Validation of the Molecular International Prognostic Scoring System (IPSS-M) for myelodysplastic neoplasms (MDS) and comparison with the revised International Prognostic Scoring System (IPSS-R) in Chinese Population: A Multicenter Retrospective Study.

Validation of the Molecular International Prognostic Scoring System (IPSS-M) for myelodysplastic neoplasms (MDS) and comparison with the revised International Prognostic Scoring System (IPSS-R) in Chinese Population: A Multicenter Retrospective Study. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation of the Molecular International Prognostic Scoring System (IPSS-M) for myelodysplastic neoplasms (MDS) and comparison with the revised International Prognostic Scoring System (IPSS-R) in Chinese Population: A Multicenter Retrospective Study. Mengmeng Hu, Ming Zhou, Yingying Shen, Guangsheng He, Li Huang, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4129078/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 Objectives The Revised international prognostic scoring system (IPSS-R) is now commonly being used clinically to guide the treatment of myelodysplastic neoplasms (MDS). Recently, the Molecular International Prognostic Scoring System (IPSS-M)was proposed. In this study, we have validated the potential predictive value of the comparative IPSS-M in Chinese MDS patients. Design Retrospective multicenter observational study. Setting and participants 113 MDS patients(April 2019 - June 2022) from 10 distinct centers in Jiangnan region of China, grouped by IPSS-R and IPSS-M was obtained and the scoring criteria were retrospectively analyzed to compare the prognostic assessment efficacy of the different prognostic assessment systems. Main outcome measures The prognostic indicators of MDS patients are main outcome measures. Results 72 (63.7%) patients were reclassified after regrouping from IPSS-R to IPSS-M, and 52 of them were transferred to a higher risk group, with a higher percentage of patients aged ≥ 60 years in the higher risk group. Survival analysis confirmed that overall survival(OS) was variable in the different risk strata, with shorter survival time in the higher risk group and lower OS in the older(≥ 60 years) than in the younger group; whereas in univariate and multifactorial analysis, age ≥ 60 years, percentage of bone marrow blasts, chromosomal classification of IPSS-R, TP53, RUNX1, DNMT3A, NRAS, CBL, GNAS, and FLT3_ITD gene mutation were associated with OS. Leukemia-free survival(LFS)analysis revealed that higher IPSS-R and IPSS-M risk stratification was linked with shorter LFS time. Receiver operating characteristic (ROC) curves were drawn according to OS displaying AUC = 0.629 for IPSS-R and AUC = 0.705 for IPSS-M; AUC = 0.635 for IPSS-M younger group and AUC = 0.691 for older group. Conclusions Our study confirmed that the IPSS-M prognostic scoring system could be applicable to Chinese patients and that IPSS-M was significantly better than IPSS-R for the prognostic assessment of MDS patients. Moreover, IPSS-M appeared to have better predictive validity in older patients compared to younger patients. Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Myelodysplastic neoplasms (MDS), also known as myelodysplastic syndrome, is a clonal hematopoietic malignancy that can lead to morphological bone marrow dysplasia and anemia, with a substantial decrease in neutrophil or platelet count. 1 Interestingly, MDS patients of different risk levels can exhibit significantly variable survival rates. For instance, the median survival of patients with low-risk MDS is approximately 3–10 years, whereas the median survival of patients with high-risk MDS is less than 3 years. To date, there are several prognostic models proposed for clinical use. The revised International Prognostic Scoring System (IPSS-R) can be primarily divided into five groups according to the degree of blood cytopenia, the percentage of the bone marrow blasts, and the cytogenetic performance. 2 IPSS-R has been used clinically for more than 10 years, but its impact on clinical efficacy and prognosis is somewhat biased. Thus, in the current molecular era, at present, the molecular International Prognostic Scoring System (IPSS-M) has markedly improved the conventional risk models by using genetic data, and by dividing MDS into six distinct groups. 3 4 At present, there is not much clinical validation reported for the IPSS-M, and the accuracy of prediction still needs to be verified in large scale studies. IPSS-M mainly includes gene mutation data. It has been established previously that the IPSS-R model and the IPSS-M model have similar prognostic value, but in the elderly group, the IPSS-M model has exhibited higher predictive accuracy than the IPSS-R model. However, the researchers have also indicated that the study was a retrospective analysis from a single center, whereas MDS is a highly heterogeneous disease and thus multicenter analysis is required for confirmation. 5 Hence, in this study retrospective analysis of clinical data from patients visiting multiple centers is used to further confirm the performance in the prognostic evaluation and heterogeneity in different age cohorts of IPSS-M. It is helpful for clinicians to choose a more appropriate MDS prediction model in clinical diagnosis and treatment. METHODS Study design and population The study was registered at clinicaltrials.gov under the identifier NCT03903055. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang Chinese Medical University (with ethics approval number: 2019-KL-002-02), and was conducted in accordance with the ethical standards of the institutional and/or national research committee and the Declaration of Helsinki.113 patients diagnosed with MDS from 10 clinical centers (The First Affiliated Hospital of Zhejiang Chinese Medical University, The First Affiliated Hospital of Nanjing Medical University, The First Affiliated Hospital, College of Medicine, Zhejiang University, The Affiliated Jinhua Hospital of Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Shaoxing People's Hospital, Tongde hospital of zhejiang province, Taizhou Central Hospital, Dongyang Hospital Affiliated to Wenzhou Medical University, The Affiliated Hospital of Shaoxing University) during April 2019 - June 2022 were enrolled. The diagnosis was based on the World Health Organization (WHO) revised standards in 2016, and their basic information (e.g., gender, age, diagnosis date, follow-up time, leukemia transformation time, time of death) was collected. The relevant laboratory tests (e.g., blood routine, bone marrow morphology, identification of abnormal chromosomes as well as mutant genes) were performed. The 113 subjects were thereafter divided into different risk groups in IPSS-R model and IPSS-M model. In IPSS-R model, the subjects were divided into the very low risk group, the low risk group, intermediate risk group, high risk group, and the very high-risk group. On the contrary, in IPSS-M model, the subjects were divided into the very low risk group, the low risk group, moderate risk group, moderate high-risk group, high risk group, and the very high-risk group. Statistical analysis The data analysis was conducted using SPSS 23.0, with count data expressed as the number of cases and percentage, the measurement data expressed as Md (IQR), and inter group numerical variables using Kruskal Wallis test. P < 0.05 indicated a statistically significant difference; Overall survival (OS) is defined as the time interval from the diagnosis to death or the last follow-up, whereas leukemia-free survival (LFS) is defined as the time interval from diagnosis to leukemia transformation or death or the last follow-up. The Kaplan-Meier method was used to estimate OS, and univariate and multivariate analyses were conducted by employing the Cox risk regression model. Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. RESULTS Patient characteristics Among the 113 subjects, 72 were males (63.7%) and 41 were females (36.3%). The median age of enrolled patients was 65 years old (inter-quartile range [IQR] = 56, 71), and the median survival was 17 months ([IQR] = 9, 28.5). According to the revised WHO standards in 2016, 113 subjects were diagnosed as MDS/MPN subtype 4 cases (3.5%), MDS-EB1 41 cases (36.3%), MDS-EB2 32 cases (28.3%), MDS-MLD 26 cases (23.0%), and MDS-RS 4 cases (3.5%), MDS-SLD 3 cases (2.7%), MDS-U 3 cases (2.7%). The median bone marrow blasts were 5.3% ([IQR] = 2.0, 10.25), the median hemoglobin level was 70 g/L ([IQR] = 57.5, 93.5), the median platelet count was 46×10 9 /L ([IQR] = 24.5, 81.5), and the median neutrophil count was 1.2×10 9 /L ([IQR] = 0.62, 2.22). According to the IPSS-R and IPSS-M scoring standards, 113 subjects were then grouped and IPSS-R model was used for predicting the risk level, with 1 person in the very low risk group (0.9%), 10 people in the low risk group (8.8%), 35 people in the intermediate risk group (31%), 35 people in the high risk group (31%), and 32 people in the very high risk group (28.3%), as depicted in Table 1 . In addition, by using the IPSS-M model, there were 1 case belonging to very low risk group (0.9%), 4 people in the low risk group (3.5%), 11 people in the moderate low risk group (9.7%), 8 people in the moderate high risk group (7.1%), 31 people in the high risk group (27.4%), and 58 people in the very high risk group (51.3%), as shown in Table 1 . The percentage of bone marrow blasts, hemoglobin, platelet, neutrophil count, LFS of the 113 subjects showed no statistical difference in the young group (< 60 years old) and the elderly group (≥ 60 years old), while IPSS-M and OS showed a statistical difference between the two age groups (P < 0.05, see Table 1 ). Table 1 Clinical characteristics of younger (＜60 years) and older (≥60 years) MDS patients Characteristic Total(n = 113) <60years(n = 36) ≥ 60years(n = 77) P value BM blasts(%) 5.3(2.0,10.25) 4.815(1.4,8.875) 6(2.12,11.25) 0.153 Hb(g/L) 70(57.5,93.5) 82.5(58.75,105) 68(51,83) 0.107 Plt(×10 9 /L) 46(24.5,81.5) 36.5(21.5,81) 47(26,81.5) 0.486 Neu(×10 9 /L) 1.2(0.62,2.22) 0.965(0.515,1.595) 1.26(0.7,2.625) 0.137 OS(months) 17(9,28.5) 19.5(12,40) 15(8,25.5) 0.049 LFS(months) 15( 8 , 27 ) 11.5(5,22.75) 17( 9 , 29 ) 0.068 IPSS-R chromosome classification Very good 1(0.9%) 0 1(1.3%) 0.744 good 59(52.2%) 18(50%) 41(53.2%) intermediate 20(17.7%) 8(22.2%) 12(15.6%) poor 5(4.4%) 1(2.8%) 4(5.2%) Very poor 28(24.8%) 9(25%) 19(24.7%) IPSS-R classification Very low 1(0.9%) 0 1(1.3%) 0.052 Low 10(8.8%) 7(19.4%) 3(3.9%) Intermediate 35(31%) 12(33.3%) 23(29.9%) High 35(31%) 9(25%) 26(33.8%) Very high 32(28.3%) 8(22.2%) 24(31.2%) IPSS-M classification Very low 1(0.9%) 1(2.8%) 0 0.003 Low 4(3.5%) 2(5.6%) 2(2.6%) Moderate low 11(9.7%) 8(22.2%) 3(39%) Moderate high 8(7.1%) 3(8.3%) 5(6.5%) High 31(27.4%) 9(25%) 22(28.6%) Very high 58(51.3%) 13(36.1%) 45(58.4%) Abbreviations: BM, bone marrow; Hb, hemoglobin; Plt, platelets.；Neu, neutrophils Typical mutant genes and abnormal chromosomes According to the IPSS-M model, mutations could be mainly divided into 16 main prognostic genes ( ASXL1 , CBL, DNMT3A, ETV6, EZH2, FLT3, IDH2, KRAS, MLLPTD, NPM1, NRAS, RUNX1, SF3B1, SRSF2, TP53multihit and U2AF1 ) and 15 residual genes ( BCOR, BCORL1, CEBPA, ETNK1, GATA2, GNB1, IDH1, NF1, PHF6, PPM1D, PRPF8, PTPN11, SETBP1, STAG2, and WT1 ). 4 We found a total of 74 mutant genes (see Figure 1 A), among which the common mutant genes (>10% of the total number of mutations) included ASXL1, TET2, RUNX1, TP53, SRSF2, BCOR, DNMT3A, and U2AF1 . Interestingly, upon comparing the 17 mutant genes with a total number of mutations ≥5% by age group, it was found that ASXL1 and U2AF1 mutations were more common in the young group (<60 years old), whereas DNMT3A and BCOR mutations are relatively more common in the elderly group (≥ 60 years old) (see Figure 1 B). Among the 113 subjects, 102 (90.3%) had at least one mutant gene, 86 (76.1%) had no less than two mutant genes, and 65 (57.5%) had no less than three mutant genes. In addition, 61 of them (54%) had at least one abnormal chromosome and 27 of them (23.9%) were complex karyotypes; 54 of them (47.8%) had both gene mutation and chromosome abnormality, 47 of them (41.6%) had gene mutation but did not show chromosome abnormality, 7 of them (6.2%) had chromosome abnormality but no gene mutation, while 4 of them (3.5%) had neither gene mutation nor chromosome abnormality. Survival analysis The risk groups in the IPSS-M model and the IPSS-R model were then compared in terms of OS. As observed, the risk groups in both the models exhibited statistical differences in the survival (P<0.05, see Figure 2 A,B,C). Leukemia-free survival analysis revealed that higher IPSS-R and IPSS-M risk stratification was linked with shorter LFS time, as shown in Figure 2 D,E,F. Univariate analysis revealed that age (≥60 years old or not), percentage of bone marrow blasts, IPSS-R chromosome classification, and TP53 mutation were significant risk factors affecting prognosis (P<0.05, see Table 2). Moreover, multivariate analysis indicated that age (≥60 years old or not) (HR = 3.504 (1.409-8.716)), IPSS-R chromosome classification (HR = 1.456 (1.044-2.030)), RUNX1 mutation (HR = 3.146 (1.174-8.431)), DNMT3A mutation (HR = 6.489 (1.784-23.604)), CBL mutation (HR = 0.075(0.008-0.702)), GNAS mutation (HR = 0.016 (0.000-0.718)), and FLT3_ITD mutation (HR = 0.062(0.004-0.914)) were related with OS (P<0.05) , as depicted in Table 2. Additionally, both univariate and multivariate analyses revealed that WT1 mutation was related to LFS (P<0.05). Table 2 Multivariable analysis of prognostic factors for overall survival in patients with MDS Variables Univariable P Multivariable P HR (95% CI) HR (95% CI) age （ ≥60y ） 3.225（1.684-6.177） ＜0.001 3.504（1.409-8.716） 0.007 BM blast 1.067(1.020-1.115) 0.005 Hb 0.994(0.984-1.004) 0.244 Plt 0.999(0.996-1.002) 0.343 Neu 1.046(0.990-1.105) 0.111 IPSS-R chromosome classification 1.249(1.037-1.505) 0.019 1.456（1.044-2.030） 0.027 ASXL1 0.727(0.431-1.225) 0.231 TET2 0.943(0.550-1.616) 0.83 RUNX1 1.042(0.576-1.884) 0.891 3.146(1.174-8.431) 0.023 TP53 3.842(2.125-6.947) ＜0.001 SRSF2 0.935(0.489-1.787) 0.838 BCOR 0.943(0.494-1.802) 0.859 DNMT3A 1.257(0.637-2.479) 0.51 6.489(1.784-23.604) 0.005 U2AF1 1.282(0.632-2.602) 0.491 STAG2 0.805(0.292-2.222) 0.676 NRAS 2.196(0.995-4.843) 0.051 11.68(2.623-52.005) 0.001 WT1 0.992(0.427-2.306) 0.986 EZH2 0.943(0.378-2.357) 0.901 SETBP 1.309(0.524-3.271) 0.564 GATA2 0.907(0.363-2.264) 0.834 CREBBP 1.232(0.494-3.070) 0.654 CBL 0.561(0.175-1.796) 0.33 0.075(0.008-0.702) 0.023 ETV6 0.944(0.343-2.598) 0.912 CUX1 0.704(0.172-2.885） 0.626 CSF3R 0.513(0.125-2.097) 0.353 SF3B1 0.669(0.209-2.147) 0.5 NF1 0.606(0.148-2.486) 0.487 SH2B3 0.335(0.046-2.424) 0.279 NPM1 1.326(0.324-5.431) 0.695 CEBPA 1.654(0.517-5.294) 0.397 47.215(2.275-980.028) 0.013 MPL 0.387(0.054-2.794) 0.347 KRAS 1.567(0.488-5.037) 0.451 IDH2 1.088(0.265-4.465) 0.906 ZRSR2 1.887(0.587-6.064) 0.286 GNAS 0.503(0.070-3.638) 0.496 0.016(0.000-0.718) 0.033 FLT3_ITD 0.936(0.228-3.836) 0.927 0.062(0.004-0.914) 0.043 KMT2D 0.422(0.058-3.052) 0.393 DDX41 0.687(0.095-4.971) 0.71 TTN 2.45(0.594-10.102) 0.215 20.771(2.932-147.129) 0.002 Rating model prediction According to the two models, 72 people (63.7%) were reclassified after subgrouping from IPSS-R to IPSS-M, and 52 of them were reassigned to the higher risk group after being identified under a new risk level. According to the patient's OS outcome (i.e., death), the receiver operating characteristic (ROC) curve was plotted, which showed AUC=0.629 for the IPSS-R model and ACU=0.705 for the IPSS-M model respectively. It was observed that for the young group, the AUC of the IPSS-R model was 0.515, and the AUC of the IPSS-M model was 0.635; For the elderly group, the AUC of the IPSS-R model was 0.658, and the AUC of the IPSS-M model was 0.691 (see Figure 3 A,B,C). According to the patient's LFS outcome (i.e., leukemia or death), a ROC curve was drawn, depicting AUC=0.652 for the IPSS-R model and AUC=0.680 for the IPSS-M model. In addition, for the young group, AUC=0.635 for the IPSS-R model and AUC=0.656 for the IPSS-M model=0.656 was observed but for the elderly group, AUC=0.633 for the IPSS-R model and AUC=0.642 for the IPSS-M model was obtained (see Figure 3 D,E,F). DISCUSSION MDS is a highly heterogeneous hematological malignancy characterized by significant prognostic differences. It has been established that Incorporating genes into prognostic scoring tools is undoubtedly a good risk level method, but there are still significant differences reported in mutant genes among different populations. Next-generation sequencing (NGS) technology is an important tool for genomics research, which can obtain useful genomic information with high throughput, thereby providing novel ideas and methods for disease diagnosis and prognostic evaluation. Moreover, with the development of NGS technology, it has been widely applied in the research related to MDS. A number of prior studies have also found that mutations in various gens can exhibit great value for the prognostic evaluation of MDS in their research at the molecular level, for instance, SF3B1 mutations are predictors of favourable prognosis, while driver mutations of other genes (such as ASXL1, SRSF2, RUNX1, TP53, JAK2and IDH2) are associated with a reduced probability of survival and increased risk of disease progression. 6-22 The establishment of the revised International Prognostic Scoring System (IPSS-R) model was based on the evaluation of data from 7012 patients from multiple institutional databases in the IWG-PM joint database. The median age of patients was 71 years old, 77% was 60 years old, and Greenberg et al. proposed that age had minimal impact on the prognosis of MDS. 2 The clinical data analysis of the study subjects indicated that there was no statistical difference in percentage of the marrow blasts, hemoglobin, platelet, neutrophil count and cell genetic abnormality between the two age groups, which also confirmed the Greenberg’s viewpoint. However, somatic cell gene mutation has not been used for determination of the risk level of MDS, so the molecular International Prognostic Scoring System (IPSS-M) model has significantly increased the weight of gene mutation, and included more accurate risk scores which could be helpful for clinical selection of more accurate treatment plans. 4 Bernard et al. reported that the risk level of young patients and elderly patients was not significantly different during the establishment of the IPSS-M model. However, WU et al. found that IPSS-M has a relatively higher predictive accuracy among the elderly group. The results of our study also confirmed WU J's viewpoint (AUC = 0.635 for the young group and 0.691 for the elderly group), which risk stratification by the IPSS-M model showed a higher proportion of patients aged ≥60 years in higher risk groups and the OS of the elderly group was significantly shorter than that of the young group, which could be be related to the comorbidities(such as myocardial infarction, cerebrovascular disease) of elderly patients. The common mutant genes found in this research cohort (mutation number>10%) included ASXL1, TET2, RUNX1, TP53, SRSF2, BCOR, DNMT3A, U2AF1 , which was basically consistent with other prior research reports on the common mutant genes diagnosed in hematological diseases. 7 9 10 12 14 16 23 The major difference was that we found in this cohort study that ASXL1 mutation was more common among the young patients. ASXL1 is an epigenetic regulatory gene, and is one of the most frequently mutated genes in all subtypes of myeloid malignancies, and mutation of this gene is often detected in clonal hematopoiesis. Somatic mutation is relatively rare in people younger than 40 years old, but the frequency has been observed to increase significantly with age. 23 Although this study found that ASXL1 was more common in the young group, the median age of the young group was also 54 (IQR=48.5, 57) years old. In addition, a number of previous studies about unexplained blood cytopenia also demonstrated that the positive predictive value of spliceosome gene mutation and replacement mode for the myeloid tumors involving TET2, DNMT3A or ASXL1 was 0.86 to 1.0, 10 which could also be attributed to ASXL1 mutation that causes the disease in these patients. 8 Interestingly, there are also studies indicating that shorter OS of MDS patients was associated with ASXL1. 6 We found that U2AF1 mutation was also more common in the young group, which was consistent with the results reported by Li, B., et al., 24 but a larger sample size is still needed to further confirm. The results of univariate and multivariate analyses have established that age, bone marrow blasts, IPSS-R cytogenetic category, TP53 mutation, RUNX1 mutation, DNMT3A mutation, and NRAS mutation could serve as important factors influencing the OS and prognosis of MDS patients. Older age may lead to the poor prognosis, which could be related to comorbidities. Zipperer et al. believed that existence of comorbidities could be an important factor affecting the prognosis of MDS patients, and that elderly patients usually have more comorbidities in comparison to young patients. 25 Martin also postulated that age should become a part of the transplant decision-making process and should be integrated into the scoring system for predicting hematopoietic stem cell transplantation (HSCT)results in MDS in the future. 26 The percentage of marrow blasts has been found to be related to its diagnostic typing, and cellular genetic abnormality can serve as an important component of clinical evaluation of various malignant hematological diseases. 27 It has been demonstrated that NRAS, TP53, DNMT3A, and RUNX1 mutations are poor prognostic factors of MDS. 9 16 19-22 28-30 Additionally, both univariate and multivariate analyses revealed that WT1 mutation was a poor prognostic factor of LFS. Huang et al. also demonstrated that overexpression of WT1 at the time of diagnosis can effectively predict poor survival and early AML evolution in MDS patients with reduced platelet count. 31 In this study, the IPSS-M model showed better predictive accuracy for OS compared to IPSS-R (AUC of IPSS-R = 0.629, AUC of IPSS-M = 0.705), and the accuracy of the two models for LFS prediction using a plotted ROC curve was also observed to be comparable (AUC of IPSS-R = 0.652, AUC of IPSS-M = 0.680), which was consistent with findings of the previous studies. 4 32 33 Survival analysis using the KM method also showed that the risk stratification of the IPSS-M and IPSS-R model was statistically significant for LFS analysis.The OS prediction accuracy of both IPSS-R and the IPSS-M model in the young group cohort was relatively low (AUC of IPSS-R = 0.515, AUC of IPSS-M = 0.635). Thus, considering the possibility of allogeneic hematopoietic stem cell transplantation affecting survival in this group of patients, 11 people (30.5%) in the young group were subjected to allogeneic hematopoietic stem cell transplantation. It has been reported earlier that young MDS patients undergoing allogeneic hematopoietic stem cell transplantation had a longer survival rate, 34-36 whereas the predictive accuracy of both was found to be comparable and slightly higher for the IPSS-M model (AUC of IPSS-R = 0.658, AUC of IPSS-M = 0.691) in the elderly group, which was in line with the results of other investigators. 4 32 According to this multicenter retrospective study on IPSS-M model validation, the addition of mutant genes displayed significantly higher value for the prognostic evaluation of MDS patients, especially for the prediction the elderly group. However, there are still several limitations associated with this study. Although the subjects of this study are from multiple centers, the overall number of cases analyzed were insufficient and the number of genetic testing items for the subjects were also different. Thus, involvement of patients from more centers, more cases, and more unified testing items for mutated genes are needed to further confirm the performance in prognostic evaluation of the IPSS-M model and its heterogeneity across different age groups. Strengths and limitations of this study This study compared the prognostic values of IPSS-R and IPSS-M in MDS patients in Jiangnan region of China using real-world outcome data. This study confirmed whether IPSS-M can enhance the prognostic potential and applies to Chinese MDS patients. The overall cases were relatively insufficient and the packages of genetic testing items were also not uniform in this study. This was a retrospective analysis. DECLARATIONS Acknowledgments The authors thank all of the study participants. Contributors Mengmeng Hu, Ming Zhou, Shengyun Lin, Baodong Ye and Qinghong Yu contributed to the concept and design of the study. Yingying Shen, Guangsheng He, Li Huang, Shujuan Zhou , Jiaping Fu,Huifang Jiang, Sai Chen, Xiujin Ye, Zhiyin Zheng, Liqiang Wu, Bo Wang, Gongqiang Wu, and Qinghong Yu managed the data collection.Mengmeng Hu, Shengyun Lin and Qinghong Yu were directly responsible for the analysis of data. Mengmeng Hu was responsible for the initial draft of this manuscript. Qinghong Yu submitted and revised the article and acts as guarantor for the final manuscript. All authors read and approved the final draft. Data availability statement The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Ethics approval Our study complies with the Declaration of Helsinki and was approved by the hospital ethics committee. (2019-KL-002-02). Funding The present study was supported by the Great Item of Science and Technology Planning Project of Zhejiang Province (grant no. 2019C03047) Competing interests None declared. Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. Patient consent for publication Not applicable. REFERENCES Khoury JD, Solary E, Abla O, et al. The 5th edition of the World Health Organization classification of haematolymphoid tumours: Myeloid and histiocytic/dendritic neoplasms. Leukemia 2022;36:1703-19. doi:10.1038/s41375-022-01613-1 Greenberg PL, Tuechler H, Schanz J, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood 2012;120:2454-65. doi:10.1182/blood-2012-03-420489 Bersanelli M, Travaglino E, Meggndorfer M, et al. Classification and personalized prognostic assessment on the basis of clinical and genomic features in myelodysplastic syndromes. 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U2AF1 mutations induce oncogenic IRAK4 isoforms and activate innate immune pathways in myeloid malignancies. Nat Cell Biol 2019;21:640-50. doi:10.1038/s41556-019-0314-5 Zhao YJ, Cai WL, Hua Y, Yang XC, Zhou JD. The biological and clinical consequences of RNA splicing factor U2AF1 mutation in myeloid malignancies. Cancers (Basel) 2022; 14: 4406. doi:10.3390/cancers14184406 Wang HQ, Guo YB, Dong ZK, et al. Differential U2AF1 mutation sites, burden and co-mutation genes can predict prognosis in patients with myelodysplastic syndrome. Sci Rep 2020;10:18622. doi:10.1038/s41598-020-74744-z Lin ME, Hou HA, Tsai CH, et al. Dynamics of DNMT3A mutation and prognostic relevance in patients with primary myelodysplastic syndrome. Clin Epigenetics 2018;10:42. doi:10.1186/s13148-018-0476-1 Abuhadra N, Mukherjee S, Al-Issa K, et al. BCOR and BCORL1 mutations in myelodysplastic syndromes (MDS): Clonal architecture and impact on outcomes. Leuk Lymphoma 2019;60:1587-90. doi:10.1080/10428194.2018.1543885 Badaat I, Mirza S, Padron E, et al. Concurrent mutations in other epigenetic modulators portend better prognosis in BCOR-mutated myelodysplastic syndrome. J Clin Pathol 2020;73:209-12. doi:10.1136/jclinpath-2019-206132 Ciurea SO, Chilkulwar A, Saliba RM, et al. Prognostic factors influencing survival after allogeneic transplantation for AML/MDS patients with TP53 mutations. Blood 2018; 131: 2989-92. doi:10.1182/blood-2018-02-832360 Sallman DA, Komrokji R, List A, Padron E. Reply to Goel et al. 'TP53 mutation allele-burden and disease outcome in MDS/AML'. Leukemia 2017;31:767-68. doi:10.1038/leu.2016.257 Goel S, Hall J, Pradhan K, et al. High prevalence and allele burden-independent prognostic importance of p53 mutations in an inner-city MDS/AML cohort. Leukemia 2016;30:1793-5. doi:10.1038/leu.2016.74 Stengel A, Haferlach T, Baer C, et al. Specific subtype distribution with impact on prognosis of TP53 single hit and double hit events in AML and MDS. Blood Adv 2023;7:2952-6. doi:10.1182/bloodadvances.2022009100 Jaiswal S, Fontanillas P, Flannick J, et al. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med 2014;371:2488-98. doi:10.1056/NEJMoa1408617 Li B, Liu JQ, Jia YJ, et al. Clinical features and biological implications of different U2AF1 mutation types in myelodysplastic syndromes. Genes Chromosomes Cancer 2018;57:80-8. doi:10.1002/gcc.22510 Zipperer E, Tanha N, Strupp C, et al. The myelodysplastic syndrome-comorbidity index provides additional prognostic information on patients stratified according to the revised international prognostic scoring system. Haematologica 2014;99:e31-e32. doi:10.3324/haematol.2013.101055 Martin C, Raphaël P, Jürgen F, et al. Role of age and hematopoietic cell transplantation-specific comorbidity index in myelodysplastic patients undergoing an allotransplant: A retrospective study from the chronic malignancies working party of the european group for blood and marrow transplantation. Biol Blood Marrow Transplant 2020;26:451-7. doi:10.1016/j.bbmt.2019.10.015 Akkari YMN, Baughn LB, Dubuc AM, et al. Guiding the global evolution of cytogenetic testing for hematologic malignancies. Blood 2022;139:2273-84. doi:10.1182/blood.2021014309 You XN, Liu FB, Binder M, et al. Asxl1 loss cooperates with oncogenic Nras in mice to reprogram the immune microenvironment and drive leukemic transformation. Blood 2022;139:1066-79. doi:10.1182/blood.2021012519 Zhou PQ, Xia CX, Wang TJ, et al. Senescent bone marrow microenvironment promotes Nras-mutant leukemia. J Mol Cell Biol 2021;13:72-4. doi:10.1093/jmcb/mjaa062 Makishima H, Yoshizato T, Yoshida K, et al. Dynamics of clonal evolution in myelodysplastic syndromes. Nat Genet 2017;49:204-12. doi:10.1038/ng.3742 Huang QS, Wang JZ, Qin YZ, et al. Overexpression of WT1 and PRAME predicts poor outcomes of patients with myelodysplastic syndromes with thrombocytopenia. Blood Adv 2019;3:3406-18. doi:10.1182/bloodadvances.2019000564 Wu JY, Zhang YD, Qin TJ, et al. IPSS-M has greater survival predictive accuracy compared with IPSS-R in persons >/= 60 years with myelodysplastic syndromes. Exp Hematol Oncol 2022;11:73. doi:10.1186/s40164-022-00328-4 Polprasert C, Niparuck P, Rattanathammethee T, et al. Comparison of Molecular International Prognostic Scoring System (M-IPSS) and Revised International Prognostic Scoring System (R-IPSS) in Thai patients with myelodysplastic neoplasms. Hematology 2022;27:1301-4. doi:10.1080/16078454.2022.2156682 Grabska J, Shah B, Reed D, et al. Myelodysplastic syndromes in adolescent young adults: One institution's experience. Clin Lymphoma Myeloma Leuk 2016;16 Suppl:S53-S56. doi:10.1016/j.clml.2016.02.022 Yang GC, Wang X, Huang SQ, et al. Generalist in allogeneic hematopoietic stem cell transplantation for MDS or AML: Epigenetic therapy. Front Immunol 2022;13:1034438. doi:10.3389/fimmu.2022.1034438 Shimomura Y, Hara M, Konuma T, et al. Allogeneic hematopoietic stem cell transplantation for myelodysplastic syndrome in adolescent and young adult patients. Bone Marrow Transplant 2021;56:2510-7. doi:10.1038/s41409-021-01324-8 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4129078","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282175549,"identity":"158c753f-432f-4f7c-8af4-1e98cce4414d","order_by":0,"name":"Mengmeng Hu","email":"","orcid":"","institution":"Affiliated Hospital of Shaoxing University（Shao Xing Municipal Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Mengmeng","middleName":"","lastName":"Hu","suffix":""},{"id":282175550,"identity":"a68c80af-d891-41fe-a37d-fd17050ea77b","order_by":1,"name":"Ming Zhou","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Zhou","suffix":""},{"id":282175551,"identity":"51450240-5433-454c-ab3b-ed33f68ca6d6","order_by":2,"name":"Yingying Shen","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Shen","suffix":""},{"id":282175552,"identity":"60a8170c-eeed-4fee-9d44-0a6d10a2fb33","order_by":3,"name":"Guangsheng He","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangsheng","middleName":"","lastName":"He","suffix":""},{"id":282175553,"identity":"e5f675a2-9df9-435d-a0f3-38c531aab734","order_by":4,"name":"Li Huang","email":"","orcid":"","institution":"The Affiliated Jinhua Hospital of Wenzhou Medical 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Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYFAC5gYgYcPDz95AtBZGkNI0GcmeA6RpOWxjcMOBSA387AcbH/O2nedhuMHA+OFjDhFaJHsSm415227zMM5uYJacuY0ILQYHEtukQVqYZQ6wMfMSo8X+/MP237xt53jYJBKI1GIgkdjGzNt2gIeHaC0SNx42S845l8wjwXOwmTi/8PcnH/zwpszO3v5488EPH4nRAgJMPGAKHEFEAsYfxKsdBaNgFIyCkQgAJTw0Tv9DxE8AAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qinghong","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-03-19 09:36:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4129078/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4129078/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53419070,"identity":"b4a0538b-1163-4de4-b357-9203e0d3cafc","added_by":"auto","created_at":"2024-03-25 18:10:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":775588,"visible":true,"origin":"","legend":"\u003cp\u003e(A)The frequency of mutated genes in 113 patients. (B) Gene mutation frequency in patients aged＜60 years and ≥60 years\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4129078/v1/5c8ff0e55655b986ec705764.jpg"},{"id":53419069,"identity":"94ab7e5a-d0e4-4fbc-8a96-cf19f8b28f10","added_by":"auto","created_at":"2024-03-25 18:10:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":559818,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier probability estimates of overall survival(OS)(A,B,C) and leukemia free survival (LFS)(D,E,F) of MDS patients stratified according to different prognostic scoring systems and age groups. P-values are from the log-rank test\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4129078/v1/e79f69216885427c94a0e67d.jpg"},{"id":53419072,"identity":"682ce130-6961-4379-99d3-19ce4cf085ef","added_by":"auto","created_at":"2024-03-25 18:10:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":466739,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of prognostic model discrimination in patients with MDS. Model discrimination as measured by the AUC obtained with IPSS-R or IPSS-M categories on OS(A,B,C) and LFS(D,E,F) in the whole cohort, age\u0026lt;60 years cohort and age≥60 years cohort.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4129078/v1/95ba174457d98309a4e6da8c.jpg"},{"id":60823662,"identity":"e5c69efc-16fd-4927-a661-534e26c66090","added_by":"auto","created_at":"2024-07-22 13:38:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2629409,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4129078/v1/3f1bfbf7-1e1a-479f-acb5-03c0005d5893.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation of the Molecular International Prognostic Scoring System (IPSS-M) for myelodysplastic neoplasms (MDS) and comparison with the revised International Prognostic Scoring System (IPSS-R) in Chinese Population: A Multicenter Retrospective Study.","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMyelodysplastic neoplasms (MDS), also known as myelodysplastic syndrome, is a clonal hematopoietic malignancy that can lead to morphological bone marrow dysplasia and anemia, with a substantial decrease in neutrophil or platelet count.\u003csup\u003e1\u003c/sup\u003e Interestingly, MDS patients of different risk levels can exhibit significantly variable survival rates. For instance, the median survival of patients with low-risk MDS is approximately 3\u0026ndash;10 years, whereas the median survival of patients with high-risk MDS is less than 3 years. To date, there are several prognostic models proposed for clinical use. The revised International Prognostic Scoring System (IPSS-R) can be primarily divided into five groups according to the degree of blood cytopenia, the percentage of the bone marrow blasts, and the cytogenetic performance.\u003csup\u003e2\u003c/sup\u003e IPSS-R has been used clinically for more than 10 years, but its impact on clinical efficacy and prognosis is somewhat biased. Thus, in the current molecular era, at present, the molecular International Prognostic Scoring System (IPSS-M) has markedly improved the conventional risk models by using genetic data, and by dividing MDS into six distinct groups.\u003csup\u003e3 4\u003c/sup\u003e At present, there is not much clinical validation reported for the IPSS-M, and the accuracy of prediction still needs to be verified in large scale studies. IPSS-M mainly includes gene mutation data. It has been established previously that the IPSS-R model and the IPSS-M model have similar prognostic value, but in the elderly group, the IPSS-M model has exhibited higher predictive accuracy than the IPSS-R model. However, the researchers have also indicated that the study was a retrospective analysis from a single center, whereas MDS is a highly heterogeneous disease and thus multicenter analysis is required for confirmation.\u003csup\u003e5\u003c/sup\u003e Hence, in this study retrospective analysis of clinical data from patients visiting multiple centers is used to further confirm the performance in the prognostic evaluation and heterogeneity in different age cohorts of IPSS-M. It is helpful for clinicians to choose a more appropriate MDS prediction model in clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe study was registered at clinicaltrials.gov under the identifier NCT03903055. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang Chinese Medical University (with ethics approval number: 2019-KL-002-02), and was conducted in accordance with the ethical standards of the institutional and/or national research committee and the Declaration of Helsinki.113 patients diagnosed with MDS from 10 clinical centers (The First Affiliated Hospital of Zhejiang Chinese Medical University, The First Affiliated Hospital of Nanjing Medical University, The First Affiliated Hospital, College of Medicine, Zhejiang University, The Affiliated Jinhua Hospital of Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Shaoxing People's Hospital, Tongde hospital of zhejiang province, Taizhou Central Hospital, Dongyang Hospital Affiliated to Wenzhou Medical University, The Affiliated Hospital of Shaoxing University) during April 2019 - June 2022 were enrolled. The diagnosis was based on the World Health Organization (WHO) revised standards in 2016, and their basic information (e.g., gender, age, diagnosis date, follow-up time, leukemia transformation time, time of death) was collected. The relevant laboratory tests (e.g., blood routine, bone marrow morphology, identification of abnormal chromosomes as well as mutant genes) were performed.\u003c/p\u003e \u003cp\u003eThe 113 subjects were thereafter divided into different risk groups in IPSS-R model and IPSS-M model. In IPSS-R model, the subjects were divided into the very low risk group, the low risk group, intermediate risk group, high risk group, and the very high-risk group. On the contrary, in IPSS-M model, the subjects were divided into the very low risk group, the low risk group, moderate risk group, moderate high-risk group, high risk group, and the very high-risk group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data analysis was conducted using SPSS 23.0, with count data expressed as the number of cases and percentage, the measurement data expressed as Md (IQR), and inter group numerical variables using Kruskal Wallis test. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a statistically significant difference; Overall survival (OS) is defined as the time interval from the diagnosis to death or the last follow-up, whereas leukemia-free survival (LFS) is defined as the time interval from diagnosis to leukemia transformation or death or the last follow-up. The Kaplan-Meier method was used to estimate OS, and univariate and multivariate analyses were conducted by employing the Cox risk regression model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePatient and public involvement\u003c/h2\u003e \u003cp\u003ePatients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003ePatient characteristics\u003c/h2\u003e\n\u003cp\u003eAmong the 113 subjects, 72 were males (63.7%) and 41 were females (36.3%). The median age of enrolled patients was 65 years old (inter-quartile range [IQR]\u0026thinsp;=\u0026thinsp;56, 71), and the median survival was 17 months ([IQR]\u0026thinsp;=\u0026thinsp;9, 28.5). According to the revised WHO standards in 2016, 113 subjects were diagnosed as MDS/MPN subtype 4 cases (3.5%), MDS-EB1 41 cases (36.3%), MDS-EB2 32 cases (28.3%), MDS-MLD 26 cases (23.0%), and MDS-RS 4 cases (3.5%), MDS-SLD 3 cases (2.7%), MDS-U 3 cases (2.7%). The median bone marrow blasts were 5.3% ([IQR]\u0026thinsp;=\u0026thinsp;2.0, 10.25), the median hemoglobin level was 70 g/L ([IQR]\u0026thinsp;=\u0026thinsp;57.5, 93.5), the median platelet count was 46\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L ([IQR]\u0026thinsp;=\u0026thinsp;24.5, 81.5), and the median neutrophil count was 1.2\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L ([IQR]\u0026thinsp;=\u0026thinsp;0.62, 2.22). According to the IPSS-R and IPSS-M scoring standards, 113 subjects were then grouped and IPSS-R model was used for predicting the risk level, with 1 person in the very low risk group (0.9%), 10 people in the low risk group (8.8%), 35 people in the intermediate risk group (31%), 35 people in the high risk group (31%), and 32 people in the very high risk group (28.3%), as depicted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In addition, by using the IPSS-M model, there were 1 case belonging to very low risk group (0.9%), 4 people in the low risk group (3.5%), 11 people in the moderate low risk group (9.7%), 8 people in the moderate high risk group (7.1%), 31 people in the high risk group (27.4%), and 58 people in the very high risk group (51.3%), as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The percentage of bone marrow blasts, hemoglobin, platelet, neutrophil count, LFS of the 113 subjects showed no statistical difference in the young group (\u0026lt;\u0026thinsp;60 years old) and the elderly group (\u0026ge;\u0026thinsp;60 years old), while IPSS-M and OS showed a statistical difference between the two age groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTable 1 Clinical characteristics of younger (＜60 years) and older (\u0026ge;60 years) MDS patients\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026lt;60years(n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;60years(n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBM blasts(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.3(2.0,10.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.815(1.4,8.875)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6(2.12,11.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.153\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHb(g/L)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70(57.5,93.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.5(58.75,105)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68(51,83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.107\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePlt(\u0026times;10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46(24.5,81.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.5(21.5,81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47(26,81.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.486\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeu(\u0026times;10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.2(0.62,2.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.965(0.515,1.595)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26(0.7,2.625)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOS(months)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17(9,28.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.5(12,40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(8,25.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLFS(months)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.5(5,22.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIPSS-R chromosome classification\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVery good\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(1.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e0.744\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003egood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59(52.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41(53.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eintermediate\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20(17.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(22.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12(15.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epoor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(4.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(2.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(5.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVery poor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28(24.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9(25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(24.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIPSS-R classification\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVery low\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(1.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10(8.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7(19.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(3.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIntermediate\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35(31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12(33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23(29.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35(31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9(25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26(33.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVery high\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32(28.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(22.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24(31.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIPSS-M classification\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVery low\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(2.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2(5.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2(2.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModerate low\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11(9.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(22.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModerate high\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(7.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(8.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(6.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31(27.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9(25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22(28.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVery high\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58(51.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13(36.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45(58.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BM, bone marrow; Hb, hemoglobin; Plt, platelets.；Neu, neutrophils\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTypical mutant genes and abnormal chromosomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the IPSS-M model, mutations could be mainly divided into 16 main prognostic genes (\u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eCBL, DNMT3A, ETV6, EZH2, FLT3, IDH2, KRAS, MLLPTD, NPM1, NRAS, RUNX1, SF3B1, SRSF2, TP53multihit\u003c/em\u003e and \u003cem\u003eU2AF1\u003c/em\u003e) and 15 residual genes (\u003cem\u003eBCOR, BCORL1, CEBPA, ETNK1, GATA2, GNB1, IDH1, NF1, PHF6, PPM1D, PRPF8, PTPN11, SETBP1, STAG2, and WT1\u003c/em\u003e).\u003csup\u003e4\u003c/sup\u003e We found a total of 74 mutant genes (see Figure 1 A), among which the common mutant genes (\u0026gt;10% of the total number of mutations) included \u003cem\u003eASXL1, TET2, RUNX1, TP53, SRSF2, BCOR, DNMT3A, and U2AF1\u003c/em\u003e. Interestingly, upon comparing the 17 mutant genes with a total number of mutations \u0026ge;5% by age group, it was found that \u003cem\u003eASXL1\u0026nbsp;\u003c/em\u003eand \u003cem\u003eU2AF1\u003c/em\u003e mutations were more common in the young group (\u0026lt;60 years old), whereas \u003cem\u003eDNMT3A\u003c/em\u003e and \u003cem\u003eBCOR\u003c/em\u003e mutations are relatively more common in the elderly group (\u0026ge; 60 years old) (see Figure 1 B). Among the 113 subjects, 102 (90.3%) had at least one mutant gene, 86 (76.1%) had no less than two mutant genes, and 65 (57.5%) had no less than three mutant genes. In addition, 61 of them (54%) had at least one abnormal chromosome and 27 of them (23.9%) were complex karyotypes; 54 of them (47.8%) had both gene mutation and chromosome abnormality, 47 of them (41.6%) had gene mutation but did not show chromosome abnormality, 7 of them (6.2%) had chromosome abnormality but no gene mutation, while 4 of them (3.5%) had neither gene mutation nor chromosome abnormality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe risk groups in the IPSS-M model and the IPSS-R model were then compared in terms of OS. As observed, the risk groups in both the models exhibited statistical differences in the survival (P\u0026lt;0.05, see Figure 2 A,B,C). Leukemia-free survival analysis revealed that higher IPSS-R and IPSS-M risk stratification was linked with shorter LFS time, as shown in Figure 2 D,E,F. Univariate analysis revealed that age (\u0026ge;60 years old or not), percentage of bone marrow blasts, IPSS-R chromosome classification, and \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003emutation were significant risk factors affecting prognosis (P\u0026lt;0.05, see Table 2). Moreover, multivariate analysis indicated that age (\u0026ge;60 years old or not) (HR = 3.504 (1.409-8.716)), IPSS-R chromosome classification (HR = 1.456 (1.044-2.030)), \u003cem\u003eRUNX1\u003c/em\u003e mutation (HR = 3.146 (1.174-8.431)), \u003cem\u003eDNMT3A\u003c/em\u003e mutation (HR = 6.489 (1.784-23.604)), \u003cem\u003eCBL\u003c/em\u003e mutation (HR = 0.075(0.008-0.702)), \u003cem\u003eGNAS\u003c/em\u003e mutation (HR = 0.016 (0.000-0.718)), and \u003cem\u003eFLT3_ITD\u003c/em\u003e mutation (HR = 0.062(0.004-0.914)) were related with OS (P\u0026lt;0.05) , as depicted in Table 2. Additionally, both univariate and multivariate analyses revealed that \u003cem\u003eWT1\u003c/em\u003e mutation was related to LFS (P\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eTable 2 Multivariable analysis of prognostic factors for overall survival in patients with MDS\u003c/p\u003e\n\u003ctable border=\"1\" width=\"605\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariable\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"11.570247933884298%\"\u003e\n\u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47.61904761904762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52.38095238095238%\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eage\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e\u0026ge;60y\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e3.225（1.684-6.177）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e3.504（1.409-8.716）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eBM blast\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e1.067(1.020-1.115)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eHb\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e0.994(0.984-1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePlt\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e0.999(0.996-1.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.343\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeu\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e1.046(0.990-1.105)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eIPSS-R chromosome classification\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e1.249(1.037-1.505)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e1.456（1.044-2.030）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eASXL1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e0.727(0.431-1.225)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.231\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eTET2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e0.943(0.550-1.616)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eRUNX1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e1.042(0.576-1.884)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.891\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e3.146(1.174-8.431)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eTP53\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e3.842(2.125-6.947)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSRSF2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e0.935(0.489-1.787)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eBCOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e0.943(0.494-1.802)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eDNMT3A\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e1.257(0.637-2.479)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e6.489(1.784-23.604)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eU2AF1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e1.282(0.632-2.602)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSTAG2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e0.805(0.292-2.222)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.676\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eNRAS\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e2.196(0.995-4.843)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e11.68(2.623-52.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eWT1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e0.992(0.427-2.306)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.986\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eEZH2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e0.943(0.378-2.357)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.901\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.801652892561982%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSETBP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.446280991735538%\"\u003e\n\u003cp\u003e1.309(0.524-3.271)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.570247933884298%\"\u003e\n\u003cp\u003e0.564\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29.09090909090909%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9.090909090909092%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eGATA2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.907(0.363-2.264)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eCREBBP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e1.232(0.494-3.070)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eCBL\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.561(0.175-1.796)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e0.075(0.008-0.702)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eETV6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.944(0.343-2.598)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eCUX1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.704(0.172-2.885）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.626\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eCSF3R\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.513(0.125-2.097)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSF3B1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.669(0.209-2.147)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eNF1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.606(0.148-2.486)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSH2B3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.335(0.046-2.424)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eNPM1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e1.326(0.324-5.431)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.695\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eCEBPA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e1.654(0.517-5.294)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e47.215(2.275-980.028)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eMPL\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.387(0.054-2.794)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eKRAS\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e1.567(0.488-5.037)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.451\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eIDH2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e1.088(0.265-4.465)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.906\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eZRSR2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e1.887(0.587-6.064)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eGNAS\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.503(0.070-3.638)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.496\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e0.016(0.000-0.718)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e0.033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eFLT3_ITD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.936(0.228-3.836)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.927\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e0.062(0.004-0.914)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eKMT2D\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.422(0.058-3.052)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eDDX41\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e0.687(0.095-4.971)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23.762376237623762%\"\u003e\n\u003cp\u003e\u003cstrong\u003eTTN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26.402640264026402%\"\u003e\n\u003cp\u003e2.45(0.594-10.102)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11.551155115511552%\"\u003e\n\u003cp\u003e0.215\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"29.207920792079207%\"\u003e\n\u003cp\u003e20.771(2.932-147.129)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8.910891089108912%\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0.16501650165016502%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRating model prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the two models, 72 people (63.7%) were reclassified after subgrouping from IPSS-R to IPSS-M, and 52 of them were reassigned to the higher risk group after being identified under a new risk level. According to the patient's OS outcome (i.e., death), the receiver operating characteristic (ROC) curve was plotted, which showed AUC=0.629 for the IPSS-R model and ACU=0.705 for the IPSS-M model respectively. It was observed that for the young group, the AUC of the IPSS-R model was 0.515, and the AUC of the IPSS-M model was 0.635; For the elderly group, the AUC of the IPSS-R model was 0.658, and the AUC of the IPSS-M model was 0.691 (see Figure 3 A,B,C). According to the patient's LFS outcome (i.e., leukemia or death), a ROC curve was drawn, depicting AUC=0.652 for the IPSS-R model and AUC=0.680 for the IPSS-M model. In addition, for the young group, AUC=0.635 for the IPSS-R model and AUC=0.656 for the IPSS-M model=0.656 was observed but for the elderly group, AUC=0.633 for the IPSS-R model and AUC=0.642 for the IPSS-M model was obtained (see Figure 3 D,E,F).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMDS is a highly heterogeneous hematological malignancy characterized by significant prognostic differences. It has been established that Incorporating genes into prognostic scoring tools is undoubtedly a good risk level method, but there are still significant differences reported in mutant genes among different populations. Next-generation sequencing (NGS) technology is an important tool for genomics research, which can obtain useful genomic information with high throughput, thereby providing novel ideas and methods for disease diagnosis and prognostic evaluation. Moreover, with the development of NGS technology, it has been widely applied in the research related to MDS. A number of prior studies have also found that mutations in various gens can exhibit great value for the prognostic evaluation of MDS in their research at the molecular level, for instance, SF3B1 mutations are predictors of favourable prognosis, while driver mutations of other genes (such as ASXL1, SRSF2, RUNX1, TP53, JAK2and IDH2) are associated with a reduced probability of survival and increased risk of disease progression.\u003csup\u003e6-22\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe establishment of the revised International Prognostic Scoring System (IPSS-R) model was based on the evaluation of data from 7012 patients from multiple institutional databases in the IWG-PM joint database. The median age of patients was 71 years old, 77% was 60 years old, and Greenberg \u003cem\u003eet al.\u003c/em\u003e proposed that age had minimal impact on the prognosis of MDS.\u003csup\u003e2\u003c/sup\u003e The clinical data analysis of the study subjects indicated that there was no statistical difference in percentage of the marrow blasts, hemoglobin, platelet, neutrophil count and cell genetic abnormality between the two age groups, which also confirmed the Greenberg\u0026rsquo;s viewpoint. However, somatic cell gene mutation has not been used for determination of the risk level of MDS, so the molecular International Prognostic Scoring System (IPSS-M) model has significantly increased the weight of gene mutation, and included more accurate risk scores which could be helpful for clinical selection of more accurate treatment plans.\u003csup\u003e4\u003c/sup\u003e Bernard\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e reported that the risk level of young patients and elderly patients was not significantly different during the establishment of the IPSS-M model. However, WU\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e found that IPSS-M has a relatively higher predictive accuracy among the elderly group. The results of our study also confirmed WU J\u0026apos;s viewpoint (AUC = 0.635 for the young group and 0.691 for the elderly group), which risk stratification by the IPSS-M model showed a higher proportion of patients aged \u0026ge;60 years in higher risk groups and the OS of the elderly group was significantly shorter than that of the young group, which could be be related to the comorbidities(such as myocardial infarction, cerebrovascular disease) of elderly patients.\u003c/p\u003e\n\u003cp\u003eThe common mutant genes found in this research cohort (mutation number\u0026gt;10%) included \u003cem\u003eASXL1, TET2, RUNX1, TP53, SRSF2, BCOR, DNMT3A, U2AF1\u003c/em\u003e, which was basically consistent with other prior research reports on the common mutant genes diagnosed in hematological diseases.\u003csup\u003e7 9 10 12 14 16 23\u003c/sup\u003e The major difference was that we found in this cohort study that \u003cem\u003eASXL1\u003c/em\u003e mutation was more common among the young patients. \u003cem\u003eASXL1\u003c/em\u003e is an epigenetic regulatory gene, and is one of the most frequently mutated genes in all subtypes of myeloid malignancies, and mutation of this gene is often detected in clonal hematopoiesis. Somatic mutation is relatively rare in people younger than 40 years old, but the frequency has been observed to increase significantly with age.\u003csup\u003e23\u003c/sup\u003e Although this study found that \u003cem\u003eASXL1\u003c/em\u003e was more common in the young group, the median age of the young group was also 54 (IQR=48.5, 57) years old. In addition, a number of previous studies about unexplained blood cytopenia also demonstrated that the positive predictive value of spliceosome gene mutation and replacement mode for the myeloid tumors involving \u003cem\u003eTET2, DNMT3A or ASXL1\u003c/em\u003e was 0.86 to\u0026nbsp;1.0,\u003csup\u003e10\u003c/sup\u003e which could also be attributed to \u003cem\u003eASXL1\u003c/em\u003e mutation that causes the disease in these patients.\u003csup\u003e8\u003c/sup\u003e Interestingly, there are also studies indicating that shorter OS of MDS patients was associated with ASXL1.\u003csup\u003e6\u003c/sup\u003e We found that \u003cem\u003eU2AF1\u003c/em\u003e mutation was also more common in the young group, which was consistent with the results reported by Li, B., et al.,\u003csup\u003e24\u003c/sup\u003e but a larger sample size is still needed to further confirm.\u003c/p\u003e\n\u003cp\u003eThe results of univariate and multivariate analyses have established that age, bone marrow blasts, IPSS-R cytogenetic category, \u003cem\u003eTP53\u003c/em\u003e mutation, \u003cem\u003eRUNX1\u003c/em\u003e mutation, \u003cem\u003eDNMT3A\u0026nbsp;\u003c/em\u003emutation, and \u003cem\u003eNRAS\u003c/em\u003e mutation could serve as important factors influencing the OS and prognosis of MDS patients. Older age may lead to the poor prognosis, which could be related to comorbidities. Zipperer \u003cem\u003eet al.\u003c/em\u003e believed that existence of comorbidities could be an important factor affecting the prognosis of MDS patients, and that elderly patients usually have more comorbidities in comparison to young patients.\u003csup\u003e25\u003c/sup\u003e Martin also postulated that age should become a part of the transplant decision-making process and should be integrated into the scoring system for predicting hematopoietic stem cell transplantation (HSCT)results in MDS in the future.\u003csup\u003e26\u003c/sup\u003e The percentage of marrow blasts has been found to be related to its diagnostic typing, and cellular genetic abnormality can serve as an important component of clinical evaluation of various malignant hematological diseases.\u003csup\u003e27\u003c/sup\u003e It has been demonstrated that \u003cem\u003eNRAS, TP53, DNMT3A, and RUNX1\u003c/em\u003e mutations are poor prognostic factors of MDS.\u003csup\u003e9 16 19-22 28-30\u003c/sup\u003e Additionally, both univariate and multivariate analyses revealed that \u003cem\u003eWT1\u0026nbsp;\u003c/em\u003emutation was a poor prognostic factor of LFS. Huang \u003cem\u003eet al.\u003c/em\u003e also demonstrated that overexpression of \u003cem\u003eWT1\u003c/em\u003e at the time of diagnosis can effectively predict poor survival and early AML evolution in MDS patients with reduced platelet count.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the IPSS-M model showed better predictive accuracy for OS compared to IPSS-R (AUC of IPSS-R = 0.629, AUC of IPSS-M = 0.705), and the accuracy of the two models for LFS prediction using a plotted ROC curve was also observed to be comparable (AUC of IPSS-R = 0.652, AUC of IPSS-M = 0.680), which was consistent with findings of the previous studies.\u003csup\u003e4 32 33\u003c/sup\u003e Survival analysis using the KM method also showed that the risk stratification of the IPSS-M and IPSS-R model was statistically significant for LFS analysis.The OS prediction accuracy of both IPSS-R and the IPSS-M model in the young group cohort was relatively low (AUC of IPSS-R = 0.515, AUC of IPSS-M = 0.635). Thus, considering the possibility of allogeneic hematopoietic stem cell transplantation affecting survival in this group of patients, 11 people (30.5%) in the young group were subjected to allogeneic hematopoietic stem cell transplantation. It has been reported earlier that young MDS patients undergoing allogeneic hematopoietic stem cell transplantation had a longer survival rate,\u003csup\u003e34-36\u003c/sup\u003e whereas the predictive accuracy of both was found to be comparable and slightly higher for the IPSS-M model (AUC of IPSS-R = 0.658, AUC of IPSS-M = 0.691) in the elderly group, which was in line with the results of other investigators.\u003csup\u003e4 32\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAccording to this multicenter retrospective study on IPSS-M model validation, the addition of mutant genes displayed significantly higher value for the prognostic evaluation of MDS patients, especially for the prediction the elderly group. However, there are still several limitations associated with this study. Although the subjects of this study are from multiple centers, the overall number of cases analyzed were insufficient and the number of genetic testing items for the subjects were also different. Thus, involvement of patients from more centers, more cases, and more unified testing items for mutated genes are needed to further confirm the performance in prognostic evaluation of the IPSS-M model and its heterogeneity across different age groups.\u003c/p\u003e"},{"header":"Strengths and limitations of this study","content":"\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThis study compared the prognostic values of IPSS-R and IPSS-M in MDS patients in Jiangnan region of China using real-world outcome data.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis study confirmed whether IPSS-M can enhance the prognostic potential and applies to Chinese MDS patients.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe overall cases were relatively insufficient and the packages of genetic testing items were also not uniform in this study.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis was a retrospective analysis.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eThe authors thank all of the study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e Mengmeng Hu, Ming Zhou, Shengyun Lin, Baodong Ye and Qinghong Yu contributed to the concept and design of the study. Yingying Shen, Guangsheng He, Li Huang, Shujuan Zhou , Jiaping Fu,Huifang Jiang, Sai Chen, Xiujin Ye, Zhiyin Zheng, Liqiang Wu, Bo Wang, Gongqiang Wu, and Qinghong Yu managed the data collection.Mengmeng Hu, Shengyun Lin and Qinghong Yu were directly responsible for the analysis of data. Mengmeng Hu was responsible for the initial draft of this manuscript. Qinghong Yu submitted and revised the article and acts as guarantor for the final manuscript. All authors read and approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eOur study complies with the Declaration of Helsinki and was approved by the hospital ethics committee. (2019-KL-002-02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe present study was supported by the Great Item of Science and Technology Planning Project of Zhejiang Province (grant no. 2019C03047)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and public involvement\u003c/strong\u003e Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eKhoury JD, Solary E, Abla O, et al. 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Allogeneic hematopoietic stem cell transplantation for myelodysplastic syndrome in adolescent and young adult patients. \u003cem\u003eBone Marrow Transplant\u003c/em\u003e 2021;56:2510-7. doi:10.1038/s41409-021-01324-8\u003c/li\u003e\n\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-4129078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4129078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e The Revised international prognostic scoring system (IPSS-R) is now commonly being used clinically to guide the treatment of myelodysplastic neoplasms (MDS). Recently, the Molecular International Prognostic Scoring System (IPSS-M)was proposed. In this study, we have validated the potential predictive value of the comparative IPSS-M in Chinese MDS patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e Retrospective multicenter observational study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting and participants\u003c/strong\u003e 113 MDS patients(April 2019 - June 2022) from 10 distinct centers in Jiangnan region of China, grouped by IPSS-R and IPSS-M was obtained and the scoring criteria were retrospectively analyzed to compare the prognostic assessment efficacy of the different prognostic assessment systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain outcome measures\u003c/strong\u003e The prognostic indicators of MDS patients are main outcome measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e 72 (63.7%) patients were reclassified after regrouping from IPSS-R to IPSS-M, and 52 of them were transferred to a higher risk group, with a higher percentage of patients aged ≥ 60 years in the higher risk group. Survival analysis confirmed that overall survival(OS) was variable in the different risk strata, with shorter survival time in the higher risk group and lower OS in the older(≥ 60 years) than in the younger group; whereas in univariate and multifactorial analysis, age ≥ 60 years, percentage of bone marrow blasts, chromosomal classification of IPSS-R, TP53, RUNX1, DNMT3A, NRAS, CBL, GNAS, and FLT3_ITD gene mutation were associated with OS. Leukemia-free survival(LFS)analysis revealed that higher IPSS-R and IPSS-M risk stratification was linked with shorter LFS time. Receiver operating characteristic (ROC) curves were drawn according to OS displaying AUC = 0.629 for IPSS-R and AUC = 0.705 for IPSS-M; AUC = 0.635 for IPSS-M younger group and AUC = 0.691 for older group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e Our study confirmed that the IPSS-M prognostic scoring system could be applicable to Chinese patients and that IPSS-M was significantly better than IPSS-R for the prognostic assessment of MDS patients. Moreover, IPSS-M appeared to have better predictive validity in older patients compared to younger patients.\u003c/p\u003e","manuscriptTitle":"Validation of the Molecular International Prognostic Scoring System (IPSS-M) for myelodysplastic neoplasms (MDS) and comparison with the revised International Prognostic Scoring System (IPSS-R) in Chinese Population: A Multicenter Retrospective Study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 18:10:40","doi":"10.21203/rs.3.rs-4129078/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":"3634420e-8380-492f-8394-57d5a377c8f2","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-22T13:30:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-25 18:10:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4129078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4129078","identity":"rs-4129078","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}