GATA1 insufficiencies in dysmegakaryopoiesis of myelodysplastic syndromes

GATA1 insufficiencies in dysmegakaryopoiesis of myelodysplastic syndromes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article GATA1 insufficiencies in dysmegakaryopoiesis of myelodysplastic syndromes Zhijian Xiao, Fuhui Li, Yudi Zhang, Chengwen Li, Qi Sun, Jinqin Liu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4488001/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 GATA1 is one of critical transcription factors for megakaryopoiesis and platelet production. Our study aimed to explore the correlations between GATA1 expression and dysmegakaryopoiesis in myelodysplastic syndromes (MDS). Data of blood cell counts, cytogenetics and TP53 mutation status from 90 MDS patients at diagnosis were collected. Firstly, we assessed GATA1 expression level of megakaryocytes by performing immunohistochemical staining on paraffin-embedded bone marrow biopsy sections from these patients. According to GATA1 expression level of megakaryocytes and positive megakaryocyte percentage, we assigned each patient a GATA1 score. Compared with TP53 -wildtype patients, GATA1 scores significantly decreased in TP53- mutated patients ( P < 0.001). Patients with abnormal karyotypes showed decreased GATA1 scores than those with normal karyotypes ( P = 0.024). GATA1 expression levels were significantly downregulated in dysplastic megakaryocytes, especially micromegakaryocytes, compared with normal megakaryocytes ( P < 0.001). Furthermore, we explored the correlation between GATA1 expression levels and cytogenetic abnormalities of the same megakaryocyte using the morphology antibody chromosome (MAC) technique on fresh bone marrow smears. We found that GATA1-negative megakaryocytes had higher frequencies of cytogenetic abnormalities. Our results indicated that decreased GATA1 expression level of megakaryocytes was significantly associated with TP53 mutations, abnormal karyotypes and dysmegakaryopoiesis in MDS, suggesting that downregulation of GATA1 expression levels of megakaryocytes plays a critical role in the pathogenesis of MDS. Health sciences/Diseases/Haematological diseases/Haematological cancer/Myelodysplastic syndrome Biological sciences/Genetics/Cytogenetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Myelodysplastic syndromes (MDS), a group of heterogeneous myeloid neoplasm, are characterized by morphological dysplasia and peripheral blood cytopenia 1 , 2 . At present, the MDS subtypes are classified mainly according to cytogenetics, molecular genetics, and bone marrow morphology 1 , 3 . Dysplastic megakaryocytes, including hypolobulated, multinucleated megakaryocytes and micromegakaryocytes, are common in patients with MDS 4 , 5 . Previous studies also suggested that the severity of megakaryocytic dysplasia in MDS patients was associated with survival 4 , 5 . GATA1, which is mainly expressed in the erythroid, megakaryocytic, eosinophil and mast lineages, is a critical transcription factor for hematopoiesis 6 – 8 . GATA1 plays an essential role in erythropoiesis, megakaryopoiesis and platelet production 6 , 9 , 10 . Moreover, GATA1 deficiency in megakaryocytes caused differentiation delay and induced marked hyperproliferation and decreased platelets counts 6 , 11 – 13 . Several studies have explored GATA1 expression level in the megakaryocytes of patients with BCR/ABL1- negative myeloproliferative neoplasms (MPNs) and revealed that GATA1 expression level decreases during fibrotic progression and may be associated with leukemia transformation 14 , 15 . According to previous studies, the downregulation of GATA1 in megakaryocytes in primary myelofibrosis is closely related to impaired megakaryocyte differentiation and myelofibrosis 12 , 16 – 18 . Although the role of GATA1 deficiency in megakaryocytes in the pathogenesis of myelofibrosis in BCR/ABL1 -negative MPN is relatively clear, related research in MDS is scarce 12 , 16 , 17 , 19 . Fadilah et al . suggested that downregulation of GATA1 mRNA expression was closely associated with the severity of erythroid, granulocytic and megakaryocytic dysplasia in MDS patients 20 . Decreased GATA1 expression was identified in megakaryocytes in 34 MDS patients with dysmegakaryopoiesis using immunohistochemical staining with GATA1 on paraffin-embedded bone marrow sections 21 . Another study revealed that in patients with MDS, azacytidine-responsive patients had higher basal GATA1 mRNA expression in bone marrow mononuclear cells than did patients with stable and progressive disease, suggesting that baseline GATA1 mRNA expression may predict the response to azacytidine treatment 22 . Our study aimed to explore the correlations between GATA1 expression levels and dysmegakaryopoiesis in patients with MDS and found that decreased GATA1 expression levels of megakaryocytes were significantly associated with TP53 mutations, abnormal karyotypes and dysmegakaryopoiesis in MDS. Materials and methods Subjects MDS was diagnosed according to the 2016 World Health Organization (WHO) classification 23 . The MDS subtypes were classified (or reclassified) according to the 2016 WHO classification, 2022 WHO classification and 2022 International Consensus Classification (ICC) of myeloid neoplasms 1 , 3 , 23 . A total of 90 patients with MDS who provided written consent for the use of samples of bone marrow biopsy for research were recruited. Blood cell count, cytogenetic, TP53 mutation status and bone marrow biopsy data were obtained from these patients at diagnosis. Overall survival (OS) was calculated from the date of diagnosis to the date of death or last follow-up. The last follow-up was on May 19, 2024 and 11 patients were lost to follow-up. The study was approved by the Ethics Committees of the Institute of Hematology, Chinese Academy of Medical Science (CAMS) and Peking Union Medical College (PUMC) according to the guidelines of the Declaration of Helsinki. Immunohistochemical staining for GATA1 expression. Paraffin-embedded bone marrow biopsy samples at diagnosis of 90 patients with MDS were obtained. Immunohistochemical dual staining with CD61 and GATA1 was performed on 3 µm-thick paraffin sections. Rabbit anti-GATA1 monoclonal antibodies (Cell Signaling Technology, D52H6) and mouse anti-CD61 monoclonal antibodies (Absin, abs149828) were used to perform immunohistochemical dual staining. Patients whose bone marrow biopsy paraffin section included less than 20 megakaryocytes were excluded from the data analyses. Therefore, the final analyses included data from only 80 patients. Clinical and laboratory features of the 80 patients were documented in Supplementary Table 1. We rated GATA1 expression (brown-colored nuclei) intensity into four levels from 3 to 0: strong, 3; moderate, 2; weak, 1; and negative, 0 14 (Fig. 1 A). The percentages of GATA1-positive (strong/moderate/weak) megakaryocytes were classified into five grades: 81–100%, 4; 50–80%, 3; 21–50%, 2; 1–20%, 1; and < 1%, 0. By multiplying the GATA1 expression level scores and positive megakaryocyte percentage scores, we assigned each patient a GATA1 score 14 . Classification of megakaryocytes We classified megakaryocytes into five categories according to their morphology: normal megakaryocytes, hypolobulated or monolobar megakaryocytes, multinucleated megakaryocytes, micromegakaryocytes and hyperlobulated megakaryocytes 4 , 24 – 26 (Fig. 1 B-F). Additionally, hypolobulated or monolobar megakaryocytes, multinucleated megakaryocytes, micromegakaryocytes, and hyperlobulated megakaryocytes are collectively referred to as dysplastic megakaryocytes. Assays of the GATA1 phenotype and cytogenetics status of the same megakaryocyte To explore the correlation between the GATA1 phenotype and cytogenetic abnormalities in the same megakaryocyte, we used a combination of immunohistochemical staining and fluorescence in situ hybridization (FISH) on the same megakaryocyte sequentially, namely, the morphology antibody chromosome (MAC) technique 27 . Fresh bone marrow smears from another 6 MDS patients with abnormal karyocytes, including del(5/5q), del(7/7q), + 8, or del(20q), were obtained at diagnosis (Supplementary Table 2). First, immunohistochemical dual staining with mouse anti-CD41 monoclonal antibodies (Santa Cruz Biotechnology, sc-365938) and rabbit anti-GATA1 monoclonal antibodies (Cell Signaling Technology, D52H6) was performed on bone marrow smears. According to the intensity of GATA1 staining (red-colored nuclei), we classified megakaryocytes into two categories: GATA1-positive (Fig. 2 A) and GATA1-negative megakaryocytes (Fig. 2 B). The stained area of the bone marrow smear was scanned for images of megakaryocytes. Anhydrous ethanol and fixation using methanol and acetic acid at a 3:1 ratio were used to remove reaction products of immunohistochemical staining. Subsequently, FISH using DNA probes to detect del(5/5q), + 8, del(7/7q) and del(20q) was performed on the same bone marrow smear to analyze cytogenetic abnormalities. Finally, we obtained images of immunohistochemical dual staining and FISH of the same megakaryocyte via MAC technique (Fig. 2 A-B). Statistical analysis Medians and interquartile ranges (IQRs) were used to describe continuous variables. Counts and relative frequencies were used to present nominal variables. Comparisons of continuous variables among different groups were analyzed using the Mann–Whitney U test or Kruskal‒Wallis test. Nominal variables among different groups were compared using the Pearson chi-square test or Fisher’s exact test. Correlations between two continuous variables were evaluated by Pearson correlation analysis. Univariable and multivariable analyses were performed using Cox proportional hazard regression analyses. A two-tailed P value < 0.05 was considered significant. Statistical analyses were performed with SPSS software version 26.0 (IBM, Armonk, N.Y., USA) and GraphPad Prism version 9.0 (GraphPad Inc., San Diego, CA, USA). Results Associations between GATA1 scores and clinical and laboratory variables According to the 2016 WHO classification, 7(8.8%), 19 (23.8%), 6 (7.5%), 1 (1.3%), 24 (30.0%), 19 (23.8%), and 4 (5.0%) patients were diagnosed with MDS with single lineage dysplasia, MDS with multilineage dysplasia, MDS with ring sideroblasts, MDS with isolated del(5q), MDS with excess blasts-1, MDS with excess blasts-2 and MDS, unclassifiable 23 . There was no significant difference in GATA1 scores among the different MDS subtypes ( P = 0.702, Supplementary Fig. 1A and Table 1 ), which was the same for the different MDS subtypes classified according to the 2022 WHO classification ( P = 0.120) and 2022 ICC ( P = 0.096, Supplementary Fig. 1B-C and Table 1 ) 1 , 3 . Table 1 Comparisons of GATA1 scores among different subgroups in 80 patients with MDS. Variables Patients, n (%) GATA1 score in patients, Median (IQRs) P value 2016 WHO classification 0.702 MDS-SLD 7 (8.8) 8.0 (3.0–12.0) MDS-MLD 19 (23.8) 6.0 (3.0–12.0) MDS-RS 6 (7.5) 11.0 (5.3–12.0) MDS-5q- 1 (1.3) 8.0 (/) MDS-EB1 24 (30.0) 7.5 (3.3–12.0) MDS-EB2 19 (23.8) 4.0 (3.0–12.0) MDS-U 4 (5.0) 3.5 (3.0–7.0) 2022 WHO classification 0.120 MDS-5q 1 (1.3) 8.0 (/) MDS- SF3B1 5 (6.3) 10.0 (5.5–12.0) MDS-bi TP53 22 (27.5) 3.5 (3.0–8.0) MDS-LB 22 (27.5) 8.0 (3.0–12.0) MDS-IB1 11 (13.8) 12.0 (6.0–12.0) MDS-IB2 3 (3.8) 12.0 (/) MDS-f 14 (17.5) 6.0 (3.0–12.0) AML with NPM1 mutation 2 (2.5) 7.5 (/) 2022 ICC 0.096 MDS with del(5q) 1 (1.3) 8 (/) MDS with mutated SF3B1 1 (1.3) 3.0 (/) MDS with mutated TP53 22 (27.5) 3.5 (2.8–6.5) MDS, NOS-SLD 7 (8.8) 8.0 (4.0–12.0) MDS, NOS-MLD 13 (16.3) 10.0 (4.5–12.0) MDS-EB 22 (27.5) 11.0 (3.8–12.0) MDS/AML with mutated TP53 5 (6.3) 4.0 (2.0–10.0) MDS/AML, myelodysplasia-related gene mutations 7 (8.8) 8.0 (3.0–12.0) MDS/AML, myelodysplasia-related cytogenetic abnormalities 1 (1.3) 12.0 (/) AML with mutated NPM1 1 (1.3) 3.0 (/) Myelofibrosis grade 0.215 MF-0/1 39 (48.8) 8.0 (3.0–12.0) MF-2/3 41 (51.2) 6.0 (3.0–10.0) Karyotype category 0.006 Complex karyotype 36 (46.8) 5.0 (3.0-8.8) Noncomplex karyotype 41 (53.2) 10.0 (4.0–12.0) Karyotype category 0.024 Abnormal karyotype 54 (70.1) 6.0 (3.0-10.5) Normal karyotype 23 (29.9) 12.0 (4.0–12.0) Blasts percentage 0.770 Low blasts* 37 (46.3) 8.0 (3.0–12.0) Increased blasts* 43 (53.8) 6.0 (3.0–12.0) TP53 mutation status < 0.001 TP53 -mutated 36 (45) 3.0 (2.3-6.0) TP53 -wildtype 44 (55) 12.0 (6.5–12.0) *Low blasts, blasts < 5% in bone marrow and blasts < 2% in peripheral blood; Increased blasts, ≥ 5% blasts in bone marrow or ≥ 2% blasts in peripheral blood. Abbreviations: MDS, myelodysplastic syndromes; IQRs, interquartile ranges; WHO, World Health Organization; MDS-SLD, MDS with single lineage dysplasia; MDS-MLD, MDS with multilineage dysplasia; MDS-RS, MDS with ring sideroblasts; MDS-5q-, MDS with isolated 5q deletion; MDS-EB1/2, MDS with excess blasts type 1/2; MDS-U, MDS unclassifiable; MDS-5q, MDS with low blasts and isolated 5q deletion; MDS- SF3B1 , MDS with low blasts and SF3B1 mutation; MDS-bi TP53 , MDS with biallelic TP53 inactivation; MDS-LB, MDS with low blasts; MDS-IB1/2, MDS with increased blasts type1/2; MDS-f, MDS with fibrosis; AML, acute myeloid leukemia; ICC, International Consensus Classification; NOS, not otherwise specified; NOS-SLD, NOS with single lineage dysplasia; NOS-MLD, NOS with multilineage dysplasia; MDS-EB, MDS with excess blasts; MDS/AML, myelodysplastic syndrome/acute myeloid leukemia; MF, myelofibrosis. We found a positive correlation between GATA1 scores and reticulocyte count ( r = 0.207, P = 0.036), while no significant correlation was found between GATA1 scores and hemoglobin, mean corpuscular volume or red cell distribution width (Fig. 3 ). A correlation test revealed a positive correlation between the GATA1 score and the mean platelet volume ( r = 0.307, P = 0.034) or platelet distribution width ( r = 0.363, P = 0.010) (Fig. 3 ). Using the median GATA1 score as a cutoff value, we classified patients into two groups: the GATA1 low-expressed group (GATA1 scores < 7 points) and the GATA1 high-expressed group (GATA1 scores ≥ 7 points). Compared with the GATA1 low-expressed group, the GATA1 high-expressed group had a higher platelet count ( P = 0.028), higher mean platelet volume ( P = 0.027), wider platelet distribution width ( P = 0.013) and higher reticulocyte count ( P = 0.002) (Table 2 ). Table 2 Comparison of clinical parameters between GATA1 high-expressed and GATA1 low-expressed group. Variables GATA1 low-expressed group* GATA1 high-expressed group* P value n (%) 40 (50%) 40 (50%) BM blasts (%), median (IQRs) 3.0 (1.0–6.0) 3.5 (1.0-6.9) 0.460 HB (g/L), median (IQRs) 76.5 (65.5–82.5) 79.5 (65.3–96.5) 0.181 MCV (fL), median (IQRs) 94.3 (89.3–96.7) 96.0 (90.5-104.9) 0.069 RDW-CV (%), median (IQRs) 16.5 (15.3–18.8) 16.5 (14.7–20.3) 0.855 RDW-SD (fL), median (IQRs) 56.0 (50.9–62.4) 59.7 (50.0-67.5) 0.165 RET (×10E + 12/L), median (IQRs) 0.0215 (0.0121–0.0336) 0.0539 (0.0222–0.1182) 0.002 PLT (×10E + 9/L), median (IQRs) 47.5 (25.3–63..8) 71.5 (42.3-122.3) 0.028 MPV (fL), median (IQRs) 11.0 (10.4–12.1) 12.1 (10.8–12.7) 0.027 PDW (fL), median (IQRs) 11.5 (10.4–14.1) 15.0 (11.8–17.7) 0.013 *GATA1 low-expressed group, GATA1 scores < 7 points; GATA1 high-expressed group, GATA1 scores ≥ 7 points. Abbreviations: IQRs, interquartile ranges; BM, bone marrow; HB, hemoglobin; MCV, mean corpuscular volume; fL, femtoliter; RDW-CV, red cell distribution width-coefficient of variation; RDW-SD, red cell distribution-standard deviation; RET, reticulocyte count; PLT, platelet; MPV, mean platelet volume; PDW, platelet distribution width. Cytogenetic data were available for 77 patients. A total of 54 (70.1%) and 36 (46.8%) patients in our cohort displayed abnormal karyotypes and complex karyotypes, respectively. GATA1 scores were significantly lower in patients with abnormal karyotypes (abnormal versus normal karyotypes, 6.0 [3.0-10.5] versus 12.0 [4.0–12.0], P = 0.024) or complex karyotypes (complex versus noncomplex karyotypes, 5.0 [3.0-8.8] versus 10.0 [4.0–12.0], P = 0.006) (Table 1 ). A total of 36 (45%) patients had TP53 mutations, and TP53 -mutated patients had lower GATA1 scores than TP53 -wildtype patients did (3.0 [2.3-6.0] versus 12.0 [6.5–12.0], P < 0.001) (Table 1 ). Moreover, we classified patients into two categories based on the subtypes classified by the 2022 WHO classification: MDS with biallelic TP53 inactivation (22 [27.5%]) and other subtypes (58 [72.5%]). We found that GATA1 scores in MDS patients with biallelic TP53 inactivation were significantly lower (3.5 [3.0–8.0] versus 8.0 [3.8–12.0], P = 0.007). According to 2022 ICC of myeloid neoplasm, we reclassified patients into two subgroups: myeloid neoplasms with mutated TP53 (27 [33.8%]), including MDS with mutated TP53 and MDS/acute myeloid leukemia (AML) with mutated TP53 , and other subtypes (53 [66.3%]). GATA1 scores of patients with myeloid neoplasms with mutated TP53 were much lower than that of patients with other subtypes defined by 2022 ICC (4.0 [3.0–8.0] versus 9.0 [3.5–12.0], P = 0.001). GATA1 expression levels in megakaryocytes with different morphologic subgroups. Dysplastic megakaryocytes displayed lower GATA1 expression levels than did normal megakaryocytes (1.0 [0–2.0] versus 3.0 [2.0–3.0], P < 0.001) (Fig. 4 A). Compared with those in normal megakaryocytes, the GATA1 expression levels were significantly lower in hypolobulated or monolobar megakaryocytes ( P < 0.001), multinucleated megakaryocytes ( P < 0.001) and micromegakaryocytes ( P < 0.001) (Fig. 4 B). However, hyperlobulated megakaryocytes exhibited higher GATA1 expression levels than did normal megakaryocytes ( P < 0.001). Among the different kinds of dysplastic megakaryocytes, micromegakaryocytes exhibited the lowest GATA1 expression levels ( P < 0.001) (Fig. 4 B). Moreover, we compared the GATA1 expression levels in megakaryocytes from patients in subgroups classified according to the myelofibrosis grade, karyotype, percentage of bone marrow blasts and TP53 mutation status. The GATA1 expression levels in megakaryocytes were lower in patients with abnormal karyotypes (abnormal versus normal karyotypes 2.0 [1.0–3.0] versus 3.0 [1.0–3.0], P < 0.001), complex karyotypes (complex versus noncomplex karyotypes 1.0 [0–3.0] versus 2.0 [1.0–3.0], P < 0.001) or TP53 mutations ( TP53 -mutated versus TP53 -wildtype 1.0 [0–2.0] versus 3.0 [1.0–3.0], P < 0.001), which was also the case for the comparisons of the GATA1 scores. Compared with those from patients with myelofibrosis grade 0/1, megakaryocytes from patients with myelofibrosis grade 2/3 had lower GATA1 expression levels ( P = 0.046), and the difference between the two subgroups was also significant for normal megakaryocytes ( P < 0.001). GATA1 expression level and cytogenetic abnormalities of the same megakaryocyte. We explored the correlation between GATA1 expression level and cytogenetic abnormalities of the same megakaryocytes using MAC technique. Images of immunohistochemical dual staining and FISH were obtained from the same megakaryocytes (Fig. 2 A-B). A total of 53 megakaryocytes from 6 patients were included in the analysis. We found that GATA1-negative megakaryocytes showed higher frequencies of abnormal cytogenetics (87.5% versus 47.6%, P = 0.002, Fig. 2 C), suggesting that decreased GATA1 expression level in megakaryocytes was closely related to clonal abnormalities in MDS. Impact of GATA1 expression level of megakaryocytes in prognosis of MDS We found that the OS of GATA1 low-expressed group was significantly shorter than that of GATA1 high-expressed group (median OS, 12 months [95%CI, 0–25 months] versus 35 months [95%CI, 17–53 months], P = 0.026) (Supplementary Fig. 2). To explore the significance of GATA1 expression level in prognosis of MDS, we included revised International Prognostic Scoring System (IPSS-R) risk category and GATA1 scores in multivariable analysis of OS, and the results showed that both IPSS-R higher-risk category (IPSS-R scores > 3.5 points) ( P = 0.011) and lower GATA1 scores ( P = 0.005) were significantly associated with OS in MDS (Supplementary Table 3). Discussion The transcription factor GATA1 plays a critical role in erythropoiesis and megakaryopoiesis. GATA1 deficiency can block megakaryocyte differentiation and maturation, contributing to immature and small megakaryocytes and thrombocytopenia 13 . Our results showed that MDS patients in the GATA1 low-expressed group had lower platelet counts, suggesting that thrombocytopenia in MDS patients was associated with decreased GATA1 expression level. In our study, patients in the GATA1 low-expressed group showed smaller mean platelet volumes, while previous studies revealed that GATA1 deficiency in megakaryocytes in mice could result in fewer but enlarged platelets in vivo 6 , 9 , 28 . Most of these studies were based on GATA1 -knockdown or GATA1 -mutant mouse models. However, no patients with MDS in our study were detected with GATA1 mutation. We speculated that the differences in mean platelet volume may be attributed to different mechanisms of GATA1 deficiency in MDS. Although there was no significant correlation between GATA1 scores in patients and hemoglobin in our cohort, reticulocyte counts were positively correlated with GATA1 scores, which could also indicate that GATA1 expression levels were associated with erythropoiesis. Patients with abnormal karyotypes or complex karyotypes had lower GATA1 scores. Furthermore, we explored the correlation between clonal abnormalities and GATA1 expression level of the same megakaryocytes using MAC technique. Abnormal or complex karyotypes were associated with a worse prognosis in MDS. Our results suggested that decreased GATA1 expression levels in megakaryocytes may be correlated with clonal abnormalities and prognosis in MDS patients. In a murine erythroleukemia cell line, Trainor et al . reported that the DNA-binding domain of GATA1 can interact with the transactivation domain of p53, contributing to reciprocal inhibition between GATA1 and p53 29 . Previous studies revealed that GATA1 expression levels in megakaryocytes in primary myelofibrosis was downregulated, while microarray analysis suggested that the p53 pathway was activated in megakaryocytes in primary myelofibrosis, which was confirmed by quantitative reverse-transcriptase PCR 12 , 14 , 15 . In our study, the GATA1 scores and GATA1 expression levels of megakaryocytes in TP53 -mutated patients were significantly lower than those in TP53 -wildtype patients. Although the differences in GATA1 expression level among other subtypes of MDS were not significant, the GATA1 scores in MDS patients with biallelic TP53 inactivation were lower than those in the other subtypes. Previous studies suggested that decreased GATA1 expression level was associated with dysplasia of megakaryocytes in MDS patients 20 , 21 . In our study, compared with normal megakaryocytes, dysplastic megakaryocytes, including hypolobulated or monolobar megakaryocytes, multinucleated megakaryocytes and micromegakaryocytes, had lower GATA1 expression levels, while hyperlobulated megakaryocytes had higher GATA1 expression levels. Our data also indicated that decreased GATA1 expression level was associated with dysmegakaryopoiesis in MDS patients. Among the dysplastic megakaryocytes, the GATA1 expression levels in the micromegakaryocytes were lower than those in the other kinds of dysplastic megakaryocytes. Previous studies found that micromegakaryocytes in MDS were morphologically mature but had impaired polyploidization 30 , 31 , indicating a degree of differentiation arrest. The lowest expression levels of GATA1 in micromegakaryocytes may be attributed to the combined effects of dysplasia and differentiation arrest. Compared with that in healthy controls, essential thrombocythemia or polycythemia vera, GATA1 expression level in primary myelofibrosis patients was significantly downregulated 12 , 14 , 15 . Moreover, researchers found that GATA1 expression level decreased in patients previously diagnosed with BCR/ABL -negative MPN during leukemia transformation 14 . In BCR/ABL -negative MPN, decreased GATA1 expression level can cause impaired megakaryopoiesis and induce myelofibrosis 12 , 16 , 17 . Many studies have revealed that bone marrow fibrosis indicates an adverse prognosis in MDS patients 32 – 34 . The GATA1 scores in patients with myelofibrosis grade 2/3 were lower than those in patients with myelofibrosis grade 0/1, but the difference was not significant. However, the GATA1 expression levels in megakaryocytes were significantly lower in patients with grade 2/3 myelofibrosis. Our data indicated that GATA1 may play a role in myelofibrosis in MDS patients, but the underlying mechanism remains unclear. OS of GATA1 low-expressed group was significantly shorter than that of GATA1 high-expressed group. Furthermore, we included the GATA1 score as a continuous variable in multivariable analyses of overall survival (OS) and found that decreased GATA1 expression level was an independent adverse prognostic factor, regardless of IPSS-R risk categories in MDS patients. The main limitations of the study include the following: (ⅰ) the subjects included in the study were not consecutive, (ⅱ) the number of bone marrow biopsy or bone marrow smear samples was relatively small, and (ⅲ) the assessment of GATA1 expression levels was semiquantitative and carried a degree of subjectivity. Conclusions Insufficient GATA1 expression was closely associated with dysmegakaryopoiesis in patients with MDS. Decreased GATA1 expression levels in megakaryocytes were positively correlated with TP53 mutations, abnormal or complex karyotypes and myelofibrosis in MDS. Downregulation of GATA1 expression level in megakaryocytes may predict adverse prognosis in MDS. Our results suggested that downregulation of GATA1 expression possibly plays a critical role in the pathogenesis of MDS. Declarations Author contributions ZJ.X designed the study. YD.Z and FH.L performed the experiments and analyzed the data. CW.L, Q.S and JQ.L provided support for the experiments. TJ.Q, ZF.X, B.L, SQ.Q, LJ.P, QY.G, and M.J recruited patients and collected clinical data. FH.L and ZJ.X prepared the manuscript. All authors read, approved the final manuscript, and agreed to submit for publication. Acknowledgments The authors would like to thank all participants for their contribution and time investment in the study. Funding statement This study is supported in part by CAMS Initiative Fund for Medical Sciences (2022-I2M-1-022), National Natural Science Foundation of China (82170139，82070134) and Clinical research fund from National Clinical Research Centre for Blood Diseases (2023NCRCA0117, 2023NCRCA0103). Conflict of interest The authors have no relevant conflicts of interest to declare about the manuscript. Data availability statement The datasets supporting the findings of this study are available from the corresponding authors upon reasonable request. 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Tsai SF, Martin DI, Zon LI, D'Andrea AD, Wong GG, Orkin SH. Cloning of cDNA for the major DNA-binding protein of the erythroid lineage through expression in mammalian cells. Nature 1989; 339(6224): 446–451. Juban G, Sakakini N, Chagraoui H, Cruz Hernandez D, Cheng Q, Soady K, et al. Oncogenic Gata1 causes stage-specific megakaryocyte differentiation delay. Haematologica 2021; 106(4): 1106–1119. Gilles L, Arslan AD, Marinaccio C, Wen QJ, Arya P, McNulty M, et al. Downregulation of GATA1 drives impaired hematopoiesis in primary myelofibrosis. J Clin Invest 2017; 127(4): 1316–1320. Vyas P, Ault K, Jackson CW, Orkin SH, Shivdasani RA. Consequences of GATA-1 deficiency in megakaryocytes and platelets. Blood 1999; 93(9): 2867–2875. Yang N, Park S, Cho MS, Lee M, Hong KS, Mun YC, et al. GATA1 Expression in BCR/ABL1-negative Myeloproliferative Neoplasms. Ann Lab Med 2018; 38(4): 296–305. Sangiorgio VFI, Nam A, Chen Z, Orazi A, Tam W. GATA1 downregulation in prefibrotic and fibrotic stages of primary myelofibrosis and in the myelofibrotic progression of other myeloproliferative neoplasms. Leuk Res 2021; 100: 106495. Zingariello M, Sancillo L, Martelli F, Ciaffoni F, Marra M, Varricchio L, et al. The thrombopoietin/MPL axis is activated in the Gata1(low) mouse model of myelofibrosis and is associated with a defective RPS14 signature. Blood Cancer J 2017; 7(6): e572. Gangat N, Tefferi A. Myelofibrosis biology and contemporary management. Br J Haematol 2020; 191(2): 152–170. Tefferi A. Pathogenesis of myelofibrosis with myeloid metaplasia. J Clin Oncol 2005; 23(33): 8520–8530. Ling T, Crispino JD, Zingariello M, Martelli F, Migliaccio AR. GATA1 insufficiencies in primary myelofibrosis and other hematopoietic disorders: consequences for therapy. Expert Rev Hematol 2018; 11(3): 169–184. Fadilah SA, Cheong SK, Roslan H, Rozie-Hanisa M, Yen GK. GATA-1 and GATA-2 gene expression is related to the severity of dysplasia in myelodysplastic syndrome. Leukemia 2002; 16(8): 1563–1565. Kim H, Lee MK, Kim HR. Difference in megakaryocyte expression of GATA-1, IL-6, and IL-8 associated with maintenance of platelet counts in patients with plasma cell neoplasm with dysmegakaryopoiesis. Exp Hematol 2019; 73: 13–17.e12. Falconi G, Fabiani E, Criscuolo M, Fianchi L, Finelli C, Cerqui E, et al. Transcription factors implicated in late megakaryopoiesis as markers of outcome after azacitidine and allogeneic stem cell transplantation in myelodysplastic syndrome. Leuk Res 2019; 84: 106191. Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016; 127(20): 2391–2405. Weinberg OK, Pozdnyakova O, Campigotto F, DeAngelo DJ, Stone RM, Neuberg D, et al. Reproducibility and prognostic significance of morphologic dysplasia in de novo acute myeloid leukemia. Mod Pathol 2015; 28(7): 965–976. Weinberg OK, Gibson CJ, Blonquist TM, Neuberg D, Pozdnyakova O, Kuo F, et al. Association of mutations with morphological dysplasia in de novo acute myeloid leukemia without 2016 WHO Classification-defined cytogenetic abnormalities. Haematologica 2018; 103(4): 626–633. Goasguen JE, Bennett JM, Bain BJ, Brunning RD, Vallespí MT, Tomonaga M, et al. Quality control initiative on the evaluation of the dysmegakaryopoiesis in myeloid neoplasms: Difficulties in the assessment of dysplasia. Leuk Res 2016; 45: 75–81. Knuutila S, Mustjoki S. Morphology antibody chromosome technique for determining phenotype and genetic status of the same cell. Curr Protoc Hum Genet 2012; Chap. 4: Unit4.7. Shivdasani RA, Fujiwara Y, McDevitt MA, Orkin SH. A lineage-selective knockout establishes the critical role of transcription factor GATA-1 in megakaryocyte growth and platelet development. Embo j 1997; 16(13): 3965–3973. Trainor CD, Mas C, Archambault P, Di Lello P, Omichinski JG. GATA-1 associates with and inhibits p53. Blood 2009; 114(1): 165–173. Kobayashi Y, Uoshima N, Kimura S, Tanaka K, Wada K, Ozawa M, et al. Relationship between morphological classification of the degree of maturation and the ploidy of micromegakaryocytes in myelodysplastic syndrome patients. Int J Hematol 1995; 61(3): 117–122. Kobayashi Y, Ozawa M, Maruo N, Kondo M. Megakaryocytic ploidy in myelodysplastic syndromes. Leuk Lymphoma 1993; 9(1–2): 55–61. Buesche G, Teoman H, Wilczak W, Ganser A, Hecker H, Wilkens L, et al. Marrow fibrosis predicts early fatal marrow failure in patients with myelodysplastic syndromes. Leukemia 2008; 22(2): 313–322. Della Porta MG, Malcovati L, Boveri E, Travaglino E, Pietra D, Pascutto C, et al. Clinical relevance of bone marrow fibrosis and CD34-positive cell clusters in primary myelodysplastic syndromes. J Clin Oncol 2009; 27(5): 754–762. Fu B, Jaso JM, Sargent RL, Goswami M, Verstovsek S, Medeiros LJ, et al. Bone marrow fibrosis in patients with primary myelodysplastic syndromes has prognostic value using current therapies and new risk stratification systems. Mod Pathol 2014; 27(5): 681–689. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4488001","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":307784864,"identity":"3c38d8c4-0c5e-4f05-ac73-039a81484cc7","order_by":0,"name":"Zhijian Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFCCA0DIYMNjQKqWNJK0gMFhBuK1yDeeMTzwo+K8jDn74acbfzDYyTOwnz2AVwtjw7GEgz1nbvNY9qSZ3eZhSDZs4MlLwKuFmeHwgQO8bbd5DG4wmN0G8hMYJAj4i43hYMPBv23ngFrYv938wVBPWAsP0JbDvG0HgFp4zG4AeYS1SDAcSzgscyaZx+BMThnQeccN23hy8GuRn3HG+OObCjt7g+PHt938UVEtz89+hkCASxxA5hmAfEcI8DcQVDIKRsEoGAUjHQAAb/FEeEEgTwEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1099-4489","institution":"Institute of Hematology and Blood Diseases Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhijian","middleName":"","lastName":"Xiao","suffix":""},{"id":307784865,"identity":"1e83fefb-69f4-4c20-bc31-b501d6491a83","order_by":1,"name":"Fuhui Li","email":"","orcid":"","institution":"Institute of Hematology and Blood Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fuhui","middleName":"","lastName":"Li","suffix":""},{"id":307784866,"identity":"9b7f9626-10cf-4043-8f51-eca84e502144","order_by":2,"name":"Yudi Zhang","email":"","orcid":"","institution":"Institute of Hematology and Blood Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yudi","middleName":"","lastName":"Zhang","suffix":""},{"id":307784867,"identity":"7181a287-71b3-4fd8-aa72-a8a573f8e69d","order_by":3,"name":"Chengwen Li","email":"","orcid":"","institution":"Institute of Hematology and Blood Disease Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Tianjin, 300020, China","correspondingAuthor":false,"prefix":"","firstName":"Chengwen","middleName":"","lastName":"Li","suffix":""},{"id":307784868,"identity":"742c8ad4-ae71-4d92-a37c-796a28461545","order_by":4,"name":"Qi Sun","email":"","orcid":"","institution":"Hematological hospital of Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Sun","suffix":""},{"id":307784869,"identity":"3e0ec439-216b-4dfb-8749-46261d985f94","order_by":5,"name":"Jinqin Liu","email":"","orcid":"","institution":"Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jinqin","middleName":"","lastName":"Liu","suffix":""},{"id":307784870,"identity":"4908ae00-3902-4e00-80c2-7761dcee7fa4","order_by":6,"name":"Zefeng Xu","email":"","orcid":"https://orcid.org/0009-0001-0147-2291","institution":"Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zefeng","middleName":"","lastName":"Xu","suffix":""},{"id":307784871,"identity":"78c7b179-12fc-44a5-bed7-58f1b4068285","order_by":7,"name":"Bing Li","email":"","orcid":"","institution":"Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Li","suffix":""},{"id":307784872,"identity":"06774d39-db87-4e45-993e-84ff131c3507","order_by":8,"name":"Shiqiang Qu","email":"","orcid":"","institution":"Institute of Hematology and Blood Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shiqiang","middleName":"","lastName":"Qu","suffix":""},{"id":307784873,"identity":"fa9abac3-9fb6-4e27-9b0f-c3de17467d58","order_by":9,"name":"Lijuan Pan","email":"","orcid":"","institution":"Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Pan","suffix":""},{"id":307784874,"identity":"e868cd4f-826a-419d-8803-1b29cf40fd2b","order_by":10,"name":"QINGYAN GAO","email":"","orcid":"","institution":"Institute of Hematology \u0026 Blood Diseases Hospital, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"QINGYAN","middleName":"","lastName":"GAO","suffix":""},{"id":307784875,"identity":"0d88c005-6d69-41e9-8688-1e6bcae33c1f","order_by":11,"name":"Meng Jiao","email":"","orcid":"","institution":"Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Jiao","suffix":""},{"id":307784876,"identity":"dae3ac7f-c385-4500-9b84-76f2e9601e34","order_by":12,"name":"Tiejun Qin","email":"","orcid":"","institution":"Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Tiejun","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2024-05-28 04:20:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4488001/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4488001/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58229604,"identity":"bfcd65d6-ea2f-494e-a1bc-2ccb75499305","added_by":"auto","created_at":"2024-06-12 19:16:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":455983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunohistochemical dual staining with CD61 and GATA1 on paraffin-embedded bone marrow biopsy sections.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Megakaryocytes with different GATA1 expression levels. Red arrow: strong; green arrow: moderate; yellow arrow: weak; blue arrow: negative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB-F. \u003c/strong\u003eMegakaryocytes with different morphologies.\u003c/p\u003e","description":"","filename":"fIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-4488001/v1/b7d65d1c3d74d9a5984ee505.png"},{"id":58231374,"identity":"ba6eaac4-831a-4908-848f-e85e20656bac","added_by":"auto","created_at":"2024-06-12 19:24:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":185627,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunohistochemical dual staining with CD41 and GATA1 and cytogenetic abnormalities of the same megakaryocytes. \u003c/strong\u003e**\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e The image of immunohistochemical dual staining and FISH of a GATA1-positive megakaryocyte.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e The image of immunohistochemical dual staining and FISH of a GATA1-negative megakaryocyte.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e Comparison of karyotype analyzed by FISH between GATA1-positive and GATA1-negative megakaryocytes.\u003c/p\u003e\n\u003cp\u003eAbbreviations: FISH, fluorescence in situ hybridization.\u003c/p\u003e","description":"","filename":"fIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-4488001/v1/48636d386bbef7258cb763ef.png"},{"id":58229607,"identity":"6db3dc22-e83a-4ab7-8fbe-c4df45e56e32","added_by":"auto","created_at":"2024-06-12 19:16:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between GATA1 scores in patients with MDS and the PLT, MPV, PDW, HB, MCV, RDW-CV, RDW-SD, or RET.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: MDS, myelodysplastic syndromes; PLT, platelet count; MPV, mean platelet volume; PDW, platelet distribution width; HB, hemoglobin; MCV, mean corpuscular volume; RDW-CV, red cell distribution width coefficient of variation; RDW-SD, red cell distribution standard deviation; RET, reticulocyte count.\u003c/p\u003e","description":"","filename":"fIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-4488001/v1/8919a36e54cf6b62b04b133a.png"},{"id":58229608,"identity":"6344138d-9036-431a-96cf-9b41b2dc9ca2","added_by":"auto","created_at":"2024-06-12 19:16:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons of GATA1 expression levels in megakaryocytes of different morphologies\u003c/strong\u003e. ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ns, not significant. Data are represented as the median (IQR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e GATA1 expression levels between normal MKs and dysplastic MKs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e GATA1 expression levels among normal MKs, hypolobated or monolobar MKs, multicleated MKs, micromegakayocytes and hyperbulated MKs.\u003c/p\u003e\n\u003cp\u003eAbbreviations: IQR, interquartile ranges; MKs, megakaryocytes.\u003c/p\u003e","description":"","filename":"fIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-4488001/v1/d22056848cf87d25c8dc8c37.png"},{"id":58732236,"identity":"d60bf6aa-e673-4d2d-9e90-bfbeec36fd25","added_by":"auto","created_at":"2024-06-20 11:33:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1522822,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4488001/v1/ff241d6e-c777-41d6-83e7-55052e6aa902.pdf"},{"id":58229606,"identity":"ac69399e-5ba4-4dab-a6b9-94a76623590b","added_by":"auto","created_at":"2024-06-12 19:16:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":288446,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4488001/v1/6fd1e51f599d5c538c92b8b8.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"GATA1 insufficiencies in dysmegakaryopoiesis of myelodysplastic syndromes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMyelodysplastic syndromes (MDS), a group of heterogeneous myeloid neoplasm, are characterized by morphological dysplasia and peripheral blood cytopenia\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. At present, the MDS subtypes are classified mainly according to cytogenetics, molecular genetics, and bone marrow morphology\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Dysplastic megakaryocytes, including hypolobulated, multinucleated megakaryocytes and micromegakaryocytes, are common in patients with MDS\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Previous studies also suggested that the severity of megakaryocytic dysplasia in MDS patients was associated with survival\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGATA1, which is mainly expressed in the erythroid, megakaryocytic, eosinophil and mast lineages, is a critical transcription factor for hematopoiesis\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. GATA1 plays an essential role in erythropoiesis, megakaryopoiesis and platelet production\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Moreover, GATA1 deficiency in megakaryocytes caused differentiation delay and induced marked hyperproliferation and decreased platelets counts\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Several studies have explored GATA1 expression level in the megakaryocytes of patients with \u003cem\u003eBCR/ABL1-\u003c/em\u003enegative myeloproliferative neoplasms (MPNs) and revealed that GATA1 expression level decreases during fibrotic progression and may be associated with leukemia transformation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. According to previous studies, the downregulation of GATA1 in megakaryocytes in primary myelofibrosis is closely related to impaired megakaryocyte differentiation and myelofibrosis\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough the role of GATA1 deficiency in megakaryocytes in the pathogenesis of myelofibrosis in \u003cem\u003eBCR/ABL1\u003c/em\u003e-negative MPN is relatively clear, related research in MDS is scarce\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Fadilah \u003cem\u003eet al\u003c/em\u003e. suggested that downregulation of \u003cem\u003eGATA1\u003c/em\u003e mRNA expression was closely associated with the severity of erythroid, granulocytic and megakaryocytic dysplasia in MDS patients\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Decreased GATA1 expression was identified in megakaryocytes in 34 MDS patients with dysmegakaryopoiesis using immunohistochemical staining with GATA1 on paraffin-embedded bone marrow sections\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Another study revealed that in patients with MDS, azacytidine-responsive patients had higher basal \u003cem\u003eGATA1\u003c/em\u003e mRNA expression in bone marrow mononuclear cells than did patients with stable and progressive disease, suggesting that baseline \u003cem\u003eGATA1\u003c/em\u003e mRNA expression may predict the response to azacytidine treatment\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study aimed to explore the correlations between GATA1 expression levels and dysmegakaryopoiesis in patients with MDS and found that decreased GATA1 expression levels of megakaryocytes were significantly associated with \u003cem\u003eTP53\u003c/em\u003e mutations, abnormal karyotypes and dysmegakaryopoiesis in MDS.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eMDS was diagnosed according to the 2016 World Health Organization (WHO) classification\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The MDS subtypes were classified (or reclassified) according to the 2016 WHO classification, 2022 WHO classification and 2022 International Consensus Classification (ICC) of myeloid neoplasms\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. A total of 90 patients with MDS who provided written consent for the use of samples of bone marrow biopsy for research were recruited. Blood cell count, cytogenetic, \u003cem\u003eTP53\u003c/em\u003e mutation status and bone marrow biopsy data were obtained from these patients at diagnosis. Overall survival (OS) was calculated from the date of diagnosis to the date of death or last follow-up. The last follow-up was on May 19, 2024 and 11 patients were lost to follow-up. The study was approved by the Ethics Committees of the Institute of Hematology, Chinese Academy of Medical Science (CAMS) and Peking Union Medical College (PUMC) according to the guidelines of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunohistochemical staining for GATA1 expression.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eParaffin-embedded bone marrow biopsy samples at diagnosis of 90 patients with MDS were obtained. Immunohistochemical dual staining with CD61 and GATA1 was performed on 3 \u0026micro;m-thick paraffin sections. Rabbit anti-GATA1 monoclonal antibodies (Cell Signaling Technology, D52H6) and mouse anti-CD61 monoclonal antibodies (Absin, abs149828) were used to perform immunohistochemical dual staining. Patients whose bone marrow biopsy paraffin section included less than 20 megakaryocytes were excluded from the data analyses. Therefore, the final analyses included data from only 80 patients. Clinical and laboratory features of the 80 patients were documented in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eWe rated GATA1 expression (brown-colored nuclei) intensity into four levels from 3 to 0: strong, 3; moderate, 2; weak, 1; and negative, 0\u003csup\u003e14\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The percentages of GATA1-positive (strong/moderate/weak) megakaryocytes were classified into five grades: 81\u0026ndash;100%, 4; 50\u0026ndash;80%, 3; 21\u0026ndash;50%, 2; 1\u0026ndash;20%, 1; and \u0026lt;\u0026thinsp;1%, 0. By multiplying the GATA1 expression level scores and positive megakaryocyte percentage scores, we assigned each patient a GATA1 score\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClassification of megakaryocytes\u003c/h2\u003e \u003cp\u003eWe classified megakaryocytes into five categories according to their morphology: normal megakaryocytes, hypolobulated or monolobar megakaryocytes, multinucleated megakaryocytes, micromegakaryocytes and hyperlobulated megakaryocytes\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-F). Additionally, hypolobulated or monolobar megakaryocytes, multinucleated megakaryocytes, micromegakaryocytes, and hyperlobulated megakaryocytes are collectively referred to as dysplastic megakaryocytes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssays of the GATA1 phenotype and cytogenetics status of the same megakaryocyte\u003c/h2\u003e \u003cp\u003eTo explore the correlation between the GATA1 phenotype and cytogenetic abnormalities in the same megakaryocyte, we used a combination of immunohistochemical staining and fluorescence in situ hybridization (FISH) on the same megakaryocyte sequentially, namely, the morphology antibody chromosome (MAC) technique\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Fresh bone marrow smears from another 6 MDS patients with abnormal karyocytes, including del(5/5q), del(7/7q), +\u0026thinsp;8, or del(20q), were obtained at diagnosis (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eFirst, immunohistochemical dual staining with mouse anti-CD41 monoclonal antibodies (Santa Cruz Biotechnology, sc-365938) and rabbit anti-GATA1 monoclonal antibodies (Cell Signaling Technology, D52H6) was performed on bone marrow smears. According to the intensity of GATA1 staining (red-colored nuclei), we classified megakaryocytes into two categories: GATA1-positive (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and GATA1-negative megakaryocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The stained area of the bone marrow smear was scanned for images of megakaryocytes. Anhydrous ethanol and fixation using methanol and acetic acid at a 3:1 ratio were used to remove reaction products of immunohistochemical staining. Subsequently, FISH using DNA probes to detect del(5/5q), +\u0026thinsp;8, del(7/7q) and del(20q) was performed on the same bone marrow smear to analyze cytogenetic abnormalities. Finally, we obtained images of immunohistochemical dual staining and FISH of the same megakaryocyte via MAC technique (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMedians and interquartile ranges (IQRs) were used to describe continuous variables. Counts and relative frequencies were used to present nominal variables. Comparisons of continuous variables among different groups were analyzed using the Mann\u0026ndash;Whitney U test or Kruskal‒Wallis test. Nominal variables among different groups were compared using the Pearson chi-square test or Fisher\u0026rsquo;s exact test. Correlations between two continuous variables were evaluated by Pearson correlation analysis. Univariable and multivariable analyses were performed using Cox proportional hazard regression analyses. A two-tailed \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Statistical analyses were performed with SPSS software version 26.0 (IBM, Armonk, N.Y., USA) and GraphPad Prism version 9.0 (GraphPad Inc., San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between GATA1 scores and clinical and laboratory variables\u003c/h2\u003e \u003cp\u003eAccording to the 2016 WHO classification, 7(8.8%), 19 (23.8%), 6 (7.5%), 1 (1.3%), 24 (30.0%), 19 (23.8%), and 4 (5.0%) patients were diagnosed with MDS with single lineage dysplasia, MDS with multilineage dysplasia, MDS with ring sideroblasts, MDS with isolated del(5q), MDS with excess blasts-1, MDS with excess blasts-2 and MDS, unclassifiable\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. There was no significant difference in GATA1 scores among the different MDS subtypes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.702, Supplementary Fig.\u0026nbsp;1A and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which was the same for the different MDS subtypes classified according to the 2022 WHO classification (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.120) and 2022 ICC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096, Supplementary Fig.\u0026nbsp;1B-C and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\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\u003eComparisons of GATA1 scores among different subgroups in 80 patients with MDS.\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 \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGATA1 score in patients, Median (IQRs)\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\u003e\u003cb\u003e2016 WHO classification\u003c/b\u003e\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\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS-SLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (3.0\u0026ndash;12.0)\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-MLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;12.0)\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-RS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (5.3\u0026ndash;12.0)\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-5q-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (/)\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-EB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5 (3.3\u0026ndash;12.0)\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-EB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.0\u0026ndash;12.0)\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-U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (3.0\u0026ndash;7.0)\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\u003cb\u003e2022 WHO classification\u003c/b\u003e\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\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS-5q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (/)\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-\u003cem\u003eSF3B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0 (5.5\u0026ndash;12.0)\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-bi\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (3.0\u0026ndash;8.0)\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-LB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (3.0\u0026ndash;12.0)\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-IB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (6.0\u0026ndash;12.0)\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-IB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (/)\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-f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;12.0)\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 with \u003cem\u003eNPM1\u003c/em\u003e mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5 (/)\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\u003e\u003cb\u003e2022 ICC\u003c/b\u003e\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\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS with del(5q)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (/)\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 with mutated \u003cem\u003eSF3B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (/)\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 with mutated \u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (2.8\u0026ndash;6.5)\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\u003eMDS, NOS-SLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (4.0\u0026ndash;12.0)\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, NOS-MLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0 (4.5\u0026ndash;12.0)\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-EB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (3.8\u0026ndash;12.0)\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/AML with mutated \u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (2.0\u0026ndash;10.0)\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/AML, myelodysplasia-related gene mutations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (3.0\u0026ndash;12.0)\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/AML, myelodysplasia-related cytogenetic abnormalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (/)\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 with mutated \u003cem\u003eNPM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (/)\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\u003cb\u003eMyelofibrosis grade\u003c/b\u003e\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\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF-0/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (3.0\u0026ndash;12.0)\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\u003eMF-2/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;10.0)\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\u003cb\u003eKaryotype category\u003c/b\u003e\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\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex karyotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0-8.8)\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\u003eNoncomplex karyotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0 (4.0\u0026ndash;12.0)\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\u003cb\u003eKaryotype category\u003c/b\u003e\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\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal karyotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.0-10.5)\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\u003eNormal karyotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (4.0\u0026ndash;12.0)\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\u003cb\u003eBlasts percentage\u003c/b\u003e\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\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow blasts*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (3.0\u0026ndash;12.0)\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\u003eIncreased blasts*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;12.0)\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\u003cb\u003eTP53\u003c/b\u003e \u003cb\u003emutation status\u003c/b\u003e\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\u003cem\u003eTP53\u003c/em\u003e-mutated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.3-6.0)\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\u003cem\u003eTP53\u003c/em\u003e-wildtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (6.5\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Low blasts, blasts\u0026thinsp;\u0026lt;\u0026thinsp;5% in bone marrow and blasts\u0026thinsp;\u0026lt;\u0026thinsp;2% in peripheral blood; Increased blasts, \u0026ge;\u0026thinsp;5% blasts in bone marrow or \u0026ge;\u0026thinsp;2% blasts in peripheral blood.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: MDS, myelodysplastic syndromes; IQRs, interquartile ranges; WHO, World Health Organization; MDS-SLD, MDS with single lineage dysplasia; MDS-MLD, MDS with multilineage dysplasia; MDS-RS, MDS with ring sideroblasts; MDS-5q-, MDS with isolated 5q deletion; MDS-EB1/2, MDS with excess blasts type 1/2; MDS-U, MDS unclassifiable; MDS-5q, MDS with low blasts and isolated 5q deletion; MDS-\u003cem\u003eSF3B1\u003c/em\u003e, MDS with low blasts and \u003cem\u003eSF3B1\u003c/em\u003e mutation; MDS-bi\u003cem\u003eTP53\u003c/em\u003e, MDS with biallelic \u003cem\u003eTP53\u003c/em\u003e inactivation; MDS-LB, MDS with low blasts; MDS-IB1/2, MDS with increased blasts type1/2; MDS-f, MDS with fibrosis; AML, acute myeloid leukemia; ICC, International Consensus Classification; NOS, not otherwise specified; NOS-SLD, NOS with single lineage dysplasia; NOS-MLD, NOS with multilineage dysplasia; MDS-EB, MDS with excess blasts; MDS/AML, myelodysplastic syndrome/acute myeloid leukemia; MF, myelofibrosis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe found a positive correlation between GATA1 scores and reticulocyte count (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.207, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), while no significant correlation was found between GATA1 scores and hemoglobin, mean corpuscular volume or red cell distribution width (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A correlation test revealed a positive correlation between the GATA1 score and the mean platelet volume (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.307, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) or platelet distribution width (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.363, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Using the median GATA1 score as a cutoff value, we classified patients into two groups: the GATA1 low-expressed group (GATA1 scores\u0026thinsp;\u0026lt;\u0026thinsp;7 points) and the GATA1 high-expressed group (GATA1 scores\u0026thinsp;\u0026ge;\u0026thinsp;7 points). Compared with the GATA1 low-expressed group, the GATA1 high-expressed group had a higher platelet count (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), higher mean platelet volume (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), wider platelet distribution width (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and higher reticulocyte count (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eComparison of clinical parameters between GATA1 high-expressed and GATA1 low-expressed group.\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 \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGATA1 low-expressed group*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGATA1 high-expressed group*\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\u003e\u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (50%)\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\u003eBM blasts (%), median (IQRs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0 (1.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (1.0-6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB (g/L), median (IQRs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.5 (65.5\u0026ndash;82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.5 (65.3\u0026ndash;96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (fL), median (IQRs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.3 (89.3\u0026ndash;96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.0 (90.5-104.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW-CV (%), median (IQRs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.5 (15.3\u0026ndash;18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5 (14.7\u0026ndash;20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW-SD (fL), median (IQRs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.0 (50.9\u0026ndash;62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.7 (50.0-67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRET (\u0026times;10E\u0026thinsp;+\u0026thinsp;12/L), median (IQRs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0215 (0.0121\u0026ndash;0.0336)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0539 (0.0222\u0026ndash;0.1182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (\u0026times;10E\u0026thinsp;+\u0026thinsp;9/L), median (IQRs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.5 (25.3\u0026ndash;63..8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.5 (42.3-122.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPV (fL), median (IQRs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0 (10.4\u0026ndash;12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1 (10.8\u0026ndash;12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDW (fL), median (IQRs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5 (10.4\u0026ndash;14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0 (11.8\u0026ndash;17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*GATA1 low-expressed group, GATA1 scores\u0026thinsp;\u0026lt;\u0026thinsp;7 points; GATA1 high-expressed group, GATA1 scores\u0026thinsp;\u0026ge;\u0026thinsp;7 points.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: IQRs, interquartile ranges; BM, bone marrow; HB, hemoglobin; MCV, mean corpuscular volume; fL, femtoliter; RDW-CV, red cell distribution width-coefficient of variation; RDW-SD, red cell distribution-standard deviation; RET, reticulocyte count; PLT, platelet; MPV, mean platelet volume; PDW, platelet distribution width.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCytogenetic data were available for 77 patients. A total of 54 (70.1%) and 36 (46.8%) patients in our cohort displayed abnormal karyotypes and complex karyotypes, respectively. GATA1 scores were significantly lower in patients with abnormal karyotypes (abnormal \u003cem\u003eversus\u003c/em\u003e normal karyotypes, 6.0 [3.0-10.5] \u003cem\u003eversus\u003c/em\u003e 12.0 [4.0\u0026ndash;12.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) or complex karyotypes (complex \u003cem\u003eversus\u003c/em\u003e noncomplex karyotypes, 5.0 [3.0-8.8] \u003cem\u003eversus\u003c/em\u003e 10.0 [4.0\u0026ndash;12.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA total of 36 (45%) patients had \u003cem\u003eTP53\u003c/em\u003e mutations, and \u003cem\u003eTP53\u003c/em\u003e-mutated patients had lower GATA1 scores than \u003cem\u003eTP53\u003c/em\u003e-wildtype patients did (3.0 [2.3-6.0] \u003cem\u003eversus\u003c/em\u003e 12.0 [6.5\u0026ndash;12.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, we classified patients into two categories based on the subtypes classified by the 2022 WHO classification: MDS with biallelic \u003cem\u003eTP53\u003c/em\u003e inactivation (22 [27.5%]) and other subtypes (58 [72.5%]). We found that GATA1 scores in MDS patients with biallelic \u003cem\u003eTP53\u003c/em\u003e inactivation were significantly lower (3.5 [3.0\u0026ndash;8.0] \u003cem\u003eversus\u003c/em\u003e 8.0 [3.8\u0026ndash;12.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). According to 2022 ICC of myeloid neoplasm, we reclassified patients into two subgroups: myeloid neoplasms with mutated \u003cem\u003eTP53\u003c/em\u003e (27 [33.8%]), including MDS with mutated \u003cem\u003eTP53\u003c/em\u003e and MDS/acute myeloid leukemia (AML) with mutated \u003cem\u003eTP53\u003c/em\u003e, and other subtypes (53 [66.3%]). GATA1 scores of patients with myeloid neoplasms with mutated \u003cem\u003eTP53 were\u003c/em\u003e much lower than that of patients with other subtypes defined by 2022 ICC (4.0 [3.0\u0026ndash;8.0] \u003cem\u003eversus\u003c/em\u003e 9.0 [3.5\u0026ndash;12.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGATA1 expression levels in megakaryocytes with different morphologic subgroups.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDysplastic megakaryocytes displayed lower GATA1 expression levels than did normal megakaryocytes (1.0 [0\u0026ndash;2.0] \u003cem\u003eversus\u003c/em\u003e 3.0 [2.0\u0026ndash;3.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Compared with those in normal megakaryocytes, the GATA1 expression levels were significantly lower in hypolobulated or monolobar megakaryocytes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), multinucleated megakaryocytes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and micromegakaryocytes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). However, hyperlobulated megakaryocytes exhibited higher GATA1 expression levels than did normal megakaryocytes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the different kinds of dysplastic megakaryocytes, micromegakaryocytes exhibited the lowest GATA1 expression levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, we compared the GATA1 expression levels in megakaryocytes from patients in subgroups classified according to the myelofibrosis grade, karyotype, percentage of bone marrow blasts and \u003cem\u003eTP53\u003c/em\u003e mutation status. The GATA1 expression levels in megakaryocytes were lower in patients with abnormal karyotypes (abnormal \u003cem\u003eversus\u003c/em\u003e normal karyotypes 2.0 [1.0\u0026ndash;3.0] \u003cem\u003eversus\u003c/em\u003e 3.0 [1.0\u0026ndash;3.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), complex karyotypes (complex \u003cem\u003eversus\u003c/em\u003e noncomplex karyotypes 1.0 [0\u0026ndash;3.0] \u003cem\u003eversus\u003c/em\u003e 2.0 [1.0\u0026ndash;3.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or \u003cem\u003eTP53\u003c/em\u003e mutations (\u003cem\u003eTP53\u003c/em\u003e-mutated \u003cem\u003eversus TP53\u003c/em\u003e-wildtype 1.0 [0\u0026ndash;2.0] \u003cem\u003eversus\u003c/em\u003e 3.0 [1.0\u0026ndash;3.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which was also the case for the comparisons of the GATA1 scores. Compared with those from patients with myelofibrosis grade 0/1, megakaryocytes from patients with myelofibrosis grade 2/3 had lower GATA1 expression levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046), and the difference between the two subgroups was also significant for normal megakaryocytes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGATA1 expression level and cytogenetic abnormalities of the same megakaryocyte.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe explored the correlation between GATA1 expression level and cytogenetic abnormalities of the same megakaryocytes using MAC technique. Images of immunohistochemical dual staining and FISH were obtained from the same megakaryocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). A total of 53 megakaryocytes from 6 patients were included in the analysis. We found that GATA1-negative megakaryocytes showed higher frequencies of abnormal cytogenetics (87.5% \u003cem\u003eversus\u003c/em\u003e 47.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), suggesting that decreased GATA1 expression level in megakaryocytes was closely related to clonal abnormalities in MDS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImpact of GATA1 expression level of megakaryocytes in prognosis of MDS\u003c/h2\u003e \u003cp\u003eWe found that the OS of GATA1 low-expressed group was significantly shorter than that of GATA1 high-expressed group (median OS, 12 months [95%CI, 0\u0026ndash;25 months] \u003cem\u003eversus\u003c/em\u003e 35 months [95%CI, 17\u0026ndash;53 months], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) (Supplementary Fig.\u0026nbsp;2). To explore the significance of GATA1 expression level in prognosis of MDS, we included revised International Prognostic Scoring System (IPSS-R) risk category and GATA1 scores in multivariable analysis of OS, and the results showed that both IPSS-R higher-risk category (IPSS-R scores\u0026thinsp;\u0026gt;\u0026thinsp;3.5 points) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and lower GATA1 scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) were significantly associated with OS in MDS (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe transcription factor GATA1 plays a critical role in erythropoiesis and megakaryopoiesis. GATA1 deficiency can block megakaryocyte differentiation and maturation, contributing to immature and small megakaryocytes and thrombocytopenia\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur results showed that MDS patients in the GATA1 low-expressed group had lower platelet counts, suggesting that thrombocytopenia in MDS patients was associated with decreased GATA1 expression level. In our study, patients in the GATA1 low-expressed group showed smaller mean platelet volumes, while previous studies revealed that GATA1 deficiency in megakaryocytes in mice could result in fewer but enlarged platelets in vivo\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Most of these studies were based on \u003cem\u003eGATA1\u003c/em\u003e-knockdown or \u003cem\u003eGATA1\u003c/em\u003e-mutant mouse models. However, no patients with MDS in our study were detected with \u003cem\u003eGATA1\u003c/em\u003e mutation. We speculated that the differences in mean platelet volume may be attributed to different mechanisms of GATA1 deficiency in MDS. Although there was no significant correlation between GATA1 scores in patients and hemoglobin in our cohort, reticulocyte counts were positively correlated with GATA1 scores, which could also indicate that GATA1 expression levels were associated with erythropoiesis.\u003c/p\u003e \u003cp\u003ePatients with abnormal karyotypes or complex karyotypes had lower GATA1 scores. Furthermore, we explored the correlation between clonal abnormalities and GATA1 expression level of the same megakaryocytes using MAC technique. Abnormal or complex karyotypes were associated with a worse prognosis in MDS. Our results suggested that decreased GATA1 expression levels in megakaryocytes may be correlated with clonal abnormalities and prognosis in MDS patients.\u003c/p\u003e \u003cp\u003eIn a murine erythroleukemia cell line, Trainor \u003cem\u003eet al\u003c/em\u003e. reported that the DNA-binding domain of GATA1 can interact with the transactivation domain of p53, contributing to reciprocal inhibition between GATA1 and p53\u003csup\u003e29\u003c/sup\u003e. Previous studies revealed that GATA1 expression levels in megakaryocytes in primary myelofibrosis was downregulated, while microarray analysis suggested that the p53 pathway was activated in megakaryocytes in primary myelofibrosis, which was confirmed by quantitative reverse-transcriptase PCR\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In our study, the GATA1 scores and GATA1 expression levels of megakaryocytes in \u003cem\u003eTP53\u003c/em\u003e-mutated patients were significantly lower than those in \u003cem\u003eTP53\u003c/em\u003e-wildtype patients. Although the differences in GATA1 expression level among other subtypes of MDS were not significant, the GATA1 scores in MDS patients with biallelic \u003cem\u003eTP53\u003c/em\u003e inactivation were lower than those in the other subtypes.\u003c/p\u003e \u003cp\u003ePrevious studies suggested that decreased GATA1 expression level was associated with dysplasia of megakaryocytes in MDS patients\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In our study, compared with normal megakaryocytes, dysplastic megakaryocytes, including hypolobulated or monolobar megakaryocytes, multinucleated megakaryocytes and micromegakaryocytes, had lower GATA1 expression levels, while hyperlobulated megakaryocytes had higher GATA1 expression levels. Our data also indicated that decreased GATA1 expression level was associated with dysmegakaryopoiesis in MDS patients. Among the dysplastic megakaryocytes, the GATA1 expression levels in the micromegakaryocytes were lower than those in the other kinds of dysplastic megakaryocytes. Previous studies found that micromegakaryocytes in MDS were morphologically mature but had impaired polyploidization\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, indicating a degree of differentiation arrest. The lowest expression levels of GATA1 in micromegakaryocytes may be attributed to the combined effects of dysplasia and differentiation arrest.\u003c/p\u003e \u003cp\u003eCompared with that in healthy controls, essential thrombocythemia or polycythemia vera, GATA1 expression level in primary myelofibrosis patients was significantly downregulated\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Moreover, researchers found that GATA1 expression level decreased in patients previously diagnosed with \u003cem\u003eBCR/ABL\u003c/em\u003e-negative MPN during leukemia transformation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In \u003cem\u003eBCR/ABL\u003c/em\u003e-negative MPN, decreased GATA1 expression level can cause impaired megakaryopoiesis and induce myelofibrosis\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Many studies have revealed that bone marrow fibrosis indicates an adverse prognosis in MDS patients\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The GATA1 scores in patients with myelofibrosis grade 2/3 were lower than those in patients with myelofibrosis grade 0/1, but the difference was not significant. However, the GATA1 expression levels in megakaryocytes were significantly lower in patients with grade 2/3 myelofibrosis. Our data indicated that GATA1 may play a role in myelofibrosis in MDS patients, but the underlying mechanism remains unclear.\u003c/p\u003e \u003cp\u003eOS of GATA1 low-expressed group was significantly shorter than that of GATA1 high-expressed group. Furthermore, we included the GATA1 score as a continuous variable in multivariable analyses of overall survival (OS) and found that decreased GATA1 expression level was an independent adverse prognostic factor, regardless of IPSS-R risk categories in MDS patients.\u003c/p\u003e \u003cp\u003eThe main limitations of the study include the following: (ⅰ) the subjects included in the study were not consecutive, (ⅱ) the number of bone marrow biopsy or bone marrow smear samples was relatively small, and (ⅲ) the assessment of GATA1 expression levels was semiquantitative and carried a degree of subjectivity.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eInsufficient GATA1 expression was closely associated with dysmegakaryopoiesis in patients with MDS. Decreased GATA1 expression levels in megakaryocytes were positively correlated with \u003cem\u003eTP53\u003c/em\u003e mutations, abnormal or complex karyotypes and myelofibrosis in MDS. Downregulation of GATA1 expression level in megakaryocytes may predict adverse prognosis in MDS. Our results suggested that downregulation of GATA1 expression possibly plays a critical role in the pathogenesis of MDS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZJ.X designed the study. YD.Z and FH.L performed the experiments and analyzed the data.\u0026nbsp;CW.L, Q.S and JQ.L provided support for the experiments.\u0026nbsp;TJ.Q, ZF.X, B.L, SQ.Q, LJ.P, QY.G, and M.J recruited patients and collected clinical data. FH.L and ZJ.X prepared the manuscript. All authors read, approved the final manuscript, and agreed to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants for their contribution and time investment in the study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported in part by CAMS Initiative Fund for Medical Sciences (2022-I2M-1-022), National Natural Science Foundation of China (82170139，82070134) and Clinical research fund from National Clinical Research Centre for Blood Diseases (2023NCRCA0117, 2023NCRCA0103). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant conflicts of interest to declare about the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the findings of this study are available from the corresponding authors upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patient provided written informed consent in compliance with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, \u003cem\u003eet al.\u003c/em\u003e The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. 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Curr Protoc Hum Genet 2012; Chap. 4: Unit4.7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShivdasani RA, Fujiwara Y, McDevitt MA, Orkin SH. A lineage-selective knockout establishes the critical role of transcription factor GATA-1 in megakaryocyte growth and platelet development. Embo j 1997; 16(13): 3965\u0026ndash;3973.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrainor CD, Mas C, Archambault P, Di Lello P, Omichinski JG. GATA-1 associates with and inhibits p53. Blood 2009; 114(1): 165\u0026ndash;173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi Y, Uoshima N, Kimura S, Tanaka K, Wada K, Ozawa M, \u003cem\u003eet al.\u003c/em\u003e Relationship between morphological classification of the degree of maturation and the ploidy of micromegakaryocytes in myelodysplastic syndrome patients. Int J Hematol 1995; 61(3): 117\u0026ndash;122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi Y, Ozawa M, Maruo N, Kondo M. Megakaryocytic ploidy in myelodysplastic syndromes. Leuk Lymphoma 1993; 9(1\u0026ndash;2): 55\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuesche G, Teoman H, Wilczak W, Ganser A, Hecker H, Wilkens L, \u003cem\u003eet al.\u003c/em\u003e Marrow fibrosis predicts early fatal marrow failure in patients with myelodysplastic syndromes. Leukemia 2008; 22(2): 313\u0026ndash;322.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDella Porta MG, Malcovati L, Boveri E, Travaglino E, Pietra D, Pascutto C, \u003cem\u003eet al.\u003c/em\u003e Clinical relevance of bone marrow fibrosis and CD34-positive cell clusters in primary myelodysplastic syndromes. 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Mod Pathol 2014; 27(5): 681\u0026ndash;689.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[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":"","lastPublishedDoi":"10.21203/rs.3.rs-4488001/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4488001/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGATA1 is one of critical transcription factors for megakaryopoiesis and platelet production. Our study aimed to explore the correlations between GATA1 expression and dysmegakaryopoiesis in myelodysplastic syndromes (MDS). Data of blood cell counts, cytogenetics and \u003cem\u003eTP53\u003c/em\u003e mutation status from 90 MDS patients at diagnosis were collected. Firstly, we assessed GATA1 expression level of megakaryocytes by performing immunohistochemical staining on paraffin-embedded bone marrow biopsy sections from these patients. According to GATA1 expression level of megakaryocytes and positive megakaryocyte percentage, we assigned each patient a GATA1 score. Compared with \u003cem\u003eTP53\u003c/em\u003e-wildtype patients, GATA1 scores significantly decreased in \u003cem\u003eTP53-\u003c/em\u003emutated patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with abnormal karyotypes showed decreased GATA1 scores than those with normal karyotypes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024). GATA1 expression levels were significantly downregulated in dysplastic megakaryocytes, especially micromegakaryocytes, compared with normal megakaryocytes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, we explored the correlation between GATA1 expression levels and cytogenetic abnormalities of the same megakaryocyte using the morphology antibody chromosome (MAC) technique on fresh bone marrow smears. We found that GATA1-negative megakaryocytes had higher frequencies of cytogenetic abnormalities. Our results indicated that decreased GATA1 expression level of megakaryocytes was significantly associated with \u003cem\u003eTP53\u003c/em\u003e mutations, abnormal karyotypes and dysmegakaryopoiesis in MDS, suggesting that downregulation of GATA1 expression levels of megakaryocytes plays a critical role in the pathogenesis of MDS.\u003c/p\u003e","manuscriptTitle":"GATA1 insufficiencies in dysmegakaryopoiesis of myelodysplastic syndromes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 19:15:58","doi":"10.21203/rs.3.rs-4488001/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":"c60d8277-e9ee-4480-a107-965e9b812aaa","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32509463,"name":"Health sciences/Diseases/Haematological diseases/Haematological cancer/Myelodysplastic syndrome"},{"id":32509464,"name":"Biological sciences/Genetics/Cytogenetics"}],"tags":[],"updatedAt":"2024-06-20T11:25:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 19:15:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4488001","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4488001","identity":"rs-4488001","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}