Molecular, Clinical, and Hematological Characteristics of Myeloproliferative Neoplasms in Yemen: A Prospective Cross- Sectional Study

Molecular, Clinical, and Hematological Characteristics of Myeloproliferative Neoplasms in Yemen: A Prospective Cross- Sectional Study | 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 Molecular, Clinical, and Hematological Characteristics of Myeloproliferative Neoplasms in Yemen: A Prospective Cross- Sectional Study Mohammed Abdullah Al-Qadhi, Ahmed Qaid Salem, Mohammed Ahmed Hajar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8206505/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Myeloproliferative neoplasms (MPNs) are clonal hematopoietic disorders whose diagnosis relies on integrating clinical, morphological, and molecular data. Comprehensive data on the molecular landscape of MPNs from resource-limited regions like Yemen are scarce. This study aimed to characterize the molecular, clinical, and hematological profile of MPN patients in Yemen. Methods A prospective cross-sectional study was conducted on 118 patients with suspected MPNs at Al-Thawra Modern General Hospital, Sana’a, from August 2024 to May 2025. Diagnosis was established using WHO 2016 criteria, incorporating peripheral blood film, bone marrow evaluation, and molecular analysis for BCR-ABL1 , JAK2 V617F, CALR , and MPL mutations. Results Of 100 patients initially suspected of CML, 94 were BCR-ABL1 -positive. The remaining 6 were reclassified after further testing. The final cohort of 118 MPN patients included 94 (79.7%) CML, 21 (17.8%) Ph-negative MPNs (6 PV, 10 PMF, 5 ET), and 3 (2.5%) other rare MPNs. The median age was 41.5 years, with a male-to-female ratio of 1.4:1. CML patients were significantly younger than Ph-negative MPN patients (median age 38.5 vs. 56.0 years, p < 0.001). Among the 21 Ph-negative MPNs, a driver mutation was identified in 19 cases (90.5%): JAK2 V617F was most frequent (12/21, 57.1%), followed by CALR (6/21, 28.6%), and MPL (1/21, 4.8%). Two cases (9.5%) were triple-negative. Conclusion This study provides the first prospective data on the comprehensive molecular landscape of MPNs in Yemen. It confirms the high frequency of driver mutations and demonstrates the indispensable role of a full molecular panel for accurate classification of MPNs, highlighting the critical need to integrate these diagnostics into the standard of care in resource-limited settings. Myeloproliferative Neoplasms MPN Yemen JAK2 CALR MPL BCR-ABL1 Molecular Diagnostics Philadelphia-negative Introduction Myeloproliferative neoplasms (MPNs) are a group of clonal hematopoietic stem cell disorders characterized by the excessive proliferation of one or more mature myeloid cell lineages (Easwar and Siddon 2021 ). The World Health Organization (WHO) classification system categorizes MPNs into Chronic Myeloid Leukemia (CML), defined by the BCR-ABL1 fusion gene, and Philadelphia-negative (Ph-negative) MPNs, which primarily include Polycythemia Vera (PV), Essential Thrombocythemia (ET), and Primary Myelofibrosis (PMF) (Gianelli et al. 2022 ; Spivak 2017 ). The diagnosis and management of MPNs have been revolutionized by the discovery of key driver mutations. While BCR-ABL1 is the pathognomonic hallmark of CML (Wang et al. 2025 ; Viny and Levine 2014 ), the molecular landscape of Ph-negative MPNs is dominated by three mutually exclusive driver mutations: Janus Kinase 2 ( JAK2 ), Calreticulin ( CALR ), and Myeloproliferative Leukemia virus oncogene ( MPL ) (Helbig 2018 ). The JAK2 V617F mutation is the most common, found in over 95% of PV patients and 50–60% of ET and PMF patients (Helbig 2018 ). CALR mutations are the second most frequent, occurring in 25–35% of ET and PMF cases, while MPL mutations are found in 3–5% of ET and PMF patients (Barbui et al. 2018 ; Klampfl et al. 2013 ). The identification of these molecular markers is critical for diagnosis and carries significant prognostic weight. For instance, in PMF, CALR -mutated patients generally have a more favorable prognosis compared to JAK2 - or MPL -mutated patients, while triple-negative status is associated with the worst outcomes (Tefferi et al. 2014 ). Therefore, a comprehensive molecular workup is now considered the standard of care for the accurate classification and risk stratification of MPNs (Luque Paz et al. 2023 ). Although this study utilizes the well-established WHO 2016 criteria, which remain widely applicable, recent classifications (ICC 2022, WHO 2022) continue to emphasize the central role of these driver mutations in MPN diagnosis and classification (Gianelli et al. 2022 ). While extensive data on the molecular epidemiology of MPNs exist from high-income countries, there is a profound scarcity of such information from resource-limited regions, particularly the Middle East. In Yemen, ongoing healthcare challenges have limited access to advanced diagnostic technologies, and the diagnosis of MPNs has historically relied on clinical and morphological findings, which are often insufficient for precise classification. This knowledge gap impedes the adoption of modern, risk-adapted therapeutic strategies. This study aims to provide the first prospective, comprehensive analysis of the molecular, clinical, and hematological characteristics of MPN patients in Yemen. Materials and Methods Study Design and Patient Population This prospective cross-sectional study was conducted at the Hematology Department of Al-Thawra Modern General Hospital, a tertiary referral center in Sana’a, Yemen, from August 2024 to May 2025. The study was approved by the Institutional Ethics Committee of the Faculty of Medicine and Health Sciences, Sana’a University (Approval No.: FMHS/EC-2024/078). The study was conducted in accordance with the Declaration of Helsinki, and all participants provided written informed consent. Inclusion criteria Consecutive patients aged 18 years or older with a suspected MPN based on clinical and hematological findings (persistent cytosis, splenomegaly, or unexplained thrombosis) were eligible for enrollment. Exclusion criteria Patients with a previous history of another malignancy, those with insufficient material for molecular testing, or those who declined to provide informed consent were excluded. Clinical and Hematological Assessment Demographic data, clinical history, and physical examination findings were collected for all patients. A complete blood count (CBC) was performed using an automated hematology analyzer (Sysmex XN-1000, Sysmex Corporation, Kobe, Japan). Peripheral blood films (PBF) were prepared, stained with Leishman stain, and reviewed by two independent hematologists. Bone Marrow Examination Bone marrow aspiration (BMA) and trephine biopsy (BMB) were performed on all patients. BMA smears were stained with Leishman stain. BMB sections were stained with Hematoxylin and Eosin (H&E) and a reticulin stain. Morphological evaluation was performed by two independent hematologists according to the 2016 WHO criteria (Arber et al. 2016 ). Molecular Analysis Molecular testing was preferentially performed on peripheral blood samples; bone marrow was used only if a peripheral blood sample was unavailable or insufficient. Genomic DNA was extracted using the QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. BCR-ABL1 : The BCR-ABL1 fusion transcript (p210 and p190 isoforms) was detected by a qualitative reverse transcription-polymerase chain reaction (RT-PCR) assay. JAK2 ** V617F**: The JAK2 V617F mutation was detected using a highly sensitive allele-specific PCR (AS-PCR). CALR : Exon 9 of the CALR gene was amplified by PCR, followed by fragment analysis on a capillary electrophoresis system to screen for insertions and deletions. All identified variants were confirmed by Sanger sequencing. MPL : Exon 10 of the MPL gene was amplified by PCR and analyzed by Sanger sequencing to detect mutations at codons W515 and S505. All PCR assays included positive and negative controls in each run to ensure accuracy and avoid contamination. Statistical Analysis Categorical variables were compared using the Chi-square test or Fisher’s exact test, as appropriate. Continuous variables were compared using the Mann-Whitney U test (for two groups) or the Kruskal-Wallis test (for more than two groups) as the data were not normally distributed. A p-value < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). Results Patient Characteristics and Subtype Distribution From August 2024 to May 2025, a total of 125 patients with suspected MPN were assessed for eligibility. Of these, 118 patients met the inclusion criteria and were enrolled in the study. The final cohort comprised 94 (79.7%) patients with BCR-ABL1 -positive CML, 21 (17.8%) with Ph-negative MPNs, and 3 (2.5%) with other rare MPNs (2 Atypical CML, 1 Chronic Neutrophilic Leukemia). The Ph-negative MPN group included 6 (5.1%) cases of PV, 10 (8.5%) of PMF, and 5 (4.2%) of ET. The overall median age was 41.5 years (range: 18–75 years), with a male predominance (68 males, 50 females; M:F ratio 1.4:1). CML patients were significantly younger than Ph-negative MPN patients (median age 38.5 vs. 56.0 years, p < 0.001). Demographic and clinical characteristics are summarized in Table 1 . Table 1 Demographic and Clinical Characteristics of MPN Patients (n = 118) Characteristic CML (n = 94) PV (n = 6) PMF (n = 10) ET (n = 5) Other (n = 3) Total (n = 118) Age (years), Median (Range) 38.5 (18–72) 52 (38–68) 58 (35–75) 49 (32–62) 45 (40–55) 41.5 (18–75) Gender, n (%) Male 55 (58.5) 5 (83.3) 6 (60.0) 1 (20.0) 1 (33.3) 68 (57.6) Female 39 (41.5) 1 (16.7) 4 (40.0) 4 (80.0) 2 (66.7) 50 (42.4) Splenomegaly 77 (81.9) 5 (83.3) 9 (90.0) 2 (40.0) 3 (100) 96 (81.4) Hematological Findings at Diagnosis The hematological parameters at diagnosis for the classical MPNs (CML, PV, PMF, ET; n = 115) varied significantly across subtypes (Table 2 ). CML patients presented with marked hyperleukocytosis (median 165.0 ×10⁹/L). PV patients had the highest median hemoglobin (18.0 g/dL), and ET patients had the highest median platelet counts (825.0 ×10⁹/L). Anemia was most pronounced in PMF (median Hb 8.2 g/dL) and CML (median Hb 9.5 g/dL) patients. Table 2 Hematological Parameters at Diagnosis by Classical MPN Subtype (n = 115) * Parameter, Median (Range) CML (n = 94) PV (n = 6) PMF (n = 10) ET (n = 5) Hemoglobin (g/dL) 9.5 (5.5–15.2) 18.0 (16.0–21.0) 8.2 (6.0–12.0) 12.2 (10.5–14.5) WBC count (×10⁹/L) 165.0 (25.0-420.0) 13.5 (8.0–22.0) 11.0 (5.5–28.0) 9.0 (6.5–14.0) Platelet count (×10⁹/L) 320.0 (45–850) 460.0 (320–680) 180.0 (50–380) 825.0 (620–1150) Note :* Excludes 3 cases of other rare MPNs (2 atypical CML, 1 chronic neutrophilic leukemia).* Bone Marrow and Molecular Findings Of 100 patients initially suspected of CML based on clinical and morphological features, 94 were confirmed to be BCR-ABL1 -positive. The 6 BCR-ABL1 -negative cases underwent further molecular testing and were reclassified: 3 were found to have a Ph-negative MPN (1 PV, 1 PMF, 1 ET) and were added to that cohort, while the remaining 3 were diagnosed with other rare MPNs (2 aCML, 1 CNL). This resulted in a final Ph-negative MPN cohort of 21 patients. Among these, a driver mutation was identified in 19 cases (90.5%). The molecular findings for the classical MPNs are summarized in Table 3 . Table 3 Molecular Characterization of Classical MPN Patients (n = 115) * Molecular Marker CML (n = 94) PV (n = 6) PMF (n = 10) ET (n = 5) Total Positive (%) BCR-ABL1 Positive 94 (100%) 0 0 0 94 (81.7%) JAK2 V617F Positive 0 6 (100%) 5 (50.0%) 1 (20.0%) 12 (10.4%) CALR Positive 0 0 4 (40.0%) 2 (40.0%) 6 (5.2%) MPL Positive 0 0 1 (10.0%) 0 1 (0.9%) Triple-Negative 0 0 0 2 (40.0%) 2 (1.7%) Note :* Excludes 3 cases of other rare MPNs (2 atypical CML, 1 chronic neutrophilic leukemia).* JAK2 V617F was the most common mutation in Ph-negative MPNs (12/21, 57.1%), followed by CALR (6/21, 28.6%) and MPL (1/21, 4.8%). Two ET patients were triple-negative. The characteristics of Ph-negative MPN patients grouped by driver mutation are shown in Table 4 . Table 4 Characteristics of Ph-negative MPN Patients by Driver Mutation (n = 21) * Characteristic, Median (Range) JAK2 + (n = 12) CALR + (n = 6) MPL + (n = 1) Triple-Negative (n = 2) Age (years) 54 (38–75) 52 (32–65) 58 45 (42–48) Hemoglobin (g/dL) 17.5 (8.0–21.0) 10.5 (6.0-14.5) 8.2 11.8 (11.5–12.0) WBC count (×10⁹/L) 13.8 (8.0–28.0) 10.5 (5.5–22.0) 12.0 8.5 (8.0–9.0) Platelet count (×10⁹/L) 475 (180–680) 450 (50–850) 190 750 (650–850) Note :* JAK2 + group includes all 6 PV patients, 5 PMF, and 1 ET. CALR + group includes 4 PMF and 2 ET. MPL + is 1 PMF. Triple-negative includes 2 ET patients.* Patients with JAK2 V617F mutation had higher hemoglobin levels, consistent with the inclusion of all PV patients in this group. CALR -mutated patients were found exclusively in PMF and ET. The single MPL -mutated patient had PMF with marked anemia and thrombocytopenia. The triple-negative cases were both ET with significant thrombocytosis. Table 5 Association between BCR-ABL1 Status and Key Hematological Findings Finding BCR-ABL1 Positive (n = 94) BCR-ABL1 Negative (n = 24) p-value WBC count > 25 ×10⁹/L, n (%) 88 (93.6%) 2 (8.3%) 3%, n (%) 85 (90.4%) 3 (12.5%) 2%, n (%) 91 (96.8%) 4 (16.7%) 450 ×10⁹/L, n (%) 35 (37.2%) 8 (33.3%) 0.725 Discussion This study is the first to prospectively report on the comprehensive molecular landscape of MPNs in Yemen, providing crucial data from a severely under-represented region. Our findings confirm that the molecular epidemiology in Yemen is broadly consistent with global patterns, but also highlight the indispensable role of a full molecular diagnostic panel for accurate classification, moving beyond a reliance on clinical and morphological features alone. The Predominance of CML and a Younger Age of Onset The striking predominance of CML (79.7%) in our cohort and the significantly younger median age of onset (38.5 years) compared to Ph-negative MPNs (56.0 years) are notable findings. This aligns with previous reports from developing countries, suggesting a regional trend with significant healthcare implications. The high proportion of CML may reflect both a genuine regional trend and potential referral bias, as Al-Thawra Hospital is a tertiary referral center for complex hematological cases, where patients with aggressive presentations like CML are more likely to be referred. Molecular Landscape of Ph-negative MPNs In the Ph-negative MPN group, the identification of a driver mutation in 90.5% of cases demonstrates the high diagnostic yield of a comprehensive molecular approach. The frequencies of JAK2 V617F (57.1%), CALR (28.6%), and MPL (4.8%) are in line with established international data (Luque Paz et al. 2023 ; Walter et al. 2024 ; Tashkandi et al. 2024 ). This is a critical finding for Yemen, where a diagnostic strategy relying solely on JAK2 testing—often the only test available, if at all—would miss over 40% of Ph-negative MPN cases. The detection of CALR and MPL mutations is essential for accurate diagnosis, particularly in differentiating ET and PMF, and for prognostication, as CALR mutations are associated with a more favorable outcome (Luque Paz et al. 2023 ; Rumi et al. 2014 ). Genotype-Phenotype Correlations Our study revealed strong correlations between genotype and clinical phenotype, underscoring the biological significance of these driver mutations. JAK2 ** V617F:** As expected, all 6 PV patients were JAK2 V617F-positive. The JAK2 -mutated group exhibited significantly higher hemoglobin levels compared to CALR -mutated patients, consistent with the established role of the JAK-STAT pathway in erythropoiesis (Helbig 2018 ). CALR : CALR mutations were exclusively found in ET and PMF, and were associated with higher platelet counts, a finding consistent with previous studies (Klampfl et al. 2013 ; Rumi et al. 2014 ). MPL : The single MPL -mutated patient presented with PMF, marked anemia, and thrombocytopenia, a classic presentation for this mutation. The Challenge of Triple-Negative MPNs The presence of two triple-negative ET patients (9.5% of Ph-negative cases) highlights the diagnostic challenges that persist. The diagnosis of triple-negative MPNs remains a challenge. In resource-rich settings, next-generation sequencing (NGS) panels can identify non-canonical mutations in genes such as ASXL1 , TET2 , and DNMT3A , which can aid in diagnosis and prognostication (Tefferi 2016 ). The development of such capabilities should be a long-term goal for hematology centers in the region. Limitations This study has several limitations. First, the single-center design may limit the generalizability of our findings to the entire Yemeni population. Second, the small sample size of the Ph-negative MPN cohort, particularly for individual subtypes (ET n = 5), limits the power for robust statistical comparisons between mutation subgroups and precludes meaningful survival or outcome analyses. Third, the study was unable to perform NGS to investigate additional mutations with prognostic significance. Finally, the lack of long-term follow-up data prevents assessment of clinical outcomes and treatment responses. Conclusion In conclusion, this study provides the first prospective molecular characterization of MPNs in Yemen. We demonstrate that a comprehensive molecular panel ( BCR-ABL1 , JAK2 , CALR , MPL ) is not only feasible but is also indispensable for accurate diagnosis and classification, preventing misdiagnosis in a significant proportion of patients. Our findings strongly advocate for health policymakers and hospital administrators in Yemen and similar resource-limited settings to prioritize funding and infrastructure for the integration of this level of molecular diagnostics into the standard of care, which is crucial for improving patient management and outcomes. Declarations Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Ethics Approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Faculty of Medicine and Health Sciences, Sana’a University (Approval No.: FMHS/EC-2024/078). Consent to Participate: Informed consent was obtained from all individual participants included in the study. Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M.A.A. The draft of the manuscript was written by M.A.A. and A.K.S. M.A.H. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement The authors thank the laboratory staff at Al-Thawra Modern General Hospital for their technical assistance and the patients who participated in this study. We acknowledge the support of the Department of Hematology and the administration of Al-Thawra Modern General Hospital. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Arber DA, Orazi A, Hasserjian R et al (2016) The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 127:2391–2405. https://doi.org/10.1182/blood-2016-03-643544 Barbui T, Thiele J, Gisslinger H et al (2018) The 2016 WHO classification and diagnostic criteria for myeloproliferative neoplasms: document summary and in-depth discussion. Blood Cancer J 8:15. https://doi.org/10.1038/s41408-018-0054-y Easwar A, Siddon AJ (2021) Genetic Landscape of Myeloproliferative Neoplasms with an Emphasis on Molecular Diagnostic Laboratory Testing. Life 11:1158. https://doi.org/10.3390/life11111158 Gianelli U, Thiele J, Orazi A et al (2022) International Consensus Classification of myeloid and lymphoid neoplasms: myeloproliferative neoplasms. 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Leukemia Wang LY, Li J, Arbitman L et al (2025) Current Advances in the Diagnosis and Treatment of Major Myeloproliferative Neoplasms. Cancers 17:1834. https://doi.org/10.3390/cancers17111834 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMyeloproliferative neoplasms (MPNs) are a group of clonal hematopoietic stem cell disorders characterized by the excessive proliferation of one or more mature myeloid cell lineages (Easwar and Siddon \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The World Health Organization (WHO) classification system categorizes MPNs into Chronic Myeloid Leukemia (CML), defined by the \u003cem\u003eBCR-ABL1\u003c/em\u003e fusion gene, and Philadelphia-negative (Ph-negative) MPNs, which primarily include Polycythemia Vera (PV), Essential Thrombocythemia (ET), and Primary Myelofibrosis (PMF) (Gianelli et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Spivak \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe diagnosis and management of MPNs have been revolutionized by the discovery of key driver mutations. While \u003cem\u003eBCR-ABL1\u003c/em\u003e is the pathognomonic hallmark of CML (Wang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Viny and Levine \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the molecular landscape of Ph-negative MPNs is dominated by three mutually exclusive driver mutations: Janus Kinase 2 (\u003cem\u003eJAK2\u003c/em\u003e), Calreticulin (\u003cem\u003eCALR\u003c/em\u003e), and Myeloproliferative Leukemia virus oncogene (\u003cem\u003eMPL\u003c/em\u003e) (Helbig \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The \u003cem\u003eJAK2\u003c/em\u003e V617F mutation is the most common, found in over 95% of PV patients and 50\u0026ndash;60% of ET and PMF patients (Helbig \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eCALR\u003c/em\u003e mutations are the second most frequent, occurring in 25\u0026ndash;35% of ET and PMF cases, while \u003cem\u003eMPL\u003c/em\u003e mutations are found in 3\u0026ndash;5% of ET and PMF patients (Barbui et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Klampfl et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe identification of these molecular markers is critical for diagnosis and carries significant prognostic weight. For instance, in PMF, \u003cem\u003eCALR\u003c/em\u003e-mutated patients generally have a more favorable prognosis compared to \u003cem\u003eJAK2\u003c/em\u003e- or \u003cem\u003eMPL\u003c/em\u003e-mutated patients, while triple-negative status is associated with the worst outcomes (Tefferi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, a comprehensive molecular workup is now considered the standard of care for the accurate classification and risk stratification of MPNs (Luque Paz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although this study utilizes the well-established WHO 2016 criteria, which remain widely applicable, recent classifications (ICC 2022, WHO 2022) continue to emphasize the central role of these driver mutations in MPN diagnosis and classification (Gianelli et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile extensive data on the molecular epidemiology of MPNs exist from high-income countries, there is a profound scarcity of such information from resource-limited regions, particularly the Middle East. In Yemen, ongoing healthcare challenges have limited access to advanced diagnostic technologies, and the diagnosis of MPNs has historically relied on clinical and morphological findings, which are often insufficient for precise classification. This knowledge gap impedes the adoption of modern, risk-adapted therapeutic strategies. This study aims to provide the first prospective, comprehensive analysis of the molecular, clinical, and hematological characteristics of MPN patients in Yemen.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Patient Population\u003c/h2\u003e\u003cp\u003eThis prospective cross-sectional study was conducted at the Hematology Department of Al-Thawra Modern General Hospital, a tertiary referral center in Sana\u0026rsquo;a, Yemen, from August 2024 to May 2025. The study was approved by the Institutional Ethics Committee of the Faculty of Medicine and Health Sciences, Sana\u0026rsquo;a University (Approval No.: FMHS/EC-2024/078). The study was conducted in accordance with the Declaration of Helsinki, and all participants provided written informed consent.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003cp\u003eConsecutive patients aged 18 years or older with a suspected MPN based on clinical and hematological findings (persistent cytosis, splenomegaly, or unexplained thrombosis) were eligible for enrollment.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003cp\u003ePatients with a previous history of another malignancy, those with insufficient material for molecular testing, or those who declined to provide informed consent were excluded.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical and Hematological Assessment\u003c/h3\u003e\n\u003cp\u003eDemographic data, clinical history, and physical examination findings were collected for all patients. A complete blood count (CBC) was performed using an automated hematology analyzer (Sysmex XN-1000, Sysmex Corporation, Kobe, Japan). Peripheral blood films (PBF) were prepared, stained with Leishman stain, and reviewed by two independent hematologists.\u003c/p\u003e\n\u003ch3\u003eBone Marrow Examination\u003c/h3\u003e\n\u003cp\u003eBone marrow aspiration (BMA) and trephine biopsy (BMB) were performed on all patients. BMA smears were stained with Leishman stain. BMB sections were stained with Hematoxylin and Eosin (H\u0026amp;E) and a reticulin stain. Morphological evaluation was performed by two independent hematologists according to the 2016 WHO criteria (Arber et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMolecular Analysis\u003c/h3\u003e\n\u003cp\u003eMolecular testing was preferentially performed on peripheral blood samples; bone marrow was used only if a peripheral blood sample was unavailable or insufficient. Genomic DNA was extracted using the QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBCR-ABL1\u003c/b\u003e: The \u003cem\u003eBCR-ABL1\u003c/em\u003e fusion transcript (p210 and p190 isoforms) was detected by a qualitative reverse transcription-polymerase chain reaction (RT-PCR) assay.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eJAK2\u003c/b\u003e** V617F**: The \u003cem\u003eJAK2\u003c/em\u003e V617F mutation was detected using a highly sensitive allele-specific PCR (AS-PCR).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCALR\u003c/b\u003e: Exon 9 of the \u003cem\u003eCALR\u003c/em\u003e gene was amplified by PCR, followed by fragment analysis on a capillary electrophoresis system to screen for insertions and deletions. All identified variants were confirmed by Sanger sequencing.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMPL\u003c/b\u003e: Exon 10 of the \u003cem\u003eMPL\u003c/em\u003e gene was amplified by PCR and analyzed by Sanger sequencing to detect mutations at codons W515 and S505.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAll PCR assays included positive and negative controls in each run to ensure accuracy and avoid contamination.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were compared using the Chi-square test or Fisher\u0026rsquo;s exact test, as appropriate. Continuous variables were compared using the Mann-Whitney U test (for two groups) or the Kruskal-Wallis test (for more than two groups) as the data were not normally distributed. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics and Subtype Distribution\u003c/h2\u003e\u003cp\u003eFrom August 2024 to May 2025, a total of 125 patients with suspected MPN were assessed for eligibility. Of these, 118 patients met the inclusion criteria and were enrolled in the study. The final cohort comprised 94 (79.7%) patients with \u003cem\u003eBCR-ABL1\u003c/em\u003e-positive CML, 21 (17.8%) with Ph-negative MPNs, and 3 (2.5%) with other rare MPNs (2 Atypical CML, 1 Chronic Neutrophilic Leukemia). The Ph-negative MPN group included 6 (5.1%) cases of PV, 10 (8.5%) of PMF, and 5 (4.2%) of ET.\u003c/p\u003e\u003cp\u003eThe overall median age was 41.5 years (range: 18\u0026ndash;75 years), with a male predominance (68 males, 50 females; M:F ratio 1.4:1). CML patients were significantly younger than Ph-negative MPN patients (median age 38.5 vs. 56.0 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Demographic and clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eDemographic and Clinical Characteristics of MPN Patients (n\u0026thinsp;=\u0026thinsp;118)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCML (n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePV (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePMF (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eET (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOther (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;118)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years), Median (Range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.5 (18\u0026ndash;72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (38\u0026ndash;68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (35\u0026ndash;75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49 (32\u0026ndash;62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45 (40\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41.5 (18\u0026ndash;75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55 (58.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e68 (57.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (41.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50 (42.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSplenomegaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77 (81.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96 (81.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHematological Findings at Diagnosis\u003c/h3\u003e\n\u003cp\u003eThe hematological parameters at diagnosis for the classical MPNs (CML, PV, PMF, ET; n\u0026thinsp;=\u0026thinsp;115) varied significantly across subtypes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). CML patients presented with marked hyperleukocytosis (median 165.0 \u0026times;10⁹/L). PV patients had the highest median hemoglobin (18.0 g/dL), and ET patients had the highest median platelet counts (825.0 \u0026times;10⁹/L). Anemia was most pronounced in PMF (median Hb 8.2 g/dL) and CML (median Hb 9.5 g/dL) patients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eHematological Parameters at Diagnosis by Classical MPN Subtype (n\u0026thinsp;=\u0026thinsp;115)\u003c/b\u003e*\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter, Median (Range)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCML (n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePV (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePMF (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eET (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.5 (5.5\u0026ndash;15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.0 (16.0\u0026ndash;21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.2 (6.0\u0026ndash;12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.2 (10.5\u0026ndash;14.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC count (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e165.0 (25.0-420.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.5 (8.0\u0026ndash;22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.0 (5.5\u0026ndash;28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (6.5\u0026ndash;14.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e320.0 (45\u0026ndash;850)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e460.0 (320\u0026ndash;680)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e180.0 (50\u0026ndash;380)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e825.0 (620\u0026ndash;1150)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e:* Excludes 3 cases of other rare MPNs (2 atypical CML, 1 chronic neutrophilic leukemia).*\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBone Marrow and Molecular Findings\u003c/h2\u003e\u003cp\u003eOf 100 patients initially suspected of CML based on clinical and morphological features, 94 were confirmed to be \u003cem\u003eBCR-ABL1\u003c/em\u003e-positive. The 6 \u003cem\u003eBCR-ABL1\u003c/em\u003e-negative cases underwent further molecular testing and were reclassified: 3 were found to have a Ph-negative MPN (1 PV, 1 PMF, 1 ET) and were added to that cohort, while the remaining 3 were diagnosed with other rare MPNs (2 aCML, 1 CNL).\u003c/p\u003e\u003cp\u003eThis resulted in a final Ph-negative MPN cohort of 21 patients. Among these, a driver mutation was identified in 19 cases (90.5%). The molecular findings for the classical MPNs are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMolecular Characterization of Classical MPN Patients (n\u0026thinsp;=\u0026thinsp;115)\u003c/b\u003e*\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular Marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCML (n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePV (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePMF (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eET (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal Positive (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBCR-ABL1\u003c/em\u003e Positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94 (81.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJAK2\u003c/em\u003e V617F Positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCALR\u003c/em\u003e Positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMPL\u003c/em\u003e Positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriple-Negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e:* Excludes 3 cases of other rare MPNs (2 atypical CML, 1 chronic neutrophilic leukemia).*\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eJAK2\u003c/em\u003e V617F was the most common mutation in Ph-negative MPNs (12/21, 57.1%), followed by \u003cem\u003eCALR\u003c/em\u003e (6/21, 28.6%) and \u003cem\u003eMPL\u003c/em\u003e (1/21, 4.8%). Two ET patients were triple-negative. The characteristics of Ph-negative MPN patients grouped by driver mutation are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eCharacteristics of Ph-negative MPN Patients by Driver Mutation (n\u0026thinsp;=\u0026thinsp;21)\u003c/b\u003e*\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic, Median (Range)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eJAK2\u003c/em\u003e+ (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCALR\u003c/em\u003e+ (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eMPL\u003c/em\u003e+ (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTriple-Negative (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (38\u0026ndash;75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (32\u0026ndash;65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45 (42\u0026ndash;48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.5 (8.0\u0026ndash;21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.5 (6.0-14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.8 (11.5\u0026ndash;12.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC count (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.8 (8.0\u0026ndash;28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.5 (5.5\u0026ndash;22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.5 (8.0\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e475 (180\u0026ndash;680)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e450 (50\u0026ndash;850)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e750 (650\u0026ndash;850)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e:* \u003cb\u003eJAK2\u003c/b\u003e\u0026thinsp;+\u0026thinsp;group includes all 6 PV patients, 5 PMF, and 1 ET. \u003cb\u003eCALR\u003c/b\u003e\u0026thinsp;+\u0026thinsp;group includes 4 PMF and 2 ET. \u003cb\u003eMPL\u003c/b\u003e\u0026thinsp;+\u0026thinsp;is 1 PMF. Triple-negative includes 2 ET patients.*\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePatients with \u003cem\u003eJAK2\u003c/em\u003e V617F mutation had higher hemoglobin levels, consistent with the inclusion of all PV patients in this group. \u003cem\u003eCALR\u003c/em\u003e-mutated patients were found exclusively in PMF and ET. The single \u003cem\u003eMPL\u003c/em\u003e-mutated patient had PMF with marked anemia and thrombocytopenia. The triple-negative cases were both ET with significant thrombocytosis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between \u003cem\u003eBCR-ABL1\u003c/em\u003e Status and Key Hematological Findings\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cp\u003eFinding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBCR-ABL1\u003c/em\u003e Positive (n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eBCR-ABL1\u003c/em\u003e Negative (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC count\u0026thinsp;\u0026gt;\u0026thinsp;25 \u0026times;10⁹/L, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88 (93.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (8.3%)\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\u003eBasophilia\u0026thinsp;\u0026gt;\u0026thinsp;3%, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85 (90.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (12.5%)\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\u003eCirculating Myelocytes\u0026thinsp;\u0026gt;\u0026thinsp;2%, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e91 (96.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (16.7%)\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\u003ePlatelet count\u0026thinsp;\u0026gt;\u0026thinsp;450 \u0026times;10⁹/L, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35 (37.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to prospectively report on the comprehensive molecular landscape of MPNs in Yemen, providing crucial data from a severely under-represented region. Our findings confirm that the molecular epidemiology in Yemen is broadly consistent with global patterns, but also highlight the indispensable role of a full molecular diagnostic panel for accurate classification, moving beyond a reliance on clinical and morphological features alone.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eThe Predominance of CML and a Younger Age of Onset\u003c/h2\u003e\u003cp\u003eThe striking predominance of CML (79.7%) in our cohort and the significantly younger median age of onset (38.5 years) compared to Ph-negative MPNs (56.0 years) are notable findings. This aligns with previous reports from developing countries, suggesting a regional trend with significant healthcare implications. The high proportion of CML may reflect both a genuine regional trend and potential referral bias, as Al-Thawra Hospital is a tertiary referral center for complex hematological cases, where patients with aggressive presentations like CML are more likely to be referred.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMolecular Landscape of Ph-negative MPNs\u003c/h2\u003e\u003cp\u003eIn the Ph-negative MPN group, the identification of a driver mutation in 90.5% of cases demonstrates the high diagnostic yield of a comprehensive molecular approach. The frequencies of \u003cem\u003eJAK2\u003c/em\u003e V617F (57.1%), \u003cem\u003eCALR\u003c/em\u003e (28.6%), and \u003cem\u003eMPL\u003c/em\u003e (4.8%) are in line with established international data (Luque Paz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Walter et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tashkandi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is a critical finding for Yemen, where a diagnostic strategy relying solely on \u003cem\u003eJAK2\u003c/em\u003e testing\u0026mdash;often the only test available, if at all\u0026mdash;would miss over 40% of Ph-negative MPN cases. The detection of \u003cem\u003eCALR\u003c/em\u003e and \u003cem\u003eMPL\u003c/em\u003e mutations is essential for accurate diagnosis, particularly in differentiating ET and PMF, and for prognostication, as \u003cem\u003eCALR\u003c/em\u003e mutations are associated with a more favorable outcome (Luque Paz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rumi et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eGenotype-Phenotype Correlations\u003c/h2\u003e\u003cp\u003eOur study revealed strong correlations between genotype and clinical phenotype, underscoring the biological significance of these driver mutations.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eJAK2\u003c/b\u003e** V617F:** As expected, all 6 PV patients were \u003cem\u003eJAK2\u003c/em\u003e V617F-positive. The \u003cem\u003eJAK2\u003c/em\u003e-mutated group exhibited significantly higher hemoglobin levels compared to \u003cem\u003eCALR\u003c/em\u003e-mutated patients, consistent with the established role of the JAK-STAT pathway in erythropoiesis (Helbig \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCALR\u003c/b\u003e: \u003cem\u003eCALR\u003c/em\u003e mutations were exclusively found in ET and PMF, and were associated with higher platelet counts, a finding consistent with previous studies (Klampfl et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rumi et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMPL\u003c/b\u003e: The single \u003cem\u003eMPL\u003c/em\u003e-mutated patient presented with PMF, marked anemia, and thrombocytopenia, a classic presentation for this mutation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eThe Challenge of Triple-Negative MPNs\u003c/h2\u003e\u003cp\u003eThe presence of two triple-negative ET patients (9.5% of Ph-negative cases) highlights the diagnostic challenges that persist. The diagnosis of triple-negative MPNs remains a challenge. In resource-rich settings, next-generation sequencing (NGS) panels can identify non-canonical mutations in genes such as \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e, and \u003cem\u003eDNMT3A\u003c/em\u003e, which can aid in diagnosis and prognostication (Tefferi \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The development of such capabilities should be a long-term goal for hematology centers in the region.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, the single-center design may limit the generalizability of our findings to the entire Yemeni population. Second, the small sample size of the Ph-negative MPN cohort, particularly for individual subtypes (ET n\u0026thinsp;=\u0026thinsp;5), limits the power for robust statistical comparisons between mutation subgroups and precludes meaningful survival or outcome analyses. Third, the study was unable to perform NGS to investigate additional mutations with prognostic significance. Finally, the lack of long-term follow-up data prevents assessment of clinical outcomes and treatment responses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides the first prospective molecular characterization of MPNs in Yemen. We demonstrate that a comprehensive molecular panel (\u003cem\u003eBCR-ABL1\u003c/em\u003e, \u003cem\u003eJAK2\u003c/em\u003e, \u003cem\u003eCALR\u003c/em\u003e, \u003cem\u003eMPL\u003c/em\u003e) is not only feasible but is also indispensable for accurate diagnosis and classification, preventing misdiagnosis in a significant proportion of patients. Our findings strongly advocate for health policymakers and hospital administrators in Yemen and similar resource-limited settings to prioritize funding and infrastructure for the integration of this level of molecular diagnostics into the standard of care, which is crucial for improving patient management and outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics Approval:\u003c/h2\u003e\u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Faculty of Medicine and Health Sciences, Sana\u0026rsquo;a University (Approval No.: FMHS/EC-2024/078).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e\u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M.A.A. The draft of the manuscript was written by M.A.A. and A.K.S. M.A.H. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the laboratory staff at Al-Thawra Modern General Hospital for their technical assistance and the patients who participated in this study. We acknowledge the support of the Department of Hematology and the administration of Al-Thawra Modern General Hospital.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArber DA, Orazi A, Hasserjian R et al (2016) The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 127:2391\u0026ndash;2405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1182/blood-2016-03-643544\u003c/span\u003e\u003cspan address=\"10.1182/blood-2016-03-643544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarbui T, Thiele J, Gisslinger H et al (2018) The 2016 WHO classification and diagnostic criteria for myeloproliferative neoplasms: document summary and in-depth discussion. 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Cancers 17:1834. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers17111834\u003c/span\u003e\u003cspan address=\"10.3390/cancers17111834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Myeloproliferative Neoplasms, MPN, Yemen, JAK2, CALR, MPL, BCR-ABL1, Molecular Diagnostics, Philadelphia-negative","lastPublishedDoi":"10.21203/rs.3.rs-8206505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8206505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eMyeloproliferative neoplasms (MPNs) are clonal hematopoietic disorders whose diagnosis relies on integrating clinical, morphological, and molecular data. Comprehensive data on the molecular landscape of MPNs from resource-limited regions like Yemen are scarce. This study aimed to characterize the molecular, clinical, and hematological profile of MPN patients in Yemen.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA prospective cross-sectional study was conducted on 118 patients with suspected MPNs at Al-Thawra Modern General Hospital, Sana\u0026rsquo;a, from August 2024 to May 2025. Diagnosis was established using WHO 2016 criteria, incorporating peripheral blood film, bone marrow evaluation, and molecular analysis for \u003cem\u003eBCR-ABL1\u003c/em\u003e, \u003cem\u003eJAK2\u003c/em\u003e V617F, \u003cem\u003eCALR\u003c/em\u003e, and \u003cem\u003eMPL\u003c/em\u003e mutations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf 100 patients initially suspected of CML, 94 were \u003cem\u003eBCR-ABL1\u003c/em\u003e-positive. The remaining 6 were reclassified after further testing. The final cohort of 118 MPN patients included 94 (79.7%) CML, 21 (17.8%) Ph-negative MPNs (6 PV, 10 PMF, 5 ET), and 3 (2.5%) other rare MPNs. The median age was 41.5 years, with a male-to-female ratio of 1.4:1. CML patients were significantly younger than Ph-negative MPN patients (median age 38.5 vs. 56.0 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the 21 Ph-negative MPNs, a driver mutation was identified in 19 cases (90.5%): \u003cem\u003eJAK2\u003c/em\u003e V617F was most frequent (12/21, 57.1%), followed by \u003cem\u003eCALR\u003c/em\u003e (6/21, 28.6%), and \u003cem\u003eMPL\u003c/em\u003e (1/21, 4.8%). Two cases (9.5%) were triple-negative.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study provides the first prospective data on the comprehensive molecular landscape of MPNs in Yemen. It confirms the high frequency of driver mutations and demonstrates the indispensable role of a full molecular panel for accurate classification of MPNs, highlighting the critical need to integrate these diagnostics into the standard of care in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Molecular, Clinical, and Hematological Characteristics of Myeloproliferative Neoplasms in Yemen: A Prospective Cross- Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 18:00:07","doi":"10.21203/rs.3.rs-8206505/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b8853b4-d3fb-49b9-b0f3-a21a8c0d273f","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T18:00:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 18:00:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8206505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8206505","identity":"rs-8206505","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}