thrombosis, which manifests as deep vein thrombosis or pulmonary embolism, remains a significant complication, despite being historically under-recognized. Although AML management has focused on improving survival through targeted therapies, however, investigative and supportive care addressing complications like thrombosis remains limited. Notably, thrombocytopenia, which is an hallmark of AML due to replacement of normal bone marrow by malignant blasts, increases bleeding risk but does not protect against thrombosis thereby highlighting the complex dysregulation of hemostasis in this disease[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThrombotic events in AML have been reported in both newly diagnosed and treated patients, with understanding of mechanisms thought to involve leukemic cell-induced procoagulant activity, endothelial dysfunction, inflammatory signaling, and treatment-related effects [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, compared to some solid tumors, the molecular basis underlying thrombosis in AML remains less clearly defined. Even though the specific cytogenetic and molecular abnormalities have been extensively studied in relation to leukemogenesis and prognosis, their relationship with thrombosis-associated pathways is still not fully understood.\u003c/p\u003e \u003cp\u003eRecent advances in high-throughput sequencing and the availability of large-scale cancer genomics datasets, such as The Cancer Genome Atlas (TCGA), have provided opportunities to explore the molecular landscape of AML beyond traditional driver mutations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These datasets enable the integrative analysis of gene expression, genomic alterations, and pathway interactions, allowing for the identification of biological networks that may contribute to disease complications, including thrombosis. In particular, genes involved in coagulation, platelet activation, endothelial interaction, and fibrinolysis which represent critical components of the hemostatic system that may play a role in modulating thrombotic risk in AML patients.\u003c/p\u003e \u003cp\u003eStudies have suggested that cancer-associated thrombotic complications are not solely driven by the mutated cancer genes but may also arise from dysregulation of hemostatic pathways and tumor\u0026ndash;microenvironment interactions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, there is need for a comprehensive analysis integrating thrombosis-associated gene alterations, functional network interactions, and transcriptional activity within the AML genomic, which still remains limited. Furthermore, the extent to which these pathways interact with or operate independently of canonical AML driver mutations has not been fully elucidated.\u003c/p\u003e \u003cp\u003eIn this study, we performed an integrated genomic and transcriptomic analysis of thrombosis-associated genes in the TCGA AML cohort using a combination of bioinformatics analysis. Specifically, we examined the genomic alteration landscape of thrombosis-related genes, evaluated their co-occurrence with established AML driver mutations, analyzed protein\u0026ndash;protein interaction networks, and conducted pathway enrichment analysis to identify key biological processes involved. Additionally, we performed gene expression validation using the GEPIA platform to assess transcriptional activity in AML compared to normal tissues. Through these analyses, we aimed to elucidate the functional relevance of thrombosis-associated pathways in AML disease severity and to explore potential mechanistic links between leukemogenesis and contributors to thrombotic complications.\u003c/p\u003e \u003cp\u003eTo address this gap, this study aimed to perform an integrated genomic and transcriptomic analysis of thrombosis-associated genes in AML using the TCGA cohort. By combining genomic alteration profiling, protein\u0026ndash;protein interaction network analysis, pathway enrichment, and gene expression validation using independent datasets, we sought to elucidate the biological relevance and mechanistic interplay between thrombosis-related pathways and leukemogenic processes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e \u003cp\u003eThis study investigated thrombosis-associated genes in Acute Myeloid Leukemia (AML), genomic and clinical data were obtained from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia cohort through the cBioPortal for Cancer Genomics platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed 06 February 2026). The dataset used was Acute Myeloid Leukemia (TCGA, PanCancer Atlas), which contains data from approximately 200 AML patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] with available genomic alteration data, copy number variation data, mRNA expression profiles and clinical informations. The workflow pattern that guided this study is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSelection of Thrombosis-Associated Genes\u003c/h3\u003e\n\u003cp\u003eA panel of twenty-seven (27) thrombosis-associated genes were curated based on known involvement in coagulation, platelet activation, and fibrinolysis pathways reported via literature and biological pathway databases such as National Center for Biotechnology Information (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ); Kyoto Encyclopedia of Genes and Genomes (KEGG) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The genes analyzed are categorized based on the mechanism of action of the proteins they code for and these are (Supplementary file 1 contains the full gene names by category):\u003c/p\u003e \u003cp\u003eCoagulation cascade genes- F2, F3, F5, F7 F8 F9 F10 F11 F12 F13A1\u003c/p\u003e \u003cp\u003eNatural anticoagulant System- PROC, PROS1, SERPINC1, THBD, PROCR\u003c/p\u003e \u003cp\u003eFibrinolysis - PLAT, PLG, SERPINE1, SERPINF2\u003c/p\u003e \u003cp\u003ePlatelet activation and adhesion genes- ITGA2B, ITGB3, GP1BA, P2RY12\u003c/p\u003e \u003cp\u003eEndothelial-Thrombosis Regulation- VWF, ADAMTS13, SELE, ICAM1\u003c/p\u003e\n\u003ch3\u003eGenomic Alteration Analysis\u003c/h3\u003e\n\u003cp\u003eGenomic alterations in the 27 carefully selected thrombosis-associated genes were analyzed using the OncoPrint tool within cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The generated OncoPrint was used to assess the frequency and distribution of genetic alterations including mutations, copy number variations, and mRNA expression changes across AML samples. The percentage of patients exhibiting alterations in each gene was calculated based on the TCGA-AML dataset.\u003c/p\u003e\n\u003ch3\u003eAML Driver Mutation Visualization\u003c/h3\u003e\n\u003cp\u003eThe genomic landscape of AML driver mutations was analyzed on the cBioPortal for TCGA Acute Myeloid Leukemia (PanCancer Atlas), and 12 commonly mutated genes were identified based on their frequency in the cohort. The genomic alterations including somatic mutations and copy number variations visualized using the OncoPrint and they include FLT3, NPM1, DNMT3A, IDH1, IDH2, RUNX1, TET2, ASXL1, ETV6, BCORL1, SETBP1, and ZRSR2 (refer to Supplementary1 document for the names).\u003c/p\u003e\n\u003ch3\u003eMutation Co-occurrence Analysis\u003c/h3\u003e\n\u003cp\u003eThis study further investigated potential relationships between thrombosis-associated genes and AML driver mutations whereby mutation co-occurrence analysis was performed using the Mutual Exclusivity module in the cBioPortal platform. This analysis evaluates whether gene alterations tend to occur concurrently (co-occurrence) or rarely appear together (mutual exclusivity) across patient samples. Statistical significance was assessed using Fisher\u0026rsquo;s exact test to generate p-value, and the strength of association was represented by the log2 odds ratio. False discovery rate correction was applied to generate q-values for multiple testing.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival analysis was performed using the Comparison/Survival tool in cBioPortal platform. The patients were grouped based on the presence or absence of genomic alterations in the queried genes and overall survival was defined as the time from diagnosis to death or last follow-up available in the dataset. Survival differences between groups were evaluated using the log-rank test, and p-values less than 0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProtein–Protein Interaction Network Analysis\u003c/h3\u003e\n\u003cp\u003eThe functional relationships among the selected genes was further investigated for the protein\u0026ndash;protein interaction (PPI) analysis using the STRING database (version 12.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://version-12-0.string-db.org/\u003c/span\u003e\u003cspan address=\"https://version-12-0.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The selected thrombosis-associated and major AML driver genes set was uploaded to STRING with the organism set to Homo sapiens to generate interaction networks at a minimum interaction confidence score of 0.7 (high confidence). This yielded a visualized network of functional associations between proteins based on experimental evidence, curated databases, co-expression, and text mining. Then, the network topology was analyzed to identify clusters and potential hub proteins within the thrombosis and AML mutation pathways.\u003c/p\u003e\n\u003ch3\u003eFunctional Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eThe functional enrichment analysis was performed using the Enrichr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] to identify specific biological pathways associated with the thrombosis gene panel. Gene lists were analyzed against multiple pathway databases -KEGG Pathway Database, Reactome Pathway Database and Gene Ontology Biological Processes. The platform was used to determine the enrichment significance using adjusted p-values derived from Fisher\u0026rsquo;s exact test with multiple testing correction. Pathways with adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched. Then, the Enrichment results were visualized using bar plots representing the most significantly enriched pathways.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGene Expression Validation using GEPIA\u003c/h2\u003e \u003cp\u003eThe Gene Expression Profiling Interactive Analysis (GEPIA) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to validate the transcriptional expression of thrombosis-associated genes in the Acute Myeloid Leukemia (AML) cohort since it integrates RNA sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database.\u003c/p\u003e \u003cp\u003eThe expression profiles of selected thrombosis-related genes including F2, F3, F10, P2RY12, SERPINE1, PROCR, VWF, ITGA2B, and ITGB3 (based on their expression frequencies) were analyzed across the TCGA AML dataset (LAML) and corresponding normal tissue samples from GTEx. Expression comparisons between tumor and normal samples were performed using the box plot module, gene expression values were normalized and presented as log2 (TPM\u0026thinsp;+\u0026thinsp;1), where TPM represents transcripts per million. Box plots were generated for each gene to visualize expression differences between AML tumor samples and normal tissues. The number of samples included in the analysis was automatically defined by the GEPIA platform based on available TCGA and GTEx data, with approximately 173 AML samples and 70 normal samples included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using tools integrated within cBioPortal and Enrichr. Fisher\u0026rsquo;s exact test was used to evaluate co-occurrence and mutual exclusivity between gene alterations for mutation association analysis. Survival analysis was conducted using Kaplan\u0026ndash;Meier estimation with log-rank testing. Statistical significance between tumor and normal expression levels was evaluated on the GEPIA default statistical framework, which employs analysis of variance (ANOVA) for differential expression analysis. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenomic Landscape of Thrombosis-Associated Genes in AML\u003c/h2\u003e \u003cp\u003eOncoPrint analysis demonstrated that a subset of AML patients exhibited genomic alterations in thrombosis-associated genes, including mRNA expression changes, copy number variations, and occasional mutations. Among the analyzed genes, F2, F3, F10, P2RY12, and SERPINE1 demonstrated relatively higher alteration frequencies compared to other members of the panel and these alterations were distributed across multiple AML samples. Figure\u0026nbsp;2a-e demonstrate the oncoprint analysis of the various hemostasis associated genes, segregated based on their functions, with their genomic alterations in the AML cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGenomic Landscape of AML Driver Mutations\u003c/h2\u003e \u003cp\u003eAnalysis of the TCGA AML dataset revealed recurrent alterations in several key leukemia-associated genes, including FLT3, NPM1, DNMT3A, IDH1, IDH2, RUNX1, and TET2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among these genes, FLT3 (31%) and NPM1 (27%) exhibited the highest mutation frequencies, consistent with previously reported molecular profiles of AML. Mutations in DNMT3A were also commonly observed, while IDH1, IDH2, RUNX1, and TET2 displayed moderate mutation frequencies across the cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Other less frequent but present mutations in the cohort include ASXL1, ETV6, BCORL1, SETBP1, and ZRSR2. These characteristic mutation spectrum of AML genes generated from the cohort provided a genomic alteration basis for subsequent analyses of thrombosis-associated genes within the same patient population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMutation Co-occurrence Analysis with AML Driver Genes\u003c/h2\u003e \u003cp\u003eWhen the thrombosis gene panel was analyzed alongside the assessed common AML driver genes, FLT3, NPM1, DNMT3A, IDH1, IDH2, RUNX1, and TET2; significant co-occurrence patterns were observed among several AML driver genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Categorizing based on co-occurrence with the most significant relationship (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FLT3 and NPM1 mutations demonstrated strong co-occurrence at log2 odds ratio\u0026thinsp;=\u0026thinsp;2.097 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Similarly, NPM1 and DNMT3A mutations showed significant co-occurrence (log2 odds ratio\u0026thinsp;=\u0026thinsp;1.748; p\u0026thinsp;=\u0026thinsp;0.001). Conversely, several AML mutation pairs displayed mutual exclusivity, including FLT3 and RUNX1 (log2 odds ratio\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.726, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and NPM1 and RUNX1 (log2 odds ratio\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.457, p\u0026thinsp;=\u0026thinsp;0.003), all of which are consistent with known genomic relationships among AML driver mutations.\u003c/p\u003e \u003cp\u003eIn contrast, thrombosis-associated genes demonstrated limited statistically significant co-occurrence with AML driver mutations, the SERPINE1 and FLT3 showed insignificant co-occurrence of log2 odds ratio\u0026thinsp;=\u0026thinsp;0.918; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; P2RY12 and RUNX1 at log2 odds ratio\u0026thinsp;=\u0026thinsp;0.272; p\u0026thinsp;\u0026gt;\u0026thinsp;0.001 while F3 and F10 had similar insignificant relationship (p\u0026thinsp;\u0026gt;\u0026thinsp;0.001) at log2 odds ratio\u0026thinsp;=\u0026thinsp;0.051. Also, there was a statistically insignificant mutual exclusivity between the F10 and NPM1 as well as P2RY12 and IDH2 genes (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"609\" height=\"423\"\u003e\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis of Thrombosis-Associated Gene Alterations\u003c/h2\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival analysis demonstrated no statistically significant difference in overall survival between patients with and without alterations in thrombosis-associated genes (log-rank p\u0026thinsp;=\u0026thinsp;0.201) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similar findings were observed when individual genes were evaluated separately, indicating that genomic alterations in thrombosis-related genes were not significantly associated with overall survival in the TCGA AML cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProtein\u0026ndash;Protein Interaction Network Analysis\u003c/h2\u003e \u003cp\u003eThe resulting interaction network revealed the thrombosis and AML driver genes functional clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The first cluster consisted of the thrombosis-associated genes involved in coagulation and platelet activation- F2, F3, F5, F7, F10, VWF, SERPINE1, PLAT, PROC, PROS1, SERPINC1, ITGA2B, ITGB3, and P2RY12- whose proteins formed a highly interconnected network representing key components of the hemostatic system. The second cluster comprised the AML driver genes NPM1, DNMT3A, FLT3, IDH1, IDH2, TET2, RUNX1, ASXL1, BCORL1, SETBP1, and ZRSR2 which formed a dense interaction network associated with leukemogenic signaling and epigenetic regulation.\u003c/p\u003e \u003cp\u003eInterestingly, limited interconnectivity between two clusters was observed between RUNX1 and platelet-associated genes ITGA2B and ITGB3, as well as FLT3 and ITGA2B. Both interconnectivities appeared to form potential bridging interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe KEGG pathway analysis revealed that the most significantly enriched pathways included complement and coagulation cascades, platelet activation, fluid shear stress and atherosclerosis, ECM\u0026ndash;receptor interaction, and integrin signaling pathways. Among these pathways, the complement and coagulation cascade pathway exhibited the strongest enrichment, confirming the central role of the selected genes in thrombin generation and fibrin clot formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe reactome pathway enrichment analysis further highlighted pathways directly involved in coagulation and hemostasis to include formation of fibrin clot (clotting cascade),hemostasis, common pathway of fibrin clot formation, intrinsic pathway of fibrin clot formation and the platelet activation signaling. These pathways collectively represent the sequential processes involved in thrombus formation and stabilization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGene Ontology biological process analysis demonstrated strong enrichment in processes related to coagulation and fibrinolysis, including: positive regulation of blood coagulation, negative regulation of blood coagulation, regulation of fibrinolysis, negative regulation of hemostasis and the regulation of blood coagulation, which indicates that the presence of both pro-coagulant and anticoagulant regulators in the selected panel (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptional Expression Validation of Thrombosis-Associated Genes in AML and Normal Tissues\u003c/h2\u003e \u003cp\u003eAnalysis of the coagulation cascade genes F2, F3, and F10 revealed generally low transcriptional expression in AML samples compared with normal tissues in the GEPIA platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-i). In particular, F2 and F10 demonstrated minimal transcriptional expression across AML samples while F3 (tissue factor) exhibited moderate expression variability across AML samples. Evaluation of genes associated with platelet activation pathways revealed measurable expression levels in AML samples. The platelet receptor genes ITGA2B and ITGB3, which encode the integrin αIIbβ3 complex responsible for platelet aggregation, displayed moderate expression across the AML cohort. Similarly, P2RY12, a key ADP receptor involved in platelet activation, showed detectable expression in a subset of AML samples.\u003c/p\u003e \u003cp\u003eFurthermore, genes involved in endothelial interaction and fibrinolysis regulation also demonstrated variable expression patterns. VWF, which mediates platelet adhesion to vascular endothelium, exhibited moderate transcriptional activity across AML samples. Additionally, SERPINE1, a major inhibitor of fibrinolysis, showed heterogeneous expression among AML patients while, the anticoagulant pathway regulator, PROCR demonstrated moderate expression variability within the AML cohort.\u003c/p\u003e \u003cp\u003eGenerally, the GEPIA expression analysis indicates that while classical coagulation factors such as F2 and F10 exhibit limited transcriptional activity in AML cells, several genes involved in platelet activation, endothelial interaction, and fibrinolysis regulation are actively expressed in AML samples.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMechanistic Model linking AML genomic alterations to thrombosis-associated pathways.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe analyses in this study support a model illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e which explains series of events leading to generation of prothrombotic state in AML. Genomic alterations in AML driver genes disrupt hematopoietic transcriptional programs, particularly through the RUNX1-mediated regulation of megakaryocyte differentiation. This dysregulation may influence platelet activation pathways involving ITGA2B, ITGB3, P2RY12, and VWF. These pathways interact with coagulation cascade components including F3, F10, and F2, leading to thrombin generation and fibrin clot formation. Concurrent regulation of fibrinolysis by SERPINE1 and PROCR may further promote clot stabilization. Together, these molecular processes contribute to a pro-thrombotic microenvironment in AML.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrombotic complications remain a significant clinical challenge in the management of acute myeloid leukemia (AML), often contributing to increased morbidity and complexity of treatment. Despite advances in leukemia care, there is still limited mechanistic understanding linking thromboembolic events directly to the underlying pathology of AML. In many cases, thrombotic manifestations are detected at advanced stages of the disease, thereby complicating therapeutic decision-making and frequently necessitating reliance on empirical clinical judgment. This gap in mechanistic insight hinders the development of precise, evidence-based strategies and could pose a barrier to the realization of the needed personalized medicine in AML care. Consequently, there is a critical need to identify robust predictive biomarkers and molecular determinants that can guide clinicians in the early detection and management of thrombotic risk in AML patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eFindings from this study demonstrated that thrombosis-associated genes exhibit measurable genomic alterations in a subset of AML patients, including mRNA expression changes and copy number variations. Although the alteration frequency of individual genes were modest, their collective presence suggests that dysregulation of hemostatic pathways may occur alongside leukemogenic processes. Importantly, these alterations may not function as primary drivers of thrombotic complications but rather act as modulatory factors influencing the thrombotic phenotype in AML.\u003c/p\u003e \u003cp\u003eThis is because reports have indicated that cancer-associated thrombosis (CAT) arises not solely from genetic mutations but also from complex interactions involving gene expression changes and tumor\u0026ndash;host dynamics [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Indeed, non-specific factors such as the tumor microenvironment and chemotherapy have been implicated in the pathogenesis of CAT. Inflammatory cytokines such as interleukin-6 (IL-6), interleukin-1 (IL-1), and tumor necrosis factor-alpha (TNF-α) promote a procoagulant state by stimulating tissue factor expression on monocytes and endothelial cells while suppressing thrombomodulin activity. In addition, tumor-derived microvesicles containing podoplanin (PDPN) can directly activate factor X or platelets, thereby enhancing thrombus formation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Chemotherapeutic agents, including cisplatin and anthracyclines, further contribute to this process by inducing endothelial injury, exposing the subendothelial matrix, triggering apoptosis, and releasing prothrombotic particles from both malignant and non-malignant cells. Moreover, these agents can increase tissue factor expression, reduce natural anticoagulants such as protein C, protein S, and antithrombin, activate platelets, and elevate circulating clotting factor levels, collectively promoting a hypercoagulable state [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, oncogenic pathways driven by genetic mutations or epigenetic modifications, such as DNA methylation, may influence the procoagulant properties of cancer cells, thereby modulating the risk of thromboembolic events. Consequently, the diversity of oncogenic drivers and their downstream effector mechanisms may contribute to the heterogeneity observed in CAT across different cancer types, molecular subtypes, and individual patients, resulting in either thrombosis-promoting or protective effects [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genomic landscape of AML driver mutations observed in this study, including frequent alterations in FLT3, NPM1, and DNMT3A, aligns with established molecular classifications of AML, and this forms the basis of targeted therapy in the patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] .The co-occurrence analysis further confirmed known relationships among AML driver genes, such as the strong co-occurrence between FLT3 and NPM1 and the mutual exclusivity between FLT3 and RUNX1 as observed by Nong \u003cem\u003eet al\u003c/em\u003e.(2024) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Importantly, this analysis revealed that thrombosis-associated genes demonstrated limited statistically significant co-occurrence with AML driver mutations, suggesting that these pathways may operate largely independently of the core leukemogenic mutation network. This supports the model that thrombosis in AML arises from a complex interplay of leukemic biology, microenvironmental interactions, and systemic factors rather than direct canonical driver mutations. This dissociation may be explained by the multifactorial nature of thrombotic complications in AML, which are driven by interactions between leukemic cells and the vascular microenvironment, including tissue factor expression, release of procoagulant microparticles, inflammatory signaling, and endothelial activation, as well as the effects of therapeutic interventions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProtein\u0026ndash;protein interaction network analysis provided further insight into the functional organization of these gene sets. Two major clusters were identified: one comprising thrombosis-associated genes involved in coagulation, platelet activation, and fibrinolysis, and another consisting of AML driver genes associated with transcriptional regulation and epigenetic control. Notably, limited interconnectivity between these clusters was observed, with RUNX1 (Runt-Related Transcription Factor 1) and FLT3 (Fms-like tyrosine kinase 3 internal tandem duplication) of the AML, and platelet-related genes ITGA2B and ITGB3 forming potential bridging nodes. Given the well-established role of RUNX1 in megakaryocyte differentiation and platelet biology, this finding suggests a plausible mechanistic link between leukemogenic transcriptional dysregulation and thrombosis-related pathways. And this could be because transcriptional and epigenetic dysregulation associated with AML driver mutations, particularly involving factors such as RUNX1, may indirectly influence platelet-related and hemostatic pathways by impairing megakaryocyte differentiation, maturation, and platelet function [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eIn addition, the interaction between FLT3 and ITGA2B in AML is a complex mechanism recently elaborated more to contribute to hypercoagulable states and thrombotic complications [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. FLT3-ITD, a common mutation causing constitutive signaling has been seen to promote an aberrant, immature megakaryocytic phenotype in AML blasts, leading to increased adhesion and activation of platelets and megakaryocytes through ITGA2B [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], thus enhancing thrombogenicity\u003c/p\u003e \u003cp\u003eThe functional enrichment analysis reinforced the biological relevance of the thrombosis gene panel. KEGG and Reactome pathway analyses identified significant enrichment in complement and coagulation cascades, platelet activation, and fibrin clot formation pathways, while Gene Ontology analysis highlighted processes such as regulation of blood coagulation and fibrinolysis. These results confirm that the selected genes are functionally coherent and collectively participate in key pathways governing thrombus formation and hemostasis. Notably, the enrichment of both pro-coagulant and anticoagulant processes reflects the complex regulatory balance of the hemostatic system.\u003c/p\u003e \u003cp\u003eGene expression validation using the GEPIA platform provided additional insight into the transcriptional activity of these genes in AML. Classical coagulation factors such as F2 and F10 exhibited low expression levels in AML samples which is consistent with their primary synthesis in hepatic tissues. In contrast, genes involved in platelet activation (ITGA2B, ITGB3, P2RY12), endothelial interaction (VWF), and fibrinolysis regulation (SERPINE1, PROCR) demonstrated measurable and variable expression across AML samples. These findings suggest that thrombosis in AML may be driven not by direct expression of coagulation factors within leukemic cells, but rather through interactions of the cells in platelet activation, endothelial signaling, and regulation of fibrinolysis within the tumor microenvironment. This stems from studies demonstrating that leukemic cells have capacity to influence surrounding vascular and hematopoietic components by promoting platelet activation pathways, enhancing endothelial dysfunction, and altering the balance between procoagulant and anticoagulant systems, thereby creating a pro-thrombotic milieu [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, this iterates the importance of tumor\u0026ndash;microenvironment interactions in mediating thrombotic risk in AML and stresses that thromboembolic complications are likely a consequence of coordinated cellular and molecular crosstalk except for the link of specific driver genes, RUNX and FLT3, with platelet differentiation\u003c/p\u003e \u003cp\u003eBased on these observations, we propose a mechanistic model in which AML driver mutations disrupt hematopoietic transcriptional programs, particularly through RUNX1-mediated regulation of megakaryocyte differentiation. This dysregulation may influence platelet activation pathways, which in turn interact with coagulation cascade components and fibrinolysis regulators, ultimately promoting a pro-thrombotic microenvironment. This provides a biologically probable framework linking leukemogenesis with thrombosis-associated pathways via a mechanistic model as similarly described by Szarpak \u003cem\u003eet al.\u003c/em\u003e, (2026) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these findings, some limitations are acknowledged in this study. Firstly, it is based on retrospective bioinformatics analysis of publicly available datasets, and therefore lacks experimental validation. Secondly, the TCGA AML cohort has limited normal control samples, necessitating the use of GTEx data for comparison in expression analyses. Thirdly, the study does not include clinical variables such as treatment regimens or comorbidities that may influence thrombosis risk. Future studies incorporating experimental validation, larger cohorts, and clinical correlation are needed to further elucidate the mechanisms underlying thrombosis in AML.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrates that thrombosis-associated genes in AML form a functionally enriched network involved in coagulation, platelet activation, and fibrinolysis pathways. While these genes appear to operate largely independently of canonical AML driver mutations, potential interactions through transcriptional regulators, RUNX1 and FLT3, suggest a link between leukemogenesis and thrombosis-related processes. Therefore, future studies incorporating experimental validation and external cohorts are required for information. Also, these findings provide new insights into the molecular basis of thrombosis in AML, therefore, informed future studies aimed at identifying biomarkers or therapeutic targets for managing thrombotic complications in leukemia patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the GENOMAC OMICS Hub for their technical expertise in the course of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data created or examined in this study are included in this published article and its supplementary information files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOEB conceptualized and designed the study, coordinated the collaboration, drafted the initial version of the manuscript and performed the statistical analyses. OEB, ABA and DBA were involved in data validation and acquisition, interpreted analyzed data. OEB and LS contributed to clinical interpretation of the technicalities and biological relevance of the study. ABA and DBA contributed to critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe hereby disclose no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are publicly available and their links included in this published article [and its supplementary information files].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTsantes AG, Petrou E, Tsante KA, Sokou R, Frantzeskaki F, Domouchtsidou A, et al. 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Thromb J. 2025;23(1):35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOppelt P, Betbadal A, Nayak L. Approach to chemotherapy-associated thrombosis. Vasc Med. 2015;20(2):153\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirwan CC, McCollum CN, McDowell G, Byrne GJ. Chemotherapy-induced venous thromboembolism: endothelial activation and procoagulant release. Clin Appl Thromb Hemost. 2015;21(5):420\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki K, Imamura A, Funatsu H. Bilateral adrenal infarction during chemotherapy for lymphoma: a case report. Radiol Case Rep. 2026;21(5):2199\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe C, Dai X, Feng D, Zhou Q, Liu W, Xu Y, et al. Cancer-associated thrombosis in precision oncology: mechanisms, challenges and future directions. Crit Rev Oncol Hematol. 2026;219:105151.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiNardo CD, Cortes JE. Mutations in AML: prognostic and therapeutic implications. Hematol Am Soc Hematol Educ Program. 2016;2016(1):348\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaterno G, Palmieri R, Forte V, Del Prete V, Gurnari C, Guarnera L, et al. Predictors of early thrombotic events in AML patients. Cancers (Basel). 2022;14(22):5640.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgan NV, Daly ME. RUNX1 mutations and inherited bleeding. Platelets. 2017;28(2):208\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYokota A, Huo L, Lan F, Wu J, Huang G. RUNX1 mutations in hematological malignancies. Mol Cells. 2020;43(2):145\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarbonell D, Chicano M, Cardero AJ, G\u0026oacute;mez-Centuri\u0026oacute;n I, Bail\u0026eacute;n R, Oarbeascoa G, et al. FLT3-ITD as a biomarker in AML. Cancers (Basel). 2022;14(16):4006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolina-Ortiz P, Polizzi S, Ramery E, Gayral S, Delierneux C, Oury C, et al. Rasa3 regulates megakaryocyte differentiation. PLoS Genet. 2014;10(6):e1004420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFodil S, Arnaud M, Vaganay C, Puissant A, Lengline E, Mooney N, et al. Endothelial cells in acute myeloid leukemia. Blood Rev. 2022;54:100932.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu C, Lu T, Lv X, Cheng T, Cheng H. Bone marrow vascular niche in AML. Blood Sci. 2023;5(2):92\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzarpak L, Jach ME, Skoczylas M, Radej S, Pruc M. 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Int J Mol Sci. 2026;27(6):2750.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"[email protected]","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":"Acute Myeloid Leukemia, Cancer-Associated Thrombosis, TCGA, Gene Expression, AML-Thrombosis Genomics","lastPublishedDoi":"10.21203/rs.3.rs-9241751/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9241751/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nThromboembolic complications represent a significant clinical challenge in acute myeloid leukemia (AML), contributing to morbidity and complicating disease management. Although cancer-associated thrombosis (CAT) is well established,the molecular mechanisms linking thrombosis-associated pathways with AML biology has limited information and poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study aimed to investigate the genomic and transcriptomic landscape of thrombosis-associated genes in AML and their potential biological interaction with leukemogenic pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nGenomic and clinical data from The Cancer Genomic Atlas (TCGA) AML cohort of 200 patients were analyzed using cBioPortal. A panel of 27 thrombosis-associated genes involved in coagulation, platelet activation, endothelial regulation, and fibrinolysis was identified and selected. Genomic alterations were assessed and mutation co-occurrence with AML driver genes was evaluated using OncoPrint. Protein–protein interaction (PPI) networks were constructed with STRING, and functional enrichment analysis was performed by Enrichr. Gene expression validation was conducted using GEPIA, integrating TCGA and GTEx datasets. Survival analysis was performed using Kaplan–Meier estimation and p-value\u0026lt; 0.05 was statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nThrombosis-associated genes demonstrated measurable genomic alterations, including mRNA expression changes and copy number variations majorly in genes F2,F3,F10,P2RY12,and SERPINE1. However, these genes showed limited statistically significant co-occurrence with canonical AML driver mutations with SERPINE1 and FLT3 (log2 odds ratio =0.918;p \u0026gt;0.05) and P2RY12 and RUNX1 (log2 odds ratio =0.272;p\u0026gt;0.001).The PPI network analysis revealed two distinct clusters corresponding to thrombosis-related and leukemogenic genes, with limited bridging interactions involving RUNX1,FLT3 and platelet-associated genes ITGA2B,ITGB3 and ITGA2B respectively. Functional enrichment analysis identified significant pathways related to complement and coagulation cascades, platelet activation, and fibrin clot formation. The gene expression analysis revealed low expression of classical coagulation factors (F2,F10) and moderate expression of platelet activation, endothelial interaction, and fibrinolysis-related genes. Survival analysis showed no significant association between thrombosis gene alterations and overall survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cbr\u003e\nThrombosis-associated genes in AML form a functionally coherent network that operates largely independently of canonical driver mutations but may contribute to a pro-thrombotic microenvironment initiation through platelet activation, endothelial signaling, and fibrinolysis regulation. These findings showed the importance of tumor–microenvironment interactions in AML-associated thrombosis and provide a basis for future studies exploring predictive biomarkers and therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Integrated Genomic and Transcriptomic Analysis of Thrombosis-Associated Pathways in Acute Myeloid Leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 07:23:59","doi":"10.21203/rs.3.rs-9241751/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"ea8b6ad7-45f6-4a48-b440-5eede9cc6d9b","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T07:25:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 07:23:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9241751","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9241751","identity":"rs-9241751","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}