Spliceosome gene mutations synergize with anthracyclines to amplify atrial fibrillation risk in hematologic malignancies: a multicenter cohort study with discovery and validation

Spliceosome gene mutations synergize with anthracyclines to amplify atrial fibrillation risk in hematologic malignancies: a multicenter cohort study with discovery and validation | 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 Spliceosome gene mutations synergize with anthracyclines to amplify atrial fibrillation risk in hematologic malignancies: a multicenter cohort study with discovery and validation guangshuai teng, Yuan Zhou, Yuhui Zhang, Ke Shang, Yanjie Lan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8596634/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background Atrial fibrillation (AF) is a life-threatening complication in patients with hematologic malignancies, yet personalized risk stratification for this condition remains highly challenging. Spliceosome gene mutations are key drivers of the initiation and progression of hematologic malignancies. However, their specific role in the pathogenesis of AF, as well as the potential synergistic effects with core chemotherapeutic agents such as anthracyclines, remains largely unclear. Methods This retrospective cohort included a discovery cohort (n = 1321) and a validation cohort (n = 412) of hematologic malignancy patients (male, 53.9%; age, 62 years). Firth penalized Cox, competing risk models, and FDR correction assessed associations between CHIP-related mutations (DTA, spliceosome, DNA damage repair genes) and AF. Sensitivity analyses addressed immortal time bias and mutational burden thresholds. Interaction analysis evaluated spliceosome mutation-anthracycline synergy. Mechanistic validation involved co-culturing human monocytes with atrial fibroblasts and Western Blot (WB) to detect fibrosis-related proteins. Results Spliceosome gene mutations were significantly associated with increased AF risk in the discovery cohort (adjusted HR = 1.59, 95% CI: 1.05–2.42, P = 0.030), a finding robust to sensitivity analyses and confirmed in the validation cohort (HR = 2.71, 95% CI: 1.24–5.95, P = 0.013). A significant synergistic interaction between spliceosome mutations and anthracycline exposure was observed in both the discovery cohort (sHR = 2.99, 95% CI: 1.21–7.40, P = 0.018) and the validation cohort (sHR = 3.78, 95% CI: 1.09–13.14, P = 0.036). Patients with spliceosome mutations exhibited left atrial enlargement (mediation effect: 26.6%), elevated monocyte counts, and higher levels of IL-6 and TNF-α. In vitro , monocytes from mutation carriers promoted collagen I expression in human atrial fibroblasts. Conclusions Spliceosome gene mutations are a novel, independent predictor of AF in hematologic malignancies and synergistically amplify anthracycline-related AF risk. Myeloid-driven inflammation and atrial structural remodeling appear to be key mediating mechanisms. These findings advocate for enhanced AF surveillance in spliceosome-mutated patients receiving anthracyclines and highlight inflammation as a potential therapeutic target for cardio-protection. hematologic malignancy atrial fibrillation Spliceosome gene mutation anthracycline inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Atrial fibrillation (AF) is a highly prevalent and life-threatening complication in patients with hematologic malignancies, significantly increasing the risks of stroke, major bleeding [ 1 , 2 ] , and all-cause mortality, and often leading to the interruption of critical chemotherapy. Although clonal hematopoiesis (CH)-related spliceosome gene mutations (such as SF3B1 , SRSF2 , U2AF1 , and ZRSR2 ) have been identified as core mechanisms driving clonal evolution and disease progression in hematologic malignancies [ 3 ] , the role of these hematopoietic system-specific somatic mutations in mediating cardiovascular complications [ 4 ] , particularly AF, remains to be thoroughly investigated. This knowledge gap is crucial in the field of hematologic malignancies, as emerging evidence suggests that CH mutations can drive inflammatory atrial remodeling through abnormal activation of myeloid cells and cytokine dysregulation [ 5 ] , a pathway that may synergize with the cardiotoxicity of anthracyclines, the cornerstone of chemotherapy for hematologic malignancies. Anthracyclines themselves have been clearly and independently associated with cardiotoxicity and AF risk [ 6 , 7 ] , but whether spliceosome mutations significantly amplify this treatment-related cardiac risk unique to patients with hematologic malignancies remains unknown. Preclinical models further demonstrate that spliceosome defects can disrupt RNA splicing programs in cardiac stromal cells, potentially activating profibrotic signaling pathways [ 8 ] , providing a potential mechanism for CH-related cardiovascular damage. However, existing studies have not integrated patient-specific somatic mutation profiles, key treatment exposure histories (especially anthracyclines), and dynamic changes in inflammatory mediators in the hematologic malignancy population to systematically elucidate the unique pathogenic characteristics of AF. Focusing on the core clinical scenario of hematologic malignancies, this study aims to evaluate the association between key spliceosome gene mutations that drive the development of hematologic malignancies and the pathogenesis of AF. It will specifically investigate the interaction between these hematopoietic clone-related mutations and anthracycline exposure on AF risk. By clarifying the AF susceptibility of carriers of specific mutations and their synergistic effects with core chemotherapeutic agents, this study will provide direct evidence for developing individualized monitoring strategies for cardiovascular complications in patients with hematologic malignancies, and lay the foundation for identifying high-risk subgroups and developing targeted cardiovascular protective interventions. Methods Study population This study adopts a retrospective cohort design. The discovery cohort includes 1,321 patients with hematologic malignancies who were newly diagnosed and treated in the Department of Hematology, Second Hospital of Tianjin Medical University from November 1, 2017 to September 1, 2024. An independent validation cohort consists of 412 patients with hematologic malignancies who were newly diagnosed and treated in the Department of Hematology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College from January 1, 2015 to December 31, 2022. Hematologic malignancies include: Chronic myeloid leukemia (CML), philadelphia-negative myeloproliferative neoplasms (Ph-negative MPNs), acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), myelodysplastic syndrome/myeloproliferative neoplasm overlap syndromes (MDS/MPN overlap syndromes), and lymphoid malignancies. The diagnosis of all patients is strictly based on the 2022 diagnostic classification criteria of the World Health Organization [ 9 , 10 ] , and the clinical and laboratory data are independently reviewed and confirmed by at least three senior hematologists. The study baseline is defined as the date of the first diagnosis of hematologic malignancies. Outcomes The diagnosis of AF was established based on codes from the International Classification of Diseases, text searches in patients' electronic medical records, and confirmed by electrocardiogram (ECG) data or Holter monitoring. Cases of AF associated with surgery, valvular heart disease, or cardiomyopathy, as well as patients with a prior diagnosis of AF, were excluded. Information on all patients was verified through outpatient follow-up and telephone follow-up. AF-free survival was defined as the time from the diagnosis of MPN to the occurrence of AF or the last follow-up. Statistical analysis In this study, only genetic variants with a variant allele frequency (VAF) ≥ 2% and classified as pathogenic or likely pathogenic were included. We analyzed the following mutations: DTA ( DNMT3A , TET2 , ASXL1 ), spliceosome genes ( U2AF1 , SF3B1 , ZRSR2 , SRSF2 ), and DNA damage repair genes ( PPM1D , TP53 ). These genes were selected as they represent common mutation types in clonal hematopoiesis of CHIP [ 4 ] and hematological malignancies. Termed "CHIP-related gene mutations" due to their commonality in CHIP, the presence of these variants in patients does not indicate concurrent CHIP, as CHIP is defined as a distinct entity within the clonal hematopoiesis spectrum and cannot coexist with hematological malignancies. The incidence rate of events during follow-up was calculated per 1,000 person-years. To assess associations between gene mutations and outcomes, we used multivariable Cox proportional hazards models. To address potential small-sample bias and sparse event limitations, Firth penalized likelihood Cox models were applied for robust estimation of hazard ratios (HRs) and confidence intervals (CIs). Additionally, competing risk regression (Fine-Gray subdistribution hazard model) was used to account for mortality as a competing event, calculating subdistribution sHRs. All models in the entire cohort were adjusted for covariates including age, sex, hypertension, hyperlipidemia, diabetes, smoking status, and anthracycline exposure. Sensitivity analysis was conducted in the following three ways to verify model stability and adjust for biases: Excluding patients with a variant allele frequency (VAF) < 10%; Excluding AF events that occurred within 1 year after the diagnosis of hematologic malignancies, so as to address immortal time bias; Minimizing confounding bias and selection bias through the propensity score matching (PSM) method. This study adopted a 1:1 nearest-neighbor matching algorithm for PSM, with a caliper width set to 0.01 standard deviations of the propensity score. Propensity scores were estimated using a logistic regression model. The baseline covariates included in the PSM1 model were: age, sex, hypertension, hyperlipidemia, diabetes mellitus, and smoking status. The PSM2 model included all covariates from PSM1, plus acute coronary syndrome and anthracycline exposure. To control for the impact of potential confounding factors on study outcomes, this study performed multiple corrections using the false discovery rate (FDR) method, incorporating variables such as sex, age, and anthracycline exposure history as covariates into the model for adjustment. To assess the synergy between spliceosome mutations and anthracyclines, we performed stratified competing risk analysis: patients were grouped by mutation status and anthracycline exposure, with Gray’s test used to compare cumulative AF incidence. Additionally, we conducted interaction analysis via the Fine-Gray model, incorporating a multiplicative interaction term (mutation × anthracycline) and adjusting for age and sex. Mediation analysis tested whether larger left atrial diameter (LAD) mediates the spliceosome-AF association, with bootstrap resampling (1000 replicates) to estimate direct and indirect effects. Multivariate models and mediation analysis were performed using R statistical software (version 4.3.1), with key packages including survival (for Cox/Fine-Gray models), coxphf (for Firth correction), and mediation (for mediation analysis). Continuous variables are presented as median (range) and compared using the Mann-Whitney U test; categorical variables were analyzed with the chi-square test or Fisher’s exact test. Multiple linear regression was used to analyze the correlation between cytokine levels and monocyte counts. The Kaplan-Meier method was used for cumulative incidence analysis. A two-tailed P < 0.05 was considered significant. Analyses were performed using R v4.3.1 and SPSS v24.0. The detailed methods for next-generation sequencing (NGS) and cytokine level detection are provided in the Supplementary Appendix. Results Baseline Characteristics The study cohort comprised 1733 patients with hematologic malignancies with a median age of 62 years (range: 3–92). Among them, 934 (53.9%) were male, and 250 (14.4%) received anthracycline therapy. NGS was performed in 1586 patients, revealing CHIP-related mutations in whom 653 (41.2%) and spliceosome gene mutations in whom 243 (15.3%). AF developed in 164 patients (9.5%), with cumulative incidence rates of 5.9%, 11.2%, 16.0%, and 31.3% at 1, 5, 10, and 20 years of follow-up, respectively (Supplementary Table S1 and Supplementary Figure S1 ) . During follow-up, 339 patients (19.6%) died ( Fig. 1 ) . Based on CHIP status, patients in the entire cohort were stratified into CHIP (n = 653) and no-CHIP (n = 933) groups. CHIP carriers had higher proportions of older individuals, males, hypertension, diabetes, smoking history, AF, and mortality compared to no-CHIP carriers ( P < 0.05). Additionally, CHIP carriers had significantly lower hemoglobin levels ( P < 0.05) ( Table 1 ) . The proportions of patients with CHIP across different disease types are shown in Supplementary Figure S1 B . Table 1 Baseline characteristics of hematologic malignancy patients stratified by CHIP-related gene mutation status CHIP (n = 653) no-CHIP (n = 933) P -value Follow-up time, y media (range) 3 (1–26) 4 (1–29) 0.033* Age, media (range) 65 (10–92) 59 (3–88) < 0.001*** Age, years 70 197 (30.2%) 148 (15.9%) Male, n (%) 376 (57.6%) 479 (51.3%) 0.016* Diagnosis Ph − MPN (n = 495), n (%) 183 (28.0%) 312 (33.4%) CML (n = 44), n (%) 12 (1.8%) 32 (3.4%) AML (n = 283), n (%) 133 (20.4%) 150 (16.1%) MDS (n = 386), n (%) 196 (30.0%) 190 (20.4%) MDS/MPN (n = 40), n (%) 27 (4.1%) 13 (1.4%) Lymphoid Malignancy (n = 338), n (%) 102 (15.6%) 236 (25.3%) At the time of diagnosis HB, g/L media (range) 92.5 (26–237) 115 (21–261) < 0.001*** WBC, ×10 9 /L media (range) 5.92 (0.01–498.7) 6.99 (0.01–300) 0.651 Monocytes, ×10 9 /L media (range) 0.37 (0.01–21.56) 0.35 (0.01–75.8) 0.596 PLT, ×10 9 /L media (range) 158 (1-1559) 203 (6-2433) 0.051 Anthracycline drug exposure, n (%) 106 (16.2%) 138 (14.8%) 0.438 Hypertension, n (%) 235 (36.0%) 273(29.3%) 0.005** Hyperlipidemia, n (%) 39 (6.0%) 59 (6.3%) 0.832 Diabetes, n (%) 114 (17.5%) 128 (13.7%) 0.047* Smoking 141 (21.6%) 150 (16.1%) 0.006** LVEF, % media (range) 62.0 (24–74) 63.0 (17–74) 0.050 LAD, mm media (range) 38.8 (23.7–62.8) 37.1 (23.0-61.3) < 0.001*** LVEDD, mm media (range) 47.3 (36.6–73.6) 45.8 (36.3–63.5) < 0.001*** AF, n (%) 94 (14.4%) 62 (6.6%) < 0.001*** Death, n (%) 176 (27%) 136 (14.6%) < 0.001*** CHIP, Clonal hematopoiesis of indeterminate potential; CHIP refers to mutations in DTA, spliceosome, or DNA damage repair-related genes. Ph − MPN, Philadelphia negative myeloproliferative neoplasm; CML, chronic myeloid leukemia; AML, acute myeloid leukemia; MDS, myelodysplastic syndrome; HB, hemoglobin; WBC, white blood cell; PLT, platelet count; LVEF, left ventricular ejection fraction; LAD, left atrial diameter; LVEDD, left ventricular end-diastolic diameter; AF, atrial fibrillation. * P < 0.05; ** P < 0.01; *** P < 0.001. Association of spliceosome gene mutations with AF in hematologic malignancies In the discovery cohort, multivariable Cox models demonstrated a significant association between spliceosome gene mutations and AF (HR = 1.59, 95% CI: 1.05–2.42, P = 0.030) ( Fig. 2 A ) . When death was accounted for as a competing event in competing‑risk regression analyses, spliceosome gene mutations remained associated with AF (sHR = 1.51, 95% CI: 1.01–2.25, P = 0.048) ( Fig. 2 B ) . After adjusting for confounding factors via propensity score matching (PSM) analysis (Supplementary Table S2) , the association between spliceosome gene mutations and AF remained statistically significant (PSM1: HR = 1.801, 95% CI: 1.032–3.144, P = 0.038; PSM2: HR = 1.811, 95% CI: 1.037–3.162, P = 0.037) ( Fig. 2 C-D ) , which further verified the robustness of this association. Sensitivity analyses revealed that the association between spliceosome gene mutations and AF remained statistically significant after excluding patients with variant allele frequency (VAF) < 10% or those who developed AF within 1 year of diagnosis ( P < 0.05) (Figure S2A-B) . In the validation cohort, Traditional Cox models and Firth penalized likelihood Cox models indicated a significant association between spliceosome gene mutations and AF (Traditional Cox: HR = 2.71, 95% CI: 1.24–5.95, P = 0.013), Firth-corrected Cox: sHR = 2.58, 95% CI: 1.18–5.58, P = 0.018), and consistent associations were retained in competing risk analyses (sHR = 2.36, 95% CI: 1.10–5.05, P = 0.027) ( Fig. 3 A-C ) . After adjusting for covariates using FDR multiple corrections, spliceosome gene mutations still showed a significant association with AF (Firth-corrected Cox q value = 0.036; Fine-Gray q value = 0.045). Synergistic effects of spliceosome gene mutations and anthracycline exposure on AF risk In the discovery cohort, competing risk regression models adjusted for age and sex showed that patients with both spliceosome gene mutations and anthracycline exposure had a significantly higher cumulative incidence of AF than those with only spliceosome gene mutations, only anthracycline exposure, or neither ( P < 0.001) ( Fig. 4 A ) . The multiplicative interaction term (spliceosome gene mutation × anthracycline exposure) exhibited a synergistic effect on AF risk (sHR = 2.99, 95% CI 1.21–7.40, P = 0.018) ( Fig. 4 B ) . In the validation cohort, competing risk regression models also revealed a significantly increased cumulative incidence of AF in patients with both spliceosome gene mutations and anthracycline exposure ( P < 0.001) ( Fig. 4 C ) , and the multiplicative interaction term (spliceosome gene mutation × anthracycline exposure) similarly showed a synergistic effect on AF risk (sHR = 3.78, 95% CI 1.09–13.14, P = 0.036) ( Fig. 4 D ) . Exploratory Analysis: Mediating Roles of Inflammation and Atrial Remodeling in the Association Between Spliceosome Gene Mutations and AF Risk To investigate the potential mechanism underlying the association between spliceosome gene mutations and AF risk, we analyzed the echocardiographic findings of patients with hematologic malignancies. The results showed that patients with spliceosome gene mutations had larger left atrial diameter (LAD) and left ventricular end-diastolic diameter (LVEDD) compared to those without spliceosome gene mutations ( P < 0.05), with no significant difference in left ventricular ejection fraction (LVEF) ( Fig. 5 A and Figure S3A-B) . Compared with non-AF individuals, AF patients exhibited increased LAD and decreased LVEF ( P < 0.05), while there was no significant difference in LVEDD ( Fig. 5 B and Figure S3C-D) . An exploratory mediation model ( Fig. 5 C and Supplementary Table S3) indicated that left atrial enlargement might partially mediate the association between spliceosome gene mutations and AF (mediation effect: 26.6%, P = 0.006). Further analyses revealed that compared with patients negative for spliceosome gene mutations, those positive for spliceosome gene mutations had significantly elevated absolute monocyte counts, C-reactive protein (CRP) levels, and levels of IL-6, IL-17A, and IL-17F ( P < 0.05) ( Fig. 5 D-F ) . Multiple linear regression analysis revealed that monocyte counts were significantly positively correlated with levels of TNF-α, IL-6, and IL-12, and significantly negatively correlated with IL-10 levels ( P < 0.05) ( Fig. 5 G ) . These findings suggest that inflammatory activation and structural remodeling may interact in the pathogenesis of spliceosome gene-related AF. To verify the role of monocytes in atrial remodeling, we isolated peripheral blood monocytes from patients with hematologic malignancies, including both those with spliceosome mutations and those without. In a further co-culture experiment involving monocytes and human atrial fibroblasts, the protein expression level of collagen 1 in atrial fibroblasts were significantly higher in the spliceosome mutation group than in the non-spliceosome mutation group ( Fig. 5 H-I ) . These findings suggest that monocyte-mediated inflammation is involved in the activation of atrial fibroblasts. Discussion In this study of patients with hematologic malignancies, both the discovery and validation cohorts demonstrated that spliceosome gene mutations significantly increased the risk of AF. Spliceosome gene mutations amplified the promoting effect of anthracycline exposure on AF. Elevated monocyte counts and increased LAD partially mediated the association between spliceosome gene mutations and AF. As CHIP has been established as a novel risk factor for cardiovascular diseases [ 4 , 11 ] , recent studies have shown that CHIP, especially mutations in the DTA ( DNMT3A , TET2 , ASXL1 ) genes, significantly increases the risk of arrhythmias in healthy populations [ 4 , 12 ] . Based on this, the present study focused on patients with hematologic malignancies to investigate the association between somatic mutations and AF in this high-risk population. For the first time, we identified a significant association between spliceosome gene mutations, which are a subset of CHIP-related mutations, and AF. This conclusion remained robust after rigorous adjustment for competing risks and sensitivity analyses, thus confirming that spliceosome gene mutations represent a novel and non-traditional risk factor for AF in patients with hematologic malignancies. Anthracyclines are well-established to induce myocardial injury, heart failure, and arrhythmias [ 13 – 17 ] . Our analysis revealed that spliceosome gene mutations enhanced the risk of anthracycline-associated AF in patients with hematologic malignancies, suggesting a gene-treatment synergistic interaction. This observation is consistent with recent reports in acute myeloid leukemia (AML) [ 18 ] , where clonal hematopoiesis exacerbated anthracycline-driven cardiotoxicity [ 19 ] , highlighting the need to integrate somatic genomic profiling into cardio-oncology risk stratification. The mechanism of AF primarily involves atrial structural remodeling and electrical remodeling. The present study demonstrated that patients with spliceosome mutations had a significantly LA than those without spliceosome mutations. The mediating effect of LA enlargement on the association between spliceosome gene mutations and AF reached 26.6%, suggesting that structural remodeling plays a crucial role in this process. Recent studies have also indicated that spliceosome dysfunction may impair myocardial cell contraction and lead to cardiac dysfunction [ 8 ] , which supports the hypothesis that spliceosome mutations may directly disrupt the electrophysiological stability of the heart through aberrant splicing. This study suggests that spliceosome gene mutations may promote AF through monocyte-mediated inflammatory pathways. Experiments confirmed that monocytes carrying mutations can directly induce collagen deposition in atrial fibroblasts by upregulating specific proinflammatory cytokines such as IL-6 and TNF-α, thereby forming a unique inflammatory signature. This signature differs from the widespread inflammation mediated by CRP and is similar to the mechanism observed in the CHIP mouse model [ 5 , 20 ] , providing direct experimental evidence for spliceosome mutation-driven inflammatory atrial remodeling. These findings are highly consistent with the current view that CHIP affects the cardiovascular system through multiple mechanisms including immune dysregulation and epigenetic reprogramming [ 21 , 22 ] . We thus hypothesize that in patients with spliceosome mutations, myeloid-driven inflammation and underlying cardiac-specific splicing defects may act synergistically to collectively increase AF susceptibility. Although this study reveals a new clinical perspective, its precise mechanism, particularly the respective contributions of hematopoietic clonality and intrinsic cardiac splicing defects, remains to be clarified by subsequent functional experiments. Study limitations First, exploratory cytokine profiling indicates an IL-6/TNF-α-driven inflammatory axis, but these analyses were limited and lack temporal resolution, making dynamic relationships unclear. Second, our inflammatory/structural analyses are exploratory and require further mechanistic validation in animal models. Methodologically, the retrospective design, small sample size of rare mutation subgroups, and potential unmeasured confounders (e.g., subclinical cardiac dysfunction, lifestyle factors) suggest caution in interpretation. Conclusion Our study identifies spliceosome gene mutations as a novel independent predictor of AF in patients with hematologic malignancies. Monocyte expansion and enlargement of the LAD partially mediate this association, while the synergistic effects of spliceosome mutations and anthracycline exposure further increase AF susceptibility, suggesting a gene-treatment interaction in arrhythmogenesis. These findings support enhanced AF monitoring in spliceosome-mutated patients receiving anthracyclines and highlight myeloid-driven inflammation as a potential therapeutic target. Prospective clinical trials evaluating anti-inflammatory strategies to mitigate cardiotoxicity in this high-risk population are warranted. Abbreviations AF, atrial fibrillation; CHIP, Clonal hematopoiesis of indeterminate potential; HR, hazard ratio; LAD, left atrial diameter; LVEDD, left ventricular end-diastolic diameter; LVEF, left ventricular ejection fraction; NGS, Next-generation sequencing; sHR, subdistribution hazard ratio; VAF, variant allele frequency. Declarations Ethics approval and consent to participate The protocol of this study was reviewed and approved by the Ethics Committee of the Second Hospital of Tianjin Medical University and the Ethics Committee of Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, and the study was conducted in strict accordance with the principles of the Declaration of Helsinki. Given the retrospective design of this study, which only involves the analysis of existing medical records and laboratory data without any additional interventions or patient contact, and all data have been strictly anonymized, the ethics committees approved the waiver of patient informed consent. Consent for publication Not applicable. Conflict of interest statement The authors declare no competing financial interests for this work. Funding information This work was supported by grants from the National Natural Science Foundation of China (82270148, 82400173), the Key Project of Tianjin Public Health Project (25ZXWZSY00140), the Joint Funds of the Tianjin Natural Science Foundation (25JCLMJC01040), the Haihe Laboratory of Cell Ecosystem Innovation Fund (HH24KYZX0012), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2022-I2M-2-003), and the Tianjin Key Medical Discipline Construction Project (TJYXZDXK-3-001A-3). Author Contribution GST, YZ and YHZ contributed equally as co-first authors.GST conceived the study, performed the statistical analyses and prepared the manuscript. YZ and YHZ collected data, performed the statistical analyses and prepared the manuscript. KS, YJL，XJW，and FQD collected data. GYHL and TL revised the manuscript. MHD performed the external data validation. JB conceived the study, organized the internal data analysis and external data validation, and revised the manuscript. All authors validated the final version of the article. Acknowledgement The authors sincerely thank all the patients and their families/caregivers who participated in this study, as well as the clinicians and staff involved in patient care and data collection. We also gratefully acknowledge all the funding support that made this research possible. Data Availability The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. References Gervaso L, Cardinale D, Fazio N. Vascular Complications of Atrial Fibrillation in Patients With Cancer. JACC CardioOncology. 2025;7:168–70. El-Rayes M, Adam M, Fang J, et al. The Association of Malignancy With Stroke and Bleeding in Atrial Fibrillation: A Population-Based Cohort Study. JACC CardioOncology. 2025;7:157–67. Montalban-Bravo G, Kanagal-Shamanna R, Li Z, et al. Phenotypic subtypes of leukaemic transformation in chronic myelomonocytic leukaemia. Br J Haematol. 2023;203:581–92. Schuermans A, Vlasschaert C, Nauffal V, et al. Clonal haematopoiesis of indeterminate potential predicts incident cardiac arrhythmias. Eur Heart J. 2024;45:791–805. Lin AE, Bapat AC, Xiao L, et al. Clonal Hematopoiesis of Indeterminate Potential With Loss of Tet2 Enhances Risk for Atrial Fibrillation Through Nlrp3 Inflammasome Activation. Circulation. 2024;149:1419–34. Park YM, Jung W, Yeo Y, et al. Mid- and long-term risk of atrial fibrillation among breast cancer surgery survivors. BMC Med. 2024;22:88. Moslehi JJ, Cardio-Oncology. A New Clinical Frontier and Novel Platform for Cardiovascular Investigation. Circulation. 2024;150:513–5. Jia K, Cheng H, Ma W, et al. RNA Helicase DDX5 Maintains Cardiac Function by Regulating CamkIIδ Alternative Splicing. Circulation. 2024;150:1121–39. Arber DA, Orazi A, Hasserjian RP, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140:1200–28. Alaggio R, Amador C, Anagnostopoulos I, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia. 2022;36:1720–48. Schuermans A, Honigberg MC, Raffield LM, et al. Clonal Hematopoiesis and Incident Heart Failure With Preserved Ejection Fraction. JAMA Netw open. 2024;7:e2353244. Ahn HJ, An HY, Ryu G, et al. Clonal haematopoiesis of indeterminate potential and atrial fibrillation: an east Asian cohort study. Eur Heart J. 2024;45:778–90. Greenlee H, Iribarren C, Rana JS, et al. Risk of Cardiovascular Disease in Women With and Without Breast Cancer: The Pathways Heart Study. J Clin oncology: official J Am Soc Clin Oncol. 2022;40:1647–58. Tan R, Cong T, Xu G, et al. Anthracycline-Induced Atrial Structural and Electrical Remodeling Characterizes Early Cardiotoxicity and Contributes to Atrial Conductive Instability and Dysfunction. Antioxid Redox Signal. 2022;37:19–39. Larsen CM, Garcia Arango M, Dasari H, et al. Association of Anthracycline With Heart Failure in Patients Treated for Breast Cancer or Lymphoma, 1985–2010. JAMA Netw open. 2023;6:e2254669. Boddicker NJ, Larson MC, Castellino A, et al. Anthracycline treatment, cardiovascular risk factors and the cumulative incidence of cardiovascular disease in a cohort of newly diagnosed lymphoma patients from the modern treatment era. Am J Hematol. 2021;96:979–88. Yang Y, Yang H, Kiskin FN, Zhang JZ. The new era of cardiovascular research: revolutionizing cardiovascular research with 3D models in a dish. Medical review (2021) 2024;4:68–85. Calvillo-Argüelles O, Schoffel A, Capo-Chichi JM, et al. Cardiovascular Disease Among Patients With AML and CHIP-Related Mutations. JACC CardioOncology. 2022;4:38–49. Mammadova J, Colin-Leitzinger C, Nguyen D, et al. Clonal Hematopoiesis as a Molecular Risk Factor for Doxorubicin-Induced Cardiotoxicity: A Proof-of-Concept Study. JCO precision Oncol. 2023;7:e2300208. Yu Z, Fidler TP, Ruan Y et al. Genetic modification of inflammation- and clonal hematopoiesis-associated cardiovascular risk. J Clin Investig 2023;133. Uddin MDM, Nguyen NQH, Yu B, et al. Clonal hematopoiesis of indeterminate potential, DNA methylation, and risk for coronary artery disease. Nat Commun. 2022;13:5350. Zekavat SM, Viana-Huete V, Matesanz N, et al. TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease. Nat Cardiovasc Res. 2023;2:144–58. Additional Declarations No competing interests reported. Supplementary Files floatimage6.jpeg Central Illustration: Spliceosome gene mutations synergize with anthracyclines to increase AF risk in patients with hematologic malignancies This integrated model demonstrates that spliceosome gene mutations serve as a novel independent predictor of AF in hematologic malignancies. These mutations synergize with anthracycline exposure to significantly increase AF risk. Exploratory analyses reveal that monocyte-mediated inflammatory responses and left atrial enlargement partially mediate the association between spliceosome gene mutations and AF. These findings suggest that enhanced AF surveillance is necessary for hematologic malignancy patients with spliceosome gene mutations who are receiving anthracycline treatment, while also providing a theoretical basis for intervention strategies targeting myeloid inflammation. This figure was created by Figdraw. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8596634","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575748045,"identity":"30f6abe8-7dd5-4f0d-b1b0-53be2c2db979","order_by":0,"name":"guangshuai teng","email":"","orcid":"","institution":"the Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"guangshuai","middleName":"","lastName":"teng","suffix":""},{"id":575748052,"identity":"cc56292f-8e63-45f7-9fd9-b8510505c2a8","order_by":1,"name":"Yuan Zhou","email":"","orcid":"","institution":"Institute of Hematology \u0026 Blood 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13:45:38","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103310,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8596634/v1/5f43a5eeddd91f15c0aff5fd.html"},{"id":100595504,"identity":"07fa4080-683f-48be-b5c7-ff8c87eeaab9","added_by":"auto","created_at":"2026-01-19 13:48:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient Flow Chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8596634/v1/bc5cfcc9c038de4b0afd4ba9.png"},{"id":100595372,"identity":"38aabbe3-6a7b-40fb-a67b-413da21e3687","added_by":"auto","created_at":"2026-01-19 13:48:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122837,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between spliceosome gene mutations and AF risk in the discovery cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssociation of CHIP-related mutations with AF was analyzed by (A) multivariable Cox model and (B) Fine-Gray competing risk model (with mortality as the competing event). All analyses were adjusted for gender, age, hypertension, smoking, diabetes, hyperlipidemia, and exposure to anthracycline exposure. After (C) PSM1 or (D) PSM2, AF-free survival analysis was performed based on spliceosome gene mutation status, and Kaplan-Meier curves were generated. PSM1 was conducted for matching and balancing based on gender, age, hypertension, smoking, diabetes, and hyperlipidemia. PSM2 included acute coronary syndrome and anthracycline exposure additionally to the above factors for matching and balancing. AF, atrial fibrillation; CHIP, Clonal hematopoiesis of indeterminate potential; DTA, \u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e, or \u003cem\u003eASXL1\u003c/em\u003e mutations; HR, hazard ratio; sHR, subdistribution hazard ratio; PSM, Propensity Score Matching.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8596634/v1/5eafc4b5a2948693f2e4ed98.png"},{"id":100595900,"identity":"7648a98f-b7d0-42e1-9aea-85567592d532","added_by":"auto","created_at":"2026-01-19 13:49:37","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":350099,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between spliceosome gene mutations and AF risk in the validation cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssociation of CHIP-related mutations with AF was analyzed by (A) multivariable Cox model, (B) Firth penalized likelihood Cox model and (C) Fine-Gray competing risk model (with mortality as the competing event). AF, atrial fibrillation; CHIP, Clonal hematopoiesis of indeterminate potential; DTA, \u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e, or \u003cem\u003eASXL1\u003c/em\u003emutations.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8596634/v1/1db00fda44e5dfdd0130a2c2.jpeg"},{"id":100595081,"identity":"73cdef46-185e-44ac-bd48-e7fe854f7505","added_by":"auto","created_at":"2026-01-19 13:47:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":171641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpliceosome gene mutations amplify anthracycline-associated AF risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the discovery cohort, (A) Cumulative incidence of AF by anthracycline exposure and spliceosome gene mutation status. (B) Interaction between spliceosome gene mutations and anthracycline exposure on AF risk, adjusted for age and gender. In the validation cohort, (C) Cumulative incidence of AF by anthracycline exposure and spliceosome gene mutation status. (D) Interaction between spliceosome gene mutations and anthracycline exposure on AF risk, adjusted for age and gender. AF, atrial fibrillation; CR, competing risk; HR, hazard ratio; A: anthracycline; S: spliceosome gene mutation.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8596634/v1/71c11b22f9a868682994e346.png"},{"id":100595619,"identity":"737b042b-06fe-4eda-af8c-bb64c001880a","added_by":"auto","created_at":"2026-01-19 13:48:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":395250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploratory analysis: spliceosome gene mutations increase AF risk via inflammation and left atrial structural remodeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparison of LAD (A) between the spliceosome gene mutation group and the non-spliceosome gene mutation group, (B) between the AF group and non-AF group. (C) Mediation analysis (Bootstrap method, 1000 iterations) of LAD mediating the correlation between spliceosome gene mutations and AF. Comparison of the levels of (D) monocytes, (E) CRP, and (F) cytokines between the spliceosome gene mutation group and the non-spliceosome gene mutation group. (G) Multiple linear regression was used to analyze the correlation between cytokine levels and monocyte counts. (H) Co-culture of human peripheral blood monocytes and human atrial fibroblasts. Western blot results of target proteins in atrial fibroblasts: Atrial fibroblasts were co-cultured with peripheral blood monocytes from patients with spliceosome gene mutations and wild-type patients, respectively. (I) Quantitative analysis of collagen 1 expression level in I. Data are presented as mean ± standard deviation, with n = 3 per group, and analyzed using two-tailed unpaired t-test. AF, atrial fibrillation; CRP, C-reactive protein; LAD, left atrial diameter. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; *** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8596634/v1/e83e59f90477bf798c0dfc75.png"},{"id":100597537,"identity":"219b1a85-1bf4-405c-b5b0-5336495f6395","added_by":"auto","created_at":"2026-01-19 14:19:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2234681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8596634/v1/abcd3129-8a33-480f-bdd2-c6b3e015b0cb.pdf"},{"id":100595054,"identity":"57f432ce-eca9-40c7-80f2-0950f90ed9f7","added_by":"auto","created_at":"2026-01-19 13:47:15","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":629563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentral Illustration: Spliceosome gene mutations synergize with anthracyclines to increase AF risk in patients with hematologic malignancies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis integrated model demonstrates that spliceosome gene mutations serve as a novel independent predictor of AF in hematologic malignancies. These mutations synergize with anthracycline exposure to significantly increase AF risk. Exploratory analyses reveal that monocyte-mediated inflammatory responses and left atrial enlargement partially mediate the association between spliceosome gene mutations and AF. These findings suggest that enhanced AF surveillance is necessary for hematologic malignancy patients with spliceosome gene mutations who are receiving anthracycline treatment, while also providing a theoretical basis for intervention strategies targeting myeloid inflammation. This figure was created by Figdraw. (www.figdraw.com).\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8596634/v1/dbd3a9f0975d1dcbaceec9bf.jpeg"},{"id":100595075,"identity":"91f9d875-c9a2-4a0f-945c-2542afe475f0","added_by":"auto","created_at":"2026-01-19 13:47:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1649119,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterialsSpliceosomeandAF.docx","url":"https://assets-eu.researchsquare.com/files/rs-8596634/v1/d14e2b279fcaade85e596c7d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spliceosome gene mutations synergize with anthracyclines to amplify atrial fibrillation risk in hematologic malignancies: a multicenter cohort study with discovery and validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) is a highly prevalent and life-threatening complication in patients with hematologic malignancies, significantly increasing the risks of stroke, major bleeding\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, and all-cause mortality, and often leading to the interruption of critical chemotherapy. Although clonal hematopoiesis (CH)-related spliceosome gene mutations (such as \u003cem\u003eSF3B1\u003c/em\u003e, \u003cem\u003eSRSF2\u003c/em\u003e, \u003cem\u003eU2AF1\u003c/em\u003e, and \u003cem\u003eZRSR2\u003c/em\u003e) have been identified as core mechanisms driving clonal evolution and disease progression in hematologic malignancies\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, the role of these hematopoietic system-specific somatic mutations in mediating cardiovascular complications\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, particularly AF, remains to be thoroughly investigated. This knowledge gap is crucial in the field of hematologic malignancies, as emerging evidence suggests that CH mutations can drive inflammatory atrial remodeling through abnormal activation of myeloid cells and cytokine dysregulation\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, a pathway that may synergize with the cardiotoxicity of anthracyclines, the cornerstone of chemotherapy for hematologic malignancies. Anthracyclines themselves have been clearly and independently associated with cardiotoxicity and AF risk\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, but whether spliceosome mutations significantly amplify this treatment-related cardiac risk unique to patients with hematologic malignancies remains unknown. Preclinical models further demonstrate that spliceosome defects can disrupt RNA splicing programs in cardiac stromal cells, potentially activating profibrotic signaling pathways\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, providing a potential mechanism for CH-related cardiovascular damage. However, existing studies have not integrated patient-specific somatic mutation profiles, key treatment exposure histories (especially anthracyclines), and dynamic changes in inflammatory mediators in the hematologic malignancy population to systematically elucidate the unique pathogenic characteristics of AF.\u003c/p\u003e \u003cp\u003eFocusing on the core clinical scenario of hematologic malignancies, this study aims to evaluate the association between key spliceosome gene mutations that drive the development of hematologic malignancies and the pathogenesis of AF. It will specifically investigate the interaction between these hematopoietic clone-related mutations and anthracycline exposure on AF risk. By clarifying the AF susceptibility of carriers of specific mutations and their synergistic effects with core chemotherapeutic agents, this study will provide direct evidence for developing individualized monitoring strategies for cardiovascular complications in patients with hematologic malignancies, and lay the foundation for identifying high-risk subgroups and developing targeted cardiovascular protective interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study adopts a retrospective cohort design. The discovery cohort includes 1,321 patients with hematologic malignancies who were newly diagnosed and treated in the Department of Hematology, Second Hospital of Tianjin Medical University from November 1, 2017 to September 1, 2024. An independent validation cohort consists of 412 patients with hematologic malignancies who were newly diagnosed and treated in the Department of Hematology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences \u0026amp; Peking Union Medical College from January 1, 2015 to December 31, 2022. Hematologic malignancies include: Chronic myeloid leukemia (CML), philadelphia-negative myeloproliferative neoplasms (Ph-negative MPNs), acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), myelodysplastic syndrome/myeloproliferative neoplasm overlap syndromes (MDS/MPN overlap syndromes), and lymphoid malignancies. The diagnosis of all patients is strictly based on the 2022 diagnostic classification criteria of the World Health Organization\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, and the clinical and laboratory data are independently reviewed and confirmed by at least three senior hematologists. The study baseline is defined as the date of the first diagnosis of hematologic malignancies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe diagnosis of AF was established based on codes from the International Classification of Diseases, text searches in patients' electronic medical records, and confirmed by electrocardiogram (ECG) data or Holter monitoring. Cases of AF associated with surgery, valvular heart disease, or cardiomyopathy, as well as patients with a prior diagnosis of AF, were excluded. Information on all patients was verified through outpatient follow-up and telephone follow-up. AF-free survival was defined as the time from the diagnosis of MPN to the occurrence of AF or the last follow-up.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, only genetic variants with a variant allele frequency (VAF)\u0026thinsp;\u0026ge;\u0026thinsp;2% and classified as pathogenic or likely pathogenic were included. We analyzed the following mutations: DTA (\u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e, \u003cem\u003eASXL1\u003c/em\u003e), spliceosome genes (\u003cem\u003eU2AF1\u003c/em\u003e, \u003cem\u003eSF3B1\u003c/em\u003e, \u003cem\u003eZRSR2\u003c/em\u003e, \u003cem\u003eSRSF2\u003c/em\u003e), and DNA damage repair genes (\u003cem\u003ePPM1D\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e). These genes were selected as they represent common mutation types in clonal hematopoiesis of CHIP\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e and hematological malignancies. Termed \"CHIP-related gene mutations\" due to their commonality in CHIP, the presence of these variants in patients does not indicate concurrent CHIP, as CHIP is defined as a distinct entity within the clonal hematopoiesis spectrum and cannot coexist with hematological malignancies.\u003c/p\u003e \u003cp\u003eThe incidence rate of events during follow-up was calculated per 1,000 person-years. To assess associations between gene mutations and outcomes, we used multivariable Cox proportional hazards models. To address potential small-sample bias and sparse event limitations, Firth penalized likelihood Cox models were applied for robust estimation of hazard ratios (HRs) and confidence intervals (CIs). Additionally, competing risk regression (Fine-Gray subdistribution hazard model) was used to account for mortality as a competing event, calculating subdistribution sHRs. All models in the entire cohort were adjusted for covariates including age, sex, hypertension, hyperlipidemia, diabetes, smoking status, and anthracycline exposure. Sensitivity analysis was conducted in the following three ways to verify model stability and adjust for biases: Excluding patients with a variant allele frequency (VAF)\u0026thinsp;\u0026lt;\u0026thinsp;10%; Excluding AF events that occurred within 1 year after the diagnosis of hematologic malignancies, so as to address immortal time bias; Minimizing confounding bias and selection bias through the propensity score matching (PSM) method. This study adopted a 1:1 nearest-neighbor matching algorithm for PSM, with a caliper width set to 0.01 standard deviations of the propensity score. Propensity scores were estimated using a logistic regression model. The baseline covariates included in the PSM1 model were: age, sex, hypertension, hyperlipidemia, diabetes mellitus, and smoking status. The PSM2 model included all covariates from PSM1, plus acute coronary syndrome and anthracycline exposure. To control for the impact of potential confounding factors on study outcomes, this study performed multiple corrections using the false discovery rate (FDR) method, incorporating variables such as sex, age, and anthracycline exposure history as covariates into the model for adjustment.\u003c/p\u003e \u003cp\u003eTo assess the synergy between spliceosome mutations and anthracyclines, we performed stratified competing risk analysis: patients were grouped by mutation status and anthracycline exposure, with Gray\u0026rsquo;s test used to compare cumulative AF incidence. Additionally, we conducted interaction analysis via the Fine-Gray model, incorporating a multiplicative interaction term (mutation \u0026times; anthracycline) and adjusting for age and sex. Mediation analysis tested whether larger left atrial diameter (LAD) mediates the spliceosome-AF association, with bootstrap resampling (1000 replicates) to estimate direct and indirect effects. Multivariate models and mediation analysis were performed using R statistical software (version 4.3.1), with key packages including survival (for Cox/Fine-Gray models), coxphf (for Firth correction), and mediation (for mediation analysis).\u003c/p\u003e \u003cp\u003eContinuous variables are presented as median (range) and compared using the Mann-Whitney U test; categorical variables were analyzed with the chi-square test or Fisher\u0026rsquo;s exact test. Multiple linear regression was used to analyze the correlation between cytokine levels and monocyte counts. The Kaplan-Meier method was used for cumulative incidence analysis. A two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Analyses were performed using R v4.3.1 and SPSS v24.0.\u003c/p\u003e \u003cp\u003eThe detailed methods for next-generation sequencing (NGS) and cytokine level detection are provided in the Supplementary Appendix.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eThe study cohort comprised 1733 patients with hematologic malignancies with a median age of 62 years (range: 3\u0026ndash;92). Among them, 934 (53.9%) were male, and 250 (14.4%) received anthracycline therapy. NGS was performed in 1586 patients, revealing CHIP-related mutations in whom 653 (41.2%) and spliceosome gene mutations in whom 243 (15.3%). AF developed in 164 patients (9.5%), with cumulative incidence rates of 5.9%, 11.2%, 16.0%, and 31.3% at 1, 5, 10, and 20 years of follow-up, respectively \u003cb\u003e(Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. During follow-up, 339 patients (19.6%) died \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on CHIP status, patients in the entire cohort were stratified into CHIP (n\u0026thinsp;=\u0026thinsp;653) and no-CHIP (n\u0026thinsp;=\u0026thinsp;933) groups. CHIP carriers had higher proportions of older individuals, males, hypertension, diabetes, smoking history, AF, and mortality compared to no-CHIP carriers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, CHIP carriers had significantly lower hemoglobin levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The proportions of patients with CHIP across different disease types are shown in \u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of hematologic malignancy patients stratified by CHIP-related gene mutation status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHIP (n\u0026thinsp;=\u0026thinsp;653)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eno-CHIP (n\u0026thinsp;=\u0026thinsp;933)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time, y media (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, media (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (10\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (3\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e376 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e479 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePh\u003csup\u003e\u0026minus;\u003c/sup\u003eMPN (n\u0026thinsp;=\u0026thinsp;495), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e312 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCML (n\u0026thinsp;=\u0026thinsp;44), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML (n\u0026thinsp;=\u0026thinsp;283), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS (n\u0026thinsp;=\u0026thinsp;386), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS/MPN (n\u0026thinsp;=\u0026thinsp;40), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphoid Malignancy (n\u0026thinsp;=\u0026thinsp;338), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt the time of diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB, g/L media (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.5 (26\u0026ndash;237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (21\u0026ndash;261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L media (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.92 (0.01\u0026ndash;498.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.99 (0.01\u0026ndash;300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L media (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.01\u0026ndash;21.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35 (0.01\u0026ndash;75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L media (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (1-1559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (6-2433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthracycline drug exposure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e273(29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF, % media (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.0 (24\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0 (17\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD, mm media (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.8 (23.7\u0026ndash;62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.1 (23.0-61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD, mm media (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.3 (36.6\u0026ndash;73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.8 (36.3\u0026ndash;63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCHIP, Clonal hematopoiesis of indeterminate potential; CHIP refers to mutations in DTA, spliceosome, or DNA damage repair-related genes. Ph\u003csup\u003e\u0026minus;\u003c/sup\u003e MPN, Philadelphia negative myeloproliferative neoplasm; CML, chronic myeloid leukemia; AML, acute myeloid leukemia; MDS, myelodysplastic syndrome; HB, hemoglobin; WBC, white blood cell; PLT, platelet count; LVEF, left ventricular ejection fraction; LAD, left atrial diameter; LVEDD, left ventricular end-diastolic diameter; AF, atrial fibrillation. * \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of spliceosome gene mutations with AF in hematologic malignancies\u003c/h2\u003e \u003cp\u003eIn the discovery cohort, multivariable Cox models demonstrated a significant association between spliceosome gene mutations and AF (HR\u0026thinsp;=\u0026thinsp;1.59, 95% CI: 1.05\u0026ndash;2.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. When death was accounted for as a competing event in competing‑risk regression analyses, spliceosome gene mutations remained associated with AF (sHR\u0026thinsp;=\u0026thinsp;1.51, 95% CI: 1.01\u0026ndash;2.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. After adjusting for confounding factors via propensity score matching (PSM) analysis \u003cb\u003e(Supplementary Table S2)\u003c/b\u003e, the association between spliceosome gene mutations and AF remained statistically significant (PSM1: HR\u0026thinsp;=\u0026thinsp;1.801, 95% CI: 1.032\u0026ndash;3.144, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038; PSM2: HR\u0026thinsp;=\u0026thinsp;1.811, 95% CI: 1.037\u0026ndash;3.162, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D\u003cb\u003e)\u003c/b\u003e, which further verified the robustness of this association. Sensitivity analyses revealed that the association between spliceosome gene mutations and AF remained statistically significant after excluding patients with variant allele frequency (VAF)\u0026thinsp;\u0026lt;\u0026thinsp;10% or those who developed AF within 1 year of diagnosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(Figure S2A-B)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the validation cohort, Traditional Cox models and Firth penalized likelihood Cox models indicated a significant association between spliceosome gene mutations and AF (Traditional Cox: HR\u0026thinsp;=\u0026thinsp;2.71, 95% CI: 1.24\u0026ndash;5.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), Firth-corrected Cox: sHR\u0026thinsp;=\u0026thinsp;2.58, 95% CI: 1.18\u0026ndash;5.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), and consistent associations were retained in competing risk analyses (sHR\u0026thinsp;=\u0026thinsp;2.36, 95% CI: 1.10\u0026ndash;5.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C\u003cb\u003e)\u003c/b\u003e. After adjusting for covariates using FDR multiple corrections, spliceosome gene mutations still showed a significant association with AF (Firth-corrected Cox q value\u0026thinsp;=\u0026thinsp;0.036; Fine-Gray q value\u0026thinsp;=\u0026thinsp;0.045).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSynergistic effects of spliceosome gene mutations and anthracycline exposure on AF risk\u003c/h3\u003e\n\u003cp\u003eIn the discovery cohort, competing risk regression models adjusted for age and sex showed that patients with both spliceosome gene mutations and anthracycline exposure had a significantly higher cumulative incidence of AF than those with only spliceosome gene mutations, only anthracycline exposure, or neither (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The multiplicative interaction term (spliceosome gene mutation \u0026times; anthracycline exposure) exhibited a synergistic effect on AF risk (sHR\u0026thinsp;=\u0026thinsp;2.99, 95% CI 1.21\u0026ndash;7.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the validation cohort, competing risk regression models also revealed a significantly increased cumulative incidence of AF in patients with both spliceosome gene mutations and anthracycline exposure (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, and the multiplicative interaction term (spliceosome gene mutation \u0026times; anthracycline exposure) similarly showed a synergistic effect on AF risk (sHR\u0026thinsp;=\u0026thinsp;3.78, 95% CI 1.09\u0026ndash;13.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExploratory Analysis: Mediating Roles of Inflammation and Atrial Remodeling in the Association Between Spliceosome Gene Mutations and AF Risk\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the potential mechanism underlying the association between spliceosome gene mutations and AF risk, we analyzed the echocardiographic findings of patients with hematologic malignancies. The results showed that patients with spliceosome gene mutations had larger left atrial diameter (LAD) and left ventricular end-diastolic diameter (LVEDD) compared to those without spliceosome gene mutations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no significant difference in left ventricular ejection fraction (LVEF) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA \u003cb\u003eand Figure S3A-B)\u003c/b\u003e. Compared with non-AF individuals, AF patients exhibited increased LAD and decreased LVEF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while there was no significant difference in LVEDD \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB \u003cb\u003eand Figure S3C-D)\u003c/b\u003e. An exploratory mediation model \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC \u003cb\u003eand Supplementary Table S3)\u003c/b\u003e indicated that left atrial enlargement might partially mediate the association between spliceosome gene mutations and AF (mediation effect: 26.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analyses revealed that compared with patients negative for spliceosome gene mutations, those positive for spliceosome gene mutations had significantly elevated absolute monocyte counts, C-reactive protein (CRP) levels, and levels of IL-6, IL-17A, and IL-17F (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F\u003cb\u003e)\u003c/b\u003e. Multiple linear regression analysis revealed that monocyte counts were significantly positively correlated with levels of TNF-α, IL-6, and IL-12, and significantly negatively correlated with IL-10 levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. These findings suggest that inflammatory activation and structural remodeling may interact in the pathogenesis of spliceosome gene-related AF.\u003c/p\u003e \u003cp\u003eTo verify the role of monocytes in atrial remodeling, we isolated peripheral blood monocytes from patients with hematologic malignancies, including both those with spliceosome mutations and those without. In a further co-culture experiment involving monocytes and human atrial fibroblasts, the protein expression level of collagen 1 in atrial fibroblasts were significantly higher in the spliceosome mutation group than in the non-spliceosome mutation group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH-I\u003cb\u003e)\u003c/b\u003e. These findings suggest that monocyte-mediated inflammation is involved in the activation of atrial fibroblasts.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study of patients with hematologic malignancies, both the discovery and validation cohorts demonstrated that spliceosome gene mutations significantly increased the risk of AF. Spliceosome gene mutations amplified the promoting effect of anthracycline exposure on AF. Elevated monocyte counts and increased LAD partially mediated the association between spliceosome gene mutations and AF.\u003c/p\u003e \u003cp\u003eAs CHIP has been established as a novel risk factor for cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, recent studies have shown that CHIP, especially mutations in the DTA (\u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e, \u003cem\u003eASXL1\u003c/em\u003e) genes, significantly increases the risk of arrhythmias in healthy populations\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Based on this, the present study focused on patients with hematologic malignancies to investigate the association between somatic mutations and AF in this high-risk population. For the first time, we identified a significant association between spliceosome gene mutations, which are a subset of CHIP-related mutations, and AF. This conclusion remained robust after rigorous adjustment for competing risks and sensitivity analyses, thus confirming that spliceosome gene mutations represent a novel and non-traditional risk factor for AF in patients with hematologic malignancies.\u003c/p\u003e \u003cp\u003eAnthracyclines are well-established to induce myocardial injury, heart failure, and arrhythmias\u003csup\u003e[\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Our analysis revealed that spliceosome gene mutations enhanced the risk of anthracycline-associated AF in patients with hematologic malignancies, suggesting a gene-treatment synergistic interaction. This observation is consistent with recent reports in acute myeloid leukemia (AML)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, where clonal hematopoiesis exacerbated anthracycline-driven cardiotoxicity\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, highlighting the need to integrate somatic genomic profiling into cardio-oncology risk stratification.\u003c/p\u003e \u003cp\u003eThe mechanism of AF primarily involves atrial structural remodeling and electrical remodeling. The present study demonstrated that patients with spliceosome mutations had a significantly LA than those without spliceosome mutations. The mediating effect of LA enlargement on the association between spliceosome gene mutations and AF reached 26.6%, suggesting that structural remodeling plays a crucial role in this process. Recent studies have also indicated that spliceosome dysfunction may impair myocardial cell contraction and lead to cardiac dysfunction\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, which supports the hypothesis that spliceosome mutations may directly disrupt the electrophysiological stability of the heart through aberrant splicing.\u003c/p\u003e \u003cp\u003eThis study suggests that spliceosome gene mutations may promote AF through monocyte-mediated inflammatory pathways. Experiments confirmed that monocytes carrying mutations can directly induce collagen deposition in atrial fibroblasts by upregulating specific proinflammatory cytokines such as IL-6 and TNF-α, thereby forming a unique inflammatory signature. This signature differs from the widespread inflammation mediated by CRP and is similar to the mechanism observed in the CHIP mouse model\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, providing direct experimental evidence for spliceosome mutation-driven inflammatory atrial remodeling. These findings are highly consistent with the current view that CHIP affects the cardiovascular system through multiple mechanisms including immune dysregulation and epigenetic reprogramming\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. We thus hypothesize that in patients with spliceosome mutations, myeloid-driven inflammation and underlying cardiac-specific splicing defects may act synergistically to collectively increase AF susceptibility. Although this study reveals a new clinical perspective, its precise mechanism, particularly the respective contributions of hematopoietic clonality and intrinsic cardiac splicing defects, remains to be clarified by subsequent functional experiments.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eFirst, exploratory cytokine profiling indicates an IL-6/TNF-α-driven inflammatory axis, but these analyses were limited and lack temporal resolution, making dynamic relationships unclear. Second, our inflammatory/structural analyses are exploratory and require further mechanistic validation in animal models. Methodologically, the retrospective design, small sample size of rare mutation subgroups, and potential unmeasured confounders (e.g., subclinical cardiac dysfunction, lifestyle factors) suggest caution in interpretation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study identifies spliceosome gene mutations as a novel independent predictor of AF in patients with hematologic malignancies. Monocyte expansion and enlargement of the LAD partially mediate this association, while the synergistic effects of spliceosome mutations and anthracycline exposure further increase AF susceptibility, suggesting a gene-treatment interaction in arrhythmogenesis. These findings support enhanced AF monitoring in spliceosome-mutated patients receiving anthracyclines and highlight myeloid-driven inflammation as a potential therapeutic target. Prospective clinical trials evaluating anti-inflammatory strategies to mitigate cardiotoxicity in this high-risk population are warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAF, atrial fibrillation; CHIP, Clonal hematopoiesis of indeterminate potential; HR, hazard ratio; LAD, left atrial diameter; LVEDD, left ventricular end-diastolic diameter; LVEF, left ventricular ejection fraction; NGS, Next-generation sequencing; sHR, subdistribution hazard ratio; VAF, variant allele frequency.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThe protocol of this study was reviewed and approved by the Ethics Committee of the Second Hospital of Tianjin Medical University and the Ethics Committee of Peking Union Medical College Hospital, Chinese Academy of Medical Sciences \u0026amp; Peking Union Medical College, and the study was conducted in strict accordance with the principles of the Declaration of Helsinki. Given the retrospective design of this study, which only involves the analysis of existing medical records and laboratory data without any additional interventions or patient contact, and all data have been strictly anonymized, the ethics committees approved the waiver of patient informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of interest statement\u003c/strong\u003e \u003cp\u003eThe authors declare no competing financial interests for this work.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding information\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (82270148, 82400173), the Key Project of Tianjin Public Health Project (25ZXWZSY00140), the Joint Funds of the Tianjin Natural Science Foundation (25JCLMJC01040), the Haihe Laboratory of Cell Ecosystem Innovation Fund (HH24KYZX0012), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2022-I2M-2-003), and the Tianjin Key Medical Discipline Construction Project (TJYXZDXK-3-001A-3).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGST, YZ and YHZ contributed equally as co-first authors.GST conceived the study, performed the statistical analyses and prepared the manuscript. YZ and YHZ collected data, performed the statistical analyses and prepared the manuscript. KS, YJL，XJW，and FQD collected data. GYHL and TL revised the manuscript. MHD performed the external data validation. JB conceived the study, organized the internal data analysis and external data validation, and revised the manuscript. All authors validated the final version of the article.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors sincerely thank all the patients and their families/caregivers who participated in this study, as well as the clinicians and staff involved in patient care and data collection. We also gratefully acknowledge all the funding support that made this research possible.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGervaso L, Cardinale D, Fazio N. Vascular Complications of Atrial Fibrillation in Patients With Cancer. JACC CardioOncology. 2025;7:168\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Rayes M, Adam M, Fang J, et al. The Association of Malignancy With Stroke and Bleeding in Atrial Fibrillation: A Population-Based Cohort Study. JACC CardioOncology. 2025;7:157\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontalban-Bravo G, Kanagal-Shamanna R, Li Z, et al. Phenotypic subtypes of leukaemic transformation in chronic myelomonocytic leukaemia. Br J Haematol. 2023;203:581\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuermans A, Vlasschaert C, Nauffal V, et al. Clonal haematopoiesis of indeterminate potential predicts incident cardiac arrhythmias. Eur Heart J. 2024;45:791\u0026ndash;805.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin AE, Bapat AC, Xiao L, et al. Clonal Hematopoiesis of Indeterminate Potential With Loss of Tet2 Enhances Risk for Atrial Fibrillation Through Nlrp3 Inflammasome Activation. Circulation. 2024;149:1419\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark YM, Jung W, Yeo Y, et al. Mid- and long-term risk of atrial fibrillation among breast cancer surgery survivors. BMC Med. 2024;22:88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoslehi JJ, Cardio-Oncology. A New Clinical Frontier and Novel Platform for Cardiovascular Investigation. Circulation. 2024;150:513\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia K, Cheng H, Ma W, et al. RNA Helicase DDX5 Maintains Cardiac Function by Regulating CamkIIδ Alternative Splicing. Circulation. 2024;150:1121\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArber DA, Orazi A, Hasserjian RP, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140:1200\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlaggio R, Amador C, Anagnostopoulos I, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia. 2022;36:1720\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuermans A, Honigberg MC, Raffield LM, et al. Clonal Hematopoiesis and Incident Heart Failure With Preserved Ejection Fraction. JAMA Netw open. 2024;7:e2353244.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhn HJ, An HY, Ryu G, et al. Clonal haematopoiesis of indeterminate potential and atrial fibrillation: an east Asian cohort study. Eur Heart J. 2024;45:778\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenlee H, Iribarren C, Rana JS, et al. Risk of Cardiovascular Disease in Women With and Without Breast Cancer: The Pathways Heart Study. J Clin oncology: official J Am Soc Clin Oncol. 2022;40:1647\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan R, Cong T, Xu G, et al. Anthracycline-Induced Atrial Structural and Electrical Remodeling Characterizes Early Cardiotoxicity and Contributes to Atrial Conductive Instability and Dysfunction. Antioxid Redox Signal. 2022;37:19\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen CM, Garcia Arango M, Dasari H, et al. Association of Anthracycline With Heart Failure in Patients Treated for Breast Cancer or Lymphoma, 1985\u0026ndash;2010. JAMA Netw open. 2023;6:e2254669.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoddicker NJ, Larson MC, Castellino A, et al. Anthracycline treatment, cardiovascular risk factors and the cumulative incidence of cardiovascular disease in a cohort of newly diagnosed lymphoma patients from the modern treatment era. Am J Hematol. 2021;96:979\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Yang H, Kiskin FN, Zhang JZ. The new era of cardiovascular research: revolutionizing cardiovascular research with 3D models in a dish. Medical review (2021) 2024;4:68\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalvillo-Arg\u0026uuml;elles O, Schoffel A, Capo-Chichi JM, et al. Cardiovascular Disease Among Patients With AML and CHIP-Related Mutations. JACC CardioOncology. 2022;4:38\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMammadova J, Colin-Leitzinger C, Nguyen D, et al. Clonal Hematopoiesis as a Molecular Risk Factor for Doxorubicin-Induced Cardiotoxicity: A Proof-of-Concept Study. JCO precision Oncol. 2023;7:e2300208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Z, Fidler TP, Ruan Y et al. Genetic modification of inflammation- and clonal hematopoiesis-associated cardiovascular risk. J Clin Investig 2023;133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUddin MDM, Nguyen NQH, Yu B, et al. Clonal hematopoiesis of indeterminate potential, DNA methylation, and risk for coronary artery disease. Nat Commun. 2022;13:5350.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZekavat SM, Viana-Huete V, Matesanz N, et al. TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease. Nat Cardiovasc Res. 2023;2:144\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hematologic malignancy, atrial fibrillation, Spliceosome gene mutation, anthracycline, inflammation","lastPublishedDoi":"10.21203/rs.3.rs-8596634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8596634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAtrial fibrillation (AF) is a life-threatening complication in patients with hematologic malignancies, yet personalized risk stratification for this condition remains highly challenging. Spliceosome gene mutations are key drivers of the initiation and progression of hematologic malignancies. However, their specific role in the pathogenesis of AF, as well as the potential synergistic effects with core chemotherapeutic agents such as anthracyclines, remains largely unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort included a discovery cohort (n\u0026thinsp;=\u0026thinsp;1321) and a validation cohort (n\u0026thinsp;=\u0026thinsp;412) of hematologic malignancy patients (male, 53.9%; age, 62 years). Firth penalized Cox, competing risk models, and FDR correction assessed associations between CHIP-related mutations (DTA, spliceosome, DNA damage repair genes) and AF. Sensitivity analyses addressed immortal time bias and mutational burden thresholds. Interaction analysis evaluated spliceosome mutation-anthracycline synergy. Mechanistic validation involved co-culturing human monocytes with atrial fibroblasts and Western Blot (WB) to detect fibrosis-related proteins.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSpliceosome gene mutations were significantly associated with increased AF risk in the discovery cohort (adjusted HR\u0026thinsp;=\u0026thinsp;1.59, 95% CI: 1.05\u0026ndash;2.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), a finding robust to sensitivity analyses and confirmed in the validation cohort (HR\u0026thinsp;=\u0026thinsp;2.71, 95% CI: 1.24\u0026ndash;5.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). A significant synergistic interaction between spliceosome mutations and anthracycline exposure was observed in both the discovery cohort (sHR\u0026thinsp;=\u0026thinsp;2.99, 95% CI: 1.21\u0026ndash;7.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and the validation cohort (sHR\u0026thinsp;=\u0026thinsp;3.78, 95% CI: 1.09\u0026ndash;13.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036). Patients with spliceosome mutations exhibited left atrial enlargement (mediation effect: 26.6%), elevated monocyte counts, and higher levels of IL-6 and TNF-α. In \u003cem\u003evitro\u003c/em\u003e, monocytes from mutation carriers promoted collagen I expression in human atrial fibroblasts.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSpliceosome gene mutations are a novel, independent predictor of AF in hematologic malignancies and synergistically amplify anthracycline-related AF risk. Myeloid-driven inflammation and atrial structural remodeling appear to be key mediating mechanisms. These findings advocate for enhanced AF surveillance in spliceosome-mutated patients receiving anthracyclines and highlight inflammation as a potential therapeutic target for cardio-protection.\u003c/p\u003e","manuscriptTitle":"Spliceosome gene mutations synergize with anthracyclines to amplify atrial fibrillation risk in hematologic malignancies: a multicenter cohort study with discovery and validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 09:16:17","doi":"10.21203/rs.3.rs-8596634/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-04T09:47:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T12:52:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-31T01:30:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-29T09:45:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T23:39:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134606981673301271613660105276675793718","date":"2026-01-19T20:40:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16449930680601563021989548951463713300","date":"2026-01-15T14:58:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306563837220461250838493033269329557489","date":"2026-01-14T17:23:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137612451653248721356299933820746327458","date":"2026-01-14T16:46:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T16:32:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-14T13:04:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T08:08:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T07:47:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2026-01-14T02:33:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35c08e25-c3c2-4279-8b52-a49a8da0d930","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T11:38:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 09:16:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8596634","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8596634","identity":"rs-8596634","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}