Prognostic Impact of Lymphocyte to Monocyte Ratio in Patients with Myelodysplastic Neoplasms/Syndromes

Prognostic Impact of Lymphocyte to Monocyte Ratio in Patients with Myelodysplastic Neoplasms/Syndromes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Impact of Lymphocyte to Monocyte Ratio in Patients with Myelodysplastic Neoplasms/Syndromes Wan-Hsuan Lee, Chien-Chin Lin, Xavier Cheng Hong Tsai, Chia-Lang Hsu, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7524368/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose Myelodysplastic syndromes/neoplasms (MDS) represent a heterogeneous group of clonal hematopoietic disorders with variable prognosis. While several risk models exist, the prognostic role of immune-related biomarkers remains unclear. This study aimed to determine whether the lymphocyte-to-monocyte (L/M) ratio at diagnosis serves as an independent prognostic factor in MDS and to explore its biological correlates. Methods A retrospective analysis of 554 patients with primary MDS diagnosed at the National Taiwan University Hospital was conducted. Patients were stratified by an L/M ratio cutoff of 1.5, determined by maximally selected rank statistics. Clinical, cytogenetic, and mutational profiles were assessed. Survival outcomes were analyzed using Kaplan–Meier methods and multivariable Cox regression incorporating IPSS-R, IPSS-M, and WHO-2022/ICC classifications. RNA sequencing was performed on diagnostic bone marrow samples to evaluate transcriptomic differences between groups. Results Patients with L/M ratio > 1.5 were younger, had lower platelet counts, more advanced subtypes, and higher frequencies of STAG2 and U2AF1 mutations. Elevated L/M ratio was significantly associated with inferior leukemia-free and overall survival, independent of established prognostic models. Adverse prognostic effects were mitigated by allogeneic hematopoietic stem cell transplantation but not by hypomethylating agents. Transcriptomic analysis revealed downregulation of inflammatory pathways (IL-2–STAT5, IL6–JAK–STAT3, interferon responses) and the p53 pathway, along with enrichment of MYC targets in the high L/M group. Conclusion An elevated L/M ratio is an independent and readily available biomarker that predicts poor outcomes in MDS. Integration of this parameter into existing risk models may refine prognostication and guide treatment intensity. Transcriptomic findings suggest immune suppression and p53 deregulation underlie its adverse impact, highlighting potential therapeutic avenues. myelodysplastic syndromes/neoplasms prognosis risk stratification lymphocyte monocyte Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Myelodysplastic syndromes/neoplasms (MDS), a broad category of clonal myeloid disorders, are characterized by dysregulated hematopoiesis, which leads to cytopenia and dysplastic hematopoietic cells. Clinical and genetic heterogeneity, recurrent chromosomal abnormalities, and variable prognostic outcomes are features of MDS [ 1 ]. To risk-stratify patients with MDS and direct treatment, several prognostic models have been developed, such as the International Prognostic Scoring System (IPSS) [ 2 ], revised IPSS (IPSS-R) [ 3 ], molecular IPSS (IPSS-M), World Health Organization (WHO) Classification-based Prognostic Scoring System [ 4 ], and MD Anderson Prognostic Scoring System [ 5 ]. These models primarily incorporate parameters such as cytopenia severity, cytogenetic abnormalities, bone marrow (BM) blast percentage, gene mutations, and transfusion dependency. Recent studies have also highlighted the prognostic relevance of peripheral lymphocyte and monocyte counts in solid tumors and hematologic malignancies [ 6 – 11 ]. In MDS, absolute lymphocyte count (ALC) and absolute monocyte count (AMC) at diagnosis are individually associated with patient outcomes [ 12 , 13 ]. Lymphopenia adversely affects survival in IPSS-M-defined low-risk MDS patients [ 14 ], whereas monocytopenia, observed in 29.5% of MDS, correlates with higher blast counts and worse outcomes [ 12 , 13 ]. These findings suggest that peripheral immune cell profiles may reflect the underlying disease biology and immune dysregulation. Given the interplay between these two values, the lymphocyte-to-monocyte (L/M) ratio presents as a more integrative and robust biomarker; however, studies regarding the prognostic implications of the L/M ratio in patients with MDS are scarce. Methods At the National Taiwan University Hospital (NTUH), data were collected from 554 patients with primary MDS who were diagnosed and treated at NTUH. We retrospectively reviewed a cohort of 554 patients diagnosed with primary MDS based on the WHO-2016 criteria, with subsequent reclassification based on the WHO-2022 and International Consensus Classification (ICC). The survival impact of the L/M ratio was evaluated in the context of the novel classification systems IPSS-R and IPSS-M. To avoid confounding factors, patients with a history of chemotherapy, radiation, or hematologic malignancies were excluded, given the distinct mutational profiles and clinical outcomes of primary and secondary MDS [ 15 ]. The TruSight Myeloid Panel and HiSeq platform (Illumina, San Diego, CA, USA) were used to sequence the cryopreserved BM samples and identify mutations in 54 myeloid-related genes [ 16 , 17 ] (Supplemental Table 1). TP5 3 copy-neutral loss of heterozygosity and five residual genes ( ETNK1, GNB1, NF1, PPM1D , and PRPF8 ) identified using the IPSS-M model were not evaluated in this study. By following the manufacturer's instructions, the library was prepared and sequenced to achieve a median read depth of 10,550 x. Variant analysis used databases for somatic mutation annotation and interpretation, including COSMIC v86, dbSNP v151, ClinVar, PolyPhen-2, and SIFT. A variant analysis diagnostic algorithm has been previously described [ 18 ]. Polymerase chain reaction (PCR) and fluorescence capillary electrophoresis are required for FLT 3-ITD analysis due to the limitations of next-generation sequencing (NGS), whereas Sanger sequencing and PCR are required for KMT2A -PTD analysis [ 19 , 20 ]. Cellularity and fibrosis of the BM were evaluated and verified by pathologists using reticulin staining. Cytogenetic analyses were performed according to the International System for Human Cytogenetic Nomenclature in cytogenetic analyses [ 20 , 21 ]. Following the manufacturer’s instructions, RNA was extracted from diagnostic BM samples (without CD34 + cell isolation), and sequencing libraries were created using the TruSeq Stranded mRNA Library Prep Kit (Illumina). The libraries were subsequently sequenced using a 150-bp paired-end read mode on an Illumina NovaSeq 6000. STAR (v2.7) was used to align the clean reads to the human reference genome GRCh38 after adapter sequencing, and low-quality bases were eliminated from the raw sequencing data using Cutadapt (v3.0) in two-pass mode. Each gene's raw count was determined using GENCODE v28 annotation and was then converted to transcripts per million for additional analysis [ 22 ]. The NTUH Research Ethics Committee approved this study (approval number: 20220705RINB). Each participant provided written informed consent in compliance with the Declaration of Helsinki. Statistical analysis Fisher's exact or χ² test for categorical variables and the Mann–Whitney U test for continuous variables were used in the statistical analyses. The time between diagnosis and leukemic transformation, death, or last follow-up was referred to as leukemia-free survival (LFS). The relationship between the date of diagnosis and the last follow-up or death from any cause was known as overall survival (OS). Survival curves were produced using Kaplan–Meier analysis, and the log-rank test was used to determine significance. For both univariable and multivariable analyses, Cox proportional hazards models were used. A time-dependent covariate was thought to be allogeneic hematopoietic stem cell transplantation (HSCT) [ 23 ]. For variant allele frequency exploration, maximally selected rank statistics were used [ 24 , 25 ]. In our analysis of the relationship between the variant allele frequency of mutated genes and survival, this approach was a suitable standardized two-sample linear rank statistic to determine the maximum standardized statistics of all potential cutoffs, which offered the best separation of the results into two groups [ 26 , 27 ]. By selecting replacement samples of the same size from the original dataset, bootstrapping replicated the process of creating samples from an underlying population. The results were tested on individuals excluded from the bootstrap or original samples [ 28 ]. All P values were two-sided, and at P < 0.05, they were deemed statistically significant. IBM SPSS Statistics v23 for Windows was used for all analyses. Results Clinical characteristics and genetic profiles The demographic features are presented in Table 1 . For the total cohort, the median age was 67.3 years, with a male predominance (63.7%). According to the WHO-2016 classification, half (49.9%) of the patients had MDS with excess blasts (EB), including EB1 (19.9%) and EB2 (30.0%). When classifying patients with ICC, there were 76 (13.7%) and 11 (2.0%) patients with MDS/AML who had myelodysplasia-related gene mutations or myelodysplasia-related cytogenetic abnormalities, respectively (Table 1 ). A total of 21 (3.8%) and 36 (6.5%) patients met the diagnostic criteria for MDS with mutated TP53 and MDS/AML with mutated TP5 3, respectively, based on different blast percentages (Table 1 ). For the WHO-2022 classification, 83 (15.0%) individuals had hypocellular marrow, 12 (2.2%) had significant BM fibrosis, and the increased blasts were grouped as hypoplastic MDS (MDS-h) or MDS with fibrosis (MDS-f) (Table 1 ). Table 1 Comparison of clinical characteristics between patients with high (> 1.5) or low ( ≦ 1.5) lymphocyte/monocyte ratio Clinical characters Total (n = 554) L/M ≤ 1.5 (n = 206) L/M > 1.5 (n = 348) P value Sex 0.273 Female 201 (36.3) 81 (39.3) 120 (34.5) Male 353 (63.7) 125 (60.7) 228 (65.5) Age* 67.3 (18.4–94.5) 68.6 (19.3–94.5) 66.6 (18.4–94.2) 0.015 Laboratory data* WBC, ×10 9 /L 3.39 (0.6-32.39) 3.44 (0.6-26.31) 3.36 (0.6-32.39) 0.588 ANC, ×10 9 /L 1.55 (0-23.48) 1.66 (0-15.65) 1.49 (0.01–23.48) 0.238 ALC, ×10 9 /L 1.17 (0.07–10.26) 1.03 (0.07–2.98) 1.28 (0.08–10.26) < 0.001 Monocyote, ×10 9 /L 0.22 (0.01–5.72) 0.36 (0.03–3.54) 0.15 (0.01–5.72) < 0.001 Hb, g/dL 8.1 (2.6–17.1) 8.2 (3.4–14.1) 8.0 (2.6–17.1) 0.197 Platelet, ×10 9 /L 81 (1-721) 148 (7-655) 52 (1-721) < 0.001 BM blast (%) 4.4 (0-19.5) 4.0 (0-19.2) 4.9 (0-19.5) 0.528 PB blast (%) 0 (0-18.6) 0 (0–17.0) 0 (0-18.6) 0.123 IPSS-R 0.008 Very low 20 (3.6) 11 (5.3) 9 (2.6) 0.093 Low 152 (27.4) 72 (35.0) 80 (23.0) 0.003 Int 141 (25.5) 46 (22.3) 95 (27.3) 0.194 High 116 (20.9) 39 (18.9) 77 (22.1) 0.372 Very high 125 (22.5) 38 (18.5) 87 (25.0) 0.075 IPSS-M 0.003 Very low 16 (2.9) 9 (4.4) 7 (2.0) 0.109 Low 119 (21.5) 61 (29.6) 58 (16.7) < 0.001 Moderate low 81 (14.6) 29 (14.1) 52 (14.9) 0.781 Moderate high 83 (15.0) 23 (11.2) 60 (17.2) 0.053 High 92 (16.6) 31 (15.0) 61 (17.5) 0.448 Very high 163 (29.4) 53 (25.7) 110 (31.6) 0.142 2016 WHO classification < 0.001 MDS-5q 5 (0.9) 1 (0.5) 4 (1.1) 0.656 MDS-SLD 79 (14.3) 40 (19.4) 39 (11.2) 0.008 MDS-MLD 126 (22.7) 43 (20.9) 83 (23.9) 0.419 MDS-RS-SLD 37 (6.7) 24 (11.7) 13 (3.7) < 0.00 1 MDS-RS-MLD 24 (4.3) 12 (5.8) 12 (3.4) 0.184 MDS-EB1 110 (19.9) 38 (18.4) 72 (20.7) 0.522 MDS-EB2 166 (30.0) 48 (23.3) 118 (33.9) 0.008 MDS-U 7 (1.3) 0 (0.0) 7 (2.0) 0.050 ICC 0.002 MDS 413 (74.5) 159 (77.2) 254 (73.0) 0.273 del(5q) 5 (0.9) 1 (0.5) 4 (1.1) 0.656 mutated SF3B1 51 (9.2) 32 (15.5) 19 (5.5) < 0.001 NOS, with SLD 90 (16.2) 41 (19.9) 49 (14.1) 0.073 NOS, with MLD 131 (23.6) 46 (22.3) 85 (24.4) 0.575 EB 115 (20.8) 33 (16.0) 82 (23.6) 0.034 mutated TP53 21 (3.8) 6 (2.9) 15 (4.3) 0.405 MDS/AML 141 (25.5) 47 (22.8) 94 (27.0) 0.273 MDS-related genes mutations 76 (13.7) 26 (12.6) 50 (14.4) 0.564 MDS-related cytogenetics 11 (2.0) 5 (2.4) 6 (1.7) 0.566 mutated TP53 36 (6.5) 13 (6.3) 23 (6.6) 0.890 NOS 18 (3.2) 3 (1.5) 15 (4.3) 0.083 2022 WHO classification 0.001 MDS-5q 5 (0.9) 1 (0.5) 4 (1.1) 0.656 MDS- SF3B1 67 (12.1) 40 (19.4) 27 (7.8) < 0.001 MDS-h 83 (15.0) 27 (13.1) 56 (16.1) 0.341 MDS-LB 122 (22.0) 52 (25.2) 70 (20.1) 0.159 MDS-IB1 92 (16.6) 30 (14.6) 62 (17.8) 0.588 MDS-IB2 127 (22.9) 35 (17.0) 92 (26.4) 0.006 MDS-f 12 (2.2) 5 (2.4) 7 (2.0) > 0.999 MDS-bi TP53 46 (8.3) 16 (7.8) 30 (8.6) 0.725 Treatment HMA 147 (26.5) 40 (19.4) 107 (30.7) 0.004 Intensive chemotherapy 18 (3.2) 5 (2.4) 13 (3.7) 0.401 Clinical trial 22 (4.0) 7 (3.4) 15 (4.3) 0.595 HSCT 93 (16.8) 31 (15.0) 62 (17.8) 0.400 Supportive care 243 (43.9) 105 (51.0) 138 (39.7) 0.009 Other treatment † 122 (22.0) 50 (24.3) 72 (20.7) 0.325 P values of < 0.05 are statistically significant. Data are presented as n (%) *Median (range). † Other treatment: include low-dose cytarabine, rabbit-derived anti-thymocyte globulin, cyclosporine, danazol, eltrombopag, erythropoietin-stimulating agents, thalidomide, steroid, venetoclax-based therapy and oral chemotherapy. Abbreviations: ANC, absolute neutrophil count; ALC, absolute lymphocyte count; BM, bone marrow; Hb, hemoglobin; HMA, hypomethylating agent; HSCT, allogeneic hematopoietic stem cell transplantation; ICC, International Consensus Classification; IPSS-R, revised international prognosis scoring system; IPSS-M, molecular international prognosis scoring system; L/M, lymphocyte/monocyte ratio; MDS-RS, MDS with ring sideroblasts; MDS-EB, MDS with excess blasts; MDS-SLD, MDS with single lineage dysplasia; MDS-MLD, MDS with multilineage dysplasia; MDS-RS-SLD, MDS with ring sideroblasts and single lineage dysplasia; MDS-RS-MLD, MDS with ring sideroblasts and multilineage dysplasia; MDS-U, MDS, unclassifiable; MDS-5q, MDS with low blasts and isolated 5q deletion; MDS- SF3B1 , MDS with low blasts and SF3B1 mutation; MDS-LB and RS, MDS with low blasts and ring sideroblasts; MDS-LB, MDS with low blasts; MDS-h, hypoplastic MDS; MDS-IB1, MDS with increased blasts-1; MDS-IB2, MDS with increased blasts-2; MDS-f, MDS with fibrosis; MDS-bi TP53 , MDS with biallelic TP53 inactivation; NOS, not otherwise specified; PB, peripheral blood; WBC, while blood cell count A total of 68.9% patients had IPSS-R intermediate-(25.5%), high-(20.9%), or very high-risk disease (22.5%), and a total of 61.0% patients had IPSS-M moderately high-(15.0%), high-(16.6%), or very high-risk (29.4%) disease (Table 1 ). Regarding treatments, 41.7% of patients with EB received hypomethylating agents (HMA, 41.7%) or chemotherapy (5.4%), and 19.4% of patients in the intermediate-, high-, or very-high-risk IPSS-R group underwent allogeneic HSCT. Overall, 78.2% had at least one gene mutation or cytogenetic abnormality. As shown in Supplemental Table 2, the most common mutation in this cohort was the ASXL1 mutation (21.3%), followed by TET2 (15.5%), SF3B1 (13.9%), RUNX1 (12.3%), STAG2 (11.9%), and TP53 (10.6%). When stratified based on the biological function of the affected genes, mutations in genes involved in epigenetic modifications (45.5%), including DNA methylation-related genes (26.7%) and chromatin-modifying genes (28.7%), were the most common, followed by mutations in the spliceosome complex genes (34.4%). Clinical and genetics differences between patients with high or low L/M ratio As mentioned above, we used maximally selected rank statistics to determine the optimal L/M ratio cutoff that correlated with the outcomes. Differences in clinical characteristics and genetic profiles between patients with high (> 1.5) or low (≤ 1.5) L/M ratio were explored. Specifically, patients with L/M > 1.5 were significantly younger and had lower platelet counts at diagnosis. They had a higher prevalence of EB2 (WHO-2016), EB (ICC), and IB2 (ICC) subtypes, and a lower prevalence of MDS-SLD, MDS-RS-SLD, and SF3B1 -mutated subtypes (as defined by both ICC and WHO-2022, Table 1 ). Taken together, patients with L/M > 1.5 had a lower proportion of low-risk IPSS-R or IPSS-M (Table 1 and Supplemental Fig. 1). Furthermore, those with L/M > 1.5 had more U2AF1 (9.5% vs. 4.4%, P = 0.028) and STAG2 (14.7% vs. 7.3%, P = 0.009) mutations, while they had less SF3B1 (10.6% vs. 19.3%, P = 0.004) mutations compared with those with L/M ≤ 1.5 (Fig. 1 and Supplemental Table 2). When categorized by the genetic functional group, patients with a high L/M ratio had more cohesion complex gene mutations (15.2% vs. 7.3%, P = 0.007) (Supplemental Table 2 and Fig. 1 B). Survival impact of L/M ratio Kaplan–Meier survival analysis showed that patients with L/M ratio > 1.5 had LFS and OS of 31.5 and 34.9 months, respectively, which were significantly shorter than the LFS and OS of those with L/M ratio ≤ 1.5 (78.7 months for LFS and OS, both P < 0.05) (Fig. 2 ). When censoring at the transplantation, individuals with high L/M ratio had a trend of inferior outcomes in both lower (very low, low, or intermediate-risk IPSS-R) or higher (high or very high-risk IPSS-R) risk group (in lower risk group: median LFS: 83.6 vs. 218.6 months, P = 0.075; median OS: 102.4 vs. 218.6 months, P = 0.080; in higher risk group: LFS: 10.5 vs. 15.1 months, P = 0.086; median OS: 15.2 vs. 17.7 months, P = 0.131) (Fig. 3 ). According to our previous studies [ 29 , 30 ], we defined MDS with del5(q), MDS with low blasts (MDS-LB), and MDS-LB and RS as low-risk MDS, whereas MDS with increased blasts and MDS-f were defined as high-risk MDS in the WHO-2022 classification. For the ICC, MDS with del(5q), MDS with mutated SF3B1 , and MDS, NOS with SLD or MLD were defined as low-risk MDS. The results of Cox regression analyses were internally validated using the bootstrapping method. In univariable analysis, in addition to older age, high ferritin levels, MDS classification based on the ICC or WHO-2022 classification, and risk stratification by the IPSS-R or IPSS-M, L/M > 1.5 was associated with shorter LFS (hazard ratios [HR]: 1.422, P = 0.006) and OS (HR: 1.401, P = 0.010) (Supplemental Table 3). Furthermore, we validated the thresholds relevant to the prognostic impact of ALC (1.5, or 1.2 × 10 9 /L) and AMC (0.2, or 0.3 × 10 9 /L), which had been reported previously [ 12 , 14 , 31 , 32 ] by using our cohort. No differences in survival were observed between the groups. As a continuous variable in the univariable analysis, a higher AMC was associated with shorter LFS and OS (both HR: 1.003, P < 0.001) (Supplemental Table 3). Variables with a P -value < 0.1 in univariable Cox regression analysis and allo-HSCT were used as covariates. Two models incorporating advanced ICC or WHO-2022 subtypes were used. For LFS, older age, high ferritin levels, advanced ICC or WHO-2022 subtypes, and higher IPSS-M scores were associated with worse outcomes (all P 1.5 had a trend toward shorter LFS (HR: 1.303, P = 0.094; HR: 1.358, P = 0.053), and HSCT may confer protective effects (HR: 0.597, P = 0.065; HR: 0.568, P = 0.046) (Table 2 ). For OS, older age, L/M > 1.5 (HR: 1.484, P = 0.014; HR: 1.548, P = 0.006), advanced ICC or WHO-2022 subtypes, and higher IPSS-M were independent poor prognostic factors (Table 2 ). Additionally, HSCT could improve the outcomes of patients with a high L/M ratio. For patients who did not undergo HSCT, individuals with high L/M ratio had median LFS and OS of 28.8 and 31.3 months, respectively, which were significantly worse than those of patients with a low L/M ratio (median LFS and OS: 78.7 months, both P = 0.001, Supplemental Figs. 2A and 2B). At the same time, patients with high L/M ratio receiving HSCT had similar outcomes compared to those with low L/M ratio (median LFS 53.8 and 46.7 months, P = 0.755; median OS 73.3 and 73.7 months, P = 0.759, Supplemental Figs. 2C and 2D). However, HMA treatment could not abrogate the adverse effects of a high L/M ratio ( P = 0.056 for LFS and P = 0.023 for OS). Table 2 Multivariable analysis Cox regression analysis of the impact of different variables on the leukemia-free survival and overall survival of patients with myelodysplastic syndromes/neoplasms Variable LFS OS LFS OS HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value Age * 1.027 (1.016–1.038) < 0.001 1.032 (1.020–1.043) < 0.001 1.027 (1.016–1.038) < 0.001 1.032 (1.021–1.044) < 0.001 Female 0.859 (0.627–1.175) 0.341 0.894 (0.649–1.232) 0.494 0.855 (0.624–1.170) 0.327 0.893 (0.648–1.231) 0.491 Ferritin * (X 10 2 ng/mL) 1.001 (1.000-1.001) 0.022 1.000 (1.000-1.001) 0.078 1.001 (1.000-1.001) 0.014 1.000 (1.000-1.001) 0.065 L/M > 1.5 1.303 (0.956–1.775) 0.094 1.484 (1.084–2.031) 0.014 1.358 (0.996–1.851) 0.053 1.548 (1.130–2.119) 0.006 ICC < 0.001 < 0.001 Low-risk MDS † Reference - Reference - MDS with EB 1.685 (1.074–2.644) 0.023 1.441 (0.905–2.292) 0.123 MDS/AML ‡ 2.244 (1.344–3.746) 0.002 1.840 (1.082–3.131) 0.024 Mutated TP53 § 4.666 (2.480–8.779) < 0.001 5.743 (2.967–11.118) < 0.001 WHO-2022 < 0.001 < 0.001 MDS-h, and SF3B1 Reference - Reference - Low-risk MDS † 1.030 (0.649–1.634) 0.901 1.086 (0.683–1.727) 0.726 High-risk MDS ‡ 1.826 (1.082–3.080) 0.024 1.592 (0.933–2.718) 0.088 MDS-bi TP53 4.588 (2.283–9.220) < 0.001 5.490 (2.664–11.312) < 0.001 IPSS-M < 0.001 < 0.001 < 0.001 < 0.001 Very low/low Reference - Reference - Reference - Reference - Moderate low 1.517 (0.854–2.695) 0.155 1.611 (0.908–2.859) 0.103 1.518 (0.852–2.704) 0.157 1.598 (0.898–2.845) 0.111 Moderate high 2.081 (1.224–3.536) 0.007 1.952 (1.140–3.344) 0.015 2.023 (1.178–3.475) 0.011 1.872 (1.080–3.244) 0.025 High 2.729 (1.559–4.776) < 0.001 2.898 (1.647–5.099) < 0.001 2.755 (1.561–4.861) < 0.001 2.866 (1.616–5.085) < 0.001 Very high 5.119 (2.857–9.174) < 0.001 4.698 (2.593–8.514) < 0.001 5.536 (3.098–9.896) < 0.001 5.057 (2.804–9.121) < 0.001 HMA 0.959 (0.684–1.346) 0.810 0.833 (0.585–1.186) 0.311 1.070 (0.772–1.485) 0.684 0.943 (0.671–1.325) 0.735 HSCT 0.597 (0.345–1.032) 0.065 0.778 (0.449–1.349) 0.372 0.568 (0.330–0.976) 0.040 0.743 (0.432–1.275) 0.281 P values of < 0.05 are statistically significant. *As continuous variables analysis. † Low-risk MDS included MDS with del(5q), MDS- SF3B1 , and MDS, NOS with SLD or MLD. ‡ MDS/AML with MDS-related gene mutations, MDS-related cytogenetic abnormalities, or not otherwise specified § MDS or MDS/AML with mutated TP53 Abbreviations: CI, confidence interval; EB, excess blasts; HR, Hazard ratios; HMA, hypomethylating agents; HSCT, allogeneic hematopoietic stem cell transplantation; ICC, International Consensus Classification; IPSS-M, Molecular International Prognostic Scoring System; L/M, lymphocyte/monocyte ratio; LFS, leukemia-free survival; MDS, myelodysplastic syndromes/neoplasms; MDS/AML, myelodysplastic syndromes/acute myeloid leukemia; OS, overall survival. Using multivariable analyses, we assessed the prognostic significance of AMC and L/M ratios by combining various variables. Even after controlling for AMC, the L/M ratio remained a valid indicator of poor prognosis for both LFS and OS (Supplemental Table 4). However, without the simultaneous assessment of lymphocyte counts, AMC alone did not demonstrate significant value in independently predicting outcomes (Supplemental Table 5). Functional analysis of patients with high or low L/M ratio To clarify the potential biological mechanisms underlying the negative prognostic effect of a higher L/M ratio, we analyzed RNA sequencing data of BM samples from 66 and 44 patients with high and low L/M ratios, respectively. Differential expression analysis between patients with high vs. low L/M ratios was performed (Supplemental Fig. 3). The most significantly underexpressed functional pathways in patients with a high L/M ratio included IL-2–STAT5, IL6-JAK-STAT3, and interferon-gamma/alpha responses that regulate the inflammatory response (Fig. 4 ). Similarly, patients with a high L/M ratio showed notable downregulation of the p53 pathway and positive enrichment of MYC target genes (Fig. 4 ). Discussion This study demonstrated that an elevated L/M ratio of > 1.5 at diagnosis is an independent prognostic factor in patients with MDS, even after adjusting for established risk scoring systems such as IPSS-R and IPSS-M. Furthermore, we assessed the differences in clinical characteristics, genetic profiles, disease subtypes based on the WHO-2016 and WHO-2022 criteria, ICC, and the distribution of risk stratification using the IPSS-R and IPSS-M between patients with high and low L/M ratios. Individuals with a high L/M ratio exhibited higher frequencies of U2AF1 and STAG2 mutations but fewer SF3B1 mutations and were more likely to present with advanced WHO-2022 or ICC subtypes. In recent years, our knowledge of the pathophysiology underlying MDS has advanced considerably, with disease pathogenesis largely driven by molecular alterations [ 33 – 36 ]. In a more contemporary effort, Bernard et al. proposed the IPSS-M, a prognostic model that integrates clinical parameters, cytogenetic abnormalities, and somatic mutations in 31 genes [ 37 ]. The absolute neutrophil count was excluded due to a lack of independent prognostic factors. A six-risk category schema was established, which had a higher prognostic predictive accuracy than the IPSS-R. Previously, we confirmed the prognostic value of the IPSS-M and validated its performance in an Asian cohort [ 20 ]. Both the WHO-2022 classification [ 38 ] and the novel ICC [ 39 ] introduced novel disease entities that incorporated the mutation status of SF3B1 and TP53. Furthermore, the WHO-2022 classification evaluates BM cellularity and fibrosis to define MDS-h and MDS-f. However, the lack of comprehensive genetic sequencing technologies, including NGS or PCR for KMT2A -PTD detection, prevents IPSS-M or novel classification systems from being widely used. Because patients with MDS have severe neutropenia and/or neutrophil dysfunction, they are more likely to experience infectious complications. Pollyea et al. discovered that compared to monocytes from healthy control participants, monocytes from patients with MDS had comparatively normal innate immune functions [ 40 ]. Furthermore, monocytes from patients with MDS exhibit moderately elevated HLA-DR expression. These findings imply that monocytes help patients with MDS fight infections by compensating for other immune deficiencies [ 40 ]. Thus, several studies have documented the negative survival impact of monocytopenia in patients with MDS [ 12 , 13 , 31 ], and it has been linked to negative clinical characteristics, such as greater severity of anemia, neutropenia, and thrombocytopenia [ 12 ]. Lymphocyte count is increasingly being recognized as an important prognostic marker in various types of cancer [ 41 – 44 ]. Previous studies have suggested an association between poor prognosis and a lower ALC. Silzle et al. found that lymphopenia < 1.2 × 10 9 /l at diagnosis was associated with inferior outcomes in patients with MDS with low-risk IPSS-R [ 32 ]. In the very low- and low-risk IPSS-M groups, ALC < 1.5 × 10 9 /l was correlated with other severe cytopenias, fewer SF3B1 mutations, and shorter OS [ 14 ]. In WHO-2016 classification-defined MDS with ring sideroblasts, Mangaonkar et al. confirmed the negative prognostic predictive value of lymphopenia [ 45 ]. Additionally, we validated the prognostic impacts of AMC and ALC, which revealed that AMC did not have a survival effect as a dichotomous variable. In contrast, as a continuous variable, a higher AMC conferred poor outcomes in the univariable analysis. When using a cutoff value of 1.2 × 10 9 /L or 1.5 × 10 9 /L, no survival differences were found. The L/M ratio serves as a significant prognostic biomarker in various cancers, reflecting the interplay between the immune system and tumor biology. In the multivariable analysis, when considering the survival impact of lymphocytes and monocytes, significance was retained for the L/M ratio but not for AMC. This indicated that host immunity may be considered as a factor to incorporate into current risk stratification models, and lymphocytes and monocytes should be evaluated concomitantly. In summary, the current study adds to this knowledge by showing that the L/M ratio is a more accurate prognostic marker than AMC or ALC alone. In the past few years, various combinations of inflammatory parameters, including the ratios of neutrophils to lymphocytes, platelets to lymphocytes, and lymphocytes to monocytes, have been used to predict the prognosis of patients with hematological and oncological cancers [ 46 – 48 ]. One of the new inflammatory indices, the hemoglobin, albumin, lymphocyte, and platelet (HALP) score, can predict the outcomes of lymphoma, kidney, and lung cancers [ 49 – 51 ]. Gursoy et al. showed that a high HALP score was linked to adverse clinicopathological features in patients with MDS [ 43 ]. In our study, we revealed the prognostic implications of the L/M ratio in the context of two novel classification systems (WHO-2022 or ICC). Patients with an L/M ratio > 1.5 had more severe thrombocytopenia and a higher risk of mutational profile with more U2AF1, STAG2 , but fewer SF3B1 mutations. Furthermore, we found that HSCT could improve the survival of patients with a high L/M ratio. Thus, these routine laboratory tests may be widely applied and may help identify patients who may benefit from more aggressive therapy. To further clarify the mechanism underlying the adverse prognostic impact of a high L/M ratio in patients with MDS, we performed transcriptomic analysis and depicted differential gene expression. Gene set enrichment analysis revealed the downregulation of multiple immune and inflammatory signaling pathways, including interferon-alpha and gamma responses, IL6-JAK-STAT3, IL-2–STAT5, and the p53 pathway. These findings suggest a state of immunosuppression or immune evasion in patients with high L/M ratios, which may contribute to disease progression. The underexpression of the interferon signaling axis, particularly type I interferons, may impair antigen presentation and immune surveillance, thereby promoting leukemic clonal expansion [ 52 ]. STAT5 is essential for the development and functional maturation of multiple hematopoietic lineages, including B cells, T cells, natural killer cells, and erythroid progenitors. Loss-of-function STAT5 mutations are linked to impaired B cell adaptive immunity, immunosuppressive effects, and serious infections, which may contribute to poor outcomes [ 53 ]. Abnormal Stat3/5 signaling biosignature in patients with MDS has been reported to predict treatment response and outcomes [ 54 ]. The p53 pathway, a key tumor suppressor network, plays a critical role in genomic stability. Loss or dysfunction of p53 leads to enhanced self-renewal of leukemia-initiating cells [ 55 ] and evasion of cancer surveillance [ 56 ]. Downregulation of the p53 pathway in patients with high L/M ratios may further imply impaired apoptotic regulation and increased genomic instability. MYC expression increased in patients with a high L/M ratio. Higher MYC expression is associated with the blockade of myeloid cell differentiation [ 57 ], cooperation with other oncogenes [ 58 ], and cancer metabolism [ 59 ], which may result in accelerated disease progression and reduced survival [ 60 ]. Together, these results provide molecular insights into the adverse prognosis of patients with MDS with a high L/M ratio and highlight the potential utility of immunomodulatory or p53-targeted strategies in this population. The study's retrospective design, inability to assess TP53 copy-neutral loss of heterozygosity, the requirement of five residual genes by the IPSS-M, and treatment regimen variability are some of its limitations. Although the survival effect of the L/M ratio was internally validated using the bootstrapping method, external validation is warranted to confirm our results. The underlying pathophysiology of a high L/M ratio leading to poor prognosis requires further exploration. Future studies involving functional immune profiling and validation in larger cohorts are warranted. However, our study provides a simple and highly applicable method for further identification of patients with MDS who are at a higher risk of progression. These findings have significant clinical implications in resource-limited settings. To find a cure, patients with high L/M ratios may be eligible for more intensive therapies. In conclusion, this study presents compelling evidence for the prognostic value of the L/M ratio in MDS, advocating its integration into clinical practice. Transcriptomic profiling suggests that altered immune signaling and deregulated oncogenic pathways may have clinical implications. The L/M ratio is a simple and easy-to-use laboratory test that helps differentiate patients with different survival rates, which may potentially improve patient outcomes. Further validation in larger prospective cohorts is essential to confirm their roles in guiding treatment decisions. Declarations Disclosure of conflicts of interests The authors declare no competing financial interests. Acknowledgments We acknowledge the services provided by the Department of Laboratory Medicine and Medical Research, National Taiwan University Hospital, and Tai-Chen Cell Therapy Center. Moreover, we acknowledge the services provided by the DNA Sequencing Core of the First Core Laboratory of the National Taiwan University College of Medicine, Taiwan. Funding information This work was partially supported by grants from the Ministry of Science and Technology (Taiwan) (MOST 104–2314-B-002–128-MY4, 106-2314-B-002-226-MY3, 108-2628-B-002-015, 109-2314-B-002-213, and 111-2314-B-002-279), and the Ministry of Health and Welfare (Taiwan) (MOHW 107-TDU-B-211-114009 and 111-TDU-B-221-114001). This work was supported by the National Key Area International Cooperation Alliance: University Academic Alliance in, Kyushu-Okin Taiwan awa Open University, Medicine and Life Sciences Integrative Program, which promotes international collaboration in advanced research. Author contributions WHL: Formal analysis, Investigation, Data Curation, Writing - Original Draft. XCHT, YYK, CLH, CYY, MCL, YLP, CYS, and MHT: Investigation. CCL, FMT, MYL, BSK, WCC, MY, and HFT: Resources. SCY and CTY: Investigation. HAH: Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition. All the authors have reviewed and approved the final manuscript. Data sharing statement The datasets generated and analyzed in this study are available from the corresponding author upon reasonable request. References Tefferi A, Vardiman JW. Myelodysplastic syndromes. N Engl J Med. 2009;361:1872-85. Greenberg P, Cox C, LeBeau MM, Fenaux P, Morel P, Sanz G, et al. 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17:41:05","extension":"html","order_by":53,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221303,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7524368/v1/33968ae8fea642b446877207.html"},{"id":92617372,"identity":"2d5389b7-3a0e-4b0f-983f-a21bfc8d8cf3","added_by":"auto","created_at":"2025-10-01 17:49:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":486320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequencies of the commonly occurred mutations (a) and frequencies of mutations categorized by the functional groups (b) in patients with myelodysplastic neoplasms/syndromes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7524368/v1/ea78814045bee393b5f3e009.png"},{"id":92616748,"identity":"6fe9c382-5480-4753-8ec5-6ca0b00260a8","added_by":"auto","created_at":"2025-10-01 17:41:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":258985,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves for leukemia-free survival and overall survival in patients with myelodysplastic neoplasms/syndromes based on lymphocyte/monocyte (L/M) ratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Leukemia-free survival, stratified by L/M ratio\u003c/p\u003e\n\u003cp\u003e(B) Overall survival, stratified by L/M ratio\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7524368/v1/524aaff1962b914af0a462b8.png"},{"id":92616736,"identity":"a790255d-a6f8-4711-aedc-21e05fac2671","added_by":"auto","created_at":"2025-10-01 17:41:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":504770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves censoring at transplantation for leukemia-free survival and overall survival in patients with myelodysplastic neoplasms/syndromes based on lymphocyte/monocyte (L/M) ratio, stratified by revised International Prognostic Scoring System (IPSS-R)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Leukemia-free survival, stratified by L/M ratio in patients with very low-, low, or intermediate-risk IPSS-R\u003c/p\u003e\n\u003cp\u003e(B) Overall survival, stratified by L/M ratio in patients with very low-, low, or intermediate-risk IPSS-R\u003c/p\u003e\n\u003cp\u003e(C) Leukemia-free survival, stratified by L/M ratio in patients with high, or very high-risk IPSS-R\u003c/p\u003e\n\u003cp\u003e(D) Overall survival, stratified by L/M ratio in patients with high, or very high-risk IPSS-R\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7524368/v1/2e9a730684148246aad2718b.png"},{"id":92616745,"identity":"3bdabf6d-d679-4df8-8330-dbe31e9f51ab","added_by":"auto","created_at":"2025-10-01 17:41:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":441132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis highlighted the underexpressed functional pathway in MDS patients with high lymphocyte to monocyte ratio\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7524368/v1/842304341459868ecdccdfb0.png"},{"id":92618119,"identity":"1c93ffc6-ca17-4430-b5d8-db46c6840f57","added_by":"auto","created_at":"2025-10-01 18:05:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3231314,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7524368/v1/d3280e3b-ead8-4e5f-8d3e-771453b3c496.pdf"},{"id":92616784,"identity":"2368f0e8-817b-453e-b387-9d412b6314c7","added_by":"auto","created_at":"2025-10-01 17:41:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":500143,"visible":true,"origin":"","legend":"","description":"","filename":"LMSupplement20250519.docx","url":"https://assets-eu.researchsquare.com/files/rs-7524368/v1/a0f257506d705b18dc65bc16.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Impact of Lymphocyte to Monocyte Ratio in Patients with Myelodysplastic Neoplasms/Syndromes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMyelodysplastic syndromes/neoplasms (MDS), a broad category of clonal myeloid disorders, are characterized by dysregulated hematopoiesis, which leads to cytopenia and dysplastic hematopoietic cells. Clinical and genetic heterogeneity, recurrent chromosomal abnormalities, and variable prognostic outcomes are features of MDS [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To risk-stratify patients with MDS and direct treatment, several prognostic models have been developed, such as the International Prognostic Scoring System (IPSS) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], revised IPSS (IPSS-R) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], molecular IPSS (IPSS-M), World Health Organization (WHO) Classification-based Prognostic Scoring System [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and MD Anderson Prognostic Scoring System [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These models primarily incorporate parameters such as cytopenia severity, cytogenetic abnormalities, bone marrow (BM) blast percentage, gene mutations, and transfusion dependency.\u003c/p\u003e\u003cp\u003eRecent studies have also highlighted the prognostic relevance of peripheral lymphocyte and monocyte counts in solid tumors and hematologic malignancies [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In MDS, absolute lymphocyte count (ALC) and absolute monocyte count (AMC) at diagnosis are individually associated with patient outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Lymphopenia adversely affects survival in IPSS-M-defined low-risk MDS patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], whereas monocytopenia, observed in 29.5% of MDS, correlates with higher blast counts and worse outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings suggest that peripheral immune cell profiles may reflect the underlying disease biology and immune dysregulation. Given the interplay between these two values, the lymphocyte-to-monocyte (L/M) ratio presents as a more integrative and robust biomarker; however, studies regarding the prognostic implications of the L/M ratio in patients with MDS are scarce.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAt the National Taiwan University Hospital (NTUH), data were collected from 554 patients with primary MDS who were diagnosed and treated at NTUH. We retrospectively reviewed a cohort of 554 patients diagnosed with primary MDS based on the WHO-2016 criteria, with subsequent reclassification based on the WHO-2022 and International Consensus Classification (ICC). The survival impact of the L/M ratio was evaluated in the context of the novel classification systems IPSS-R and IPSS-M. To avoid confounding factors, patients with a history of chemotherapy, radiation, or hematologic malignancies were excluded, given the distinct mutational profiles and clinical outcomes of primary and secondary MDS [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The TruSight Myeloid Panel and HiSeq platform (Illumina, San Diego, CA, USA) were used to sequence the cryopreserved BM samples and identify mutations in 54 myeloid-related genes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] (Supplemental Table\u0026nbsp;1). \u003cem\u003eTP5\u003c/em\u003e3 copy-neutral loss of heterozygosity and five residual genes (\u003cem\u003eETNK1, GNB1, NF1, PPM1D\u003c/em\u003e, and \u003cem\u003ePRPF8\u003c/em\u003e) identified using the IPSS-M model were not evaluated in this study. By following the manufacturer's instructions, the library was prepared and sequenced to achieve a median read depth of 10,550 x. Variant analysis used databases for somatic mutation annotation and interpretation, including COSMIC v86, dbSNP v151, ClinVar, PolyPhen-2, and SIFT. A variant analysis diagnostic algorithm has been previously described [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Polymerase chain reaction (PCR) and fluorescence capillary electrophoresis are required for \u003cem\u003eFLT\u003c/em\u003e3-ITD analysis due to the limitations of next-generation sequencing (NGS), whereas Sanger sequencing and PCR are required for \u003cem\u003eKMT2A\u003c/em\u003e-PTD analysis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Cellularity and fibrosis of the BM were evaluated and verified by pathologists using reticulin staining. Cytogenetic analyses were performed according to the International System for Human Cytogenetic Nomenclature in cytogenetic analyses [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFollowing the manufacturer\u0026rsquo;s instructions, RNA was extracted from diagnostic BM samples (without CD34\u0026thinsp;+\u0026thinsp;cell isolation), and sequencing libraries were created using the TruSeq Stranded mRNA Library Prep Kit (Illumina). The libraries were subsequently sequenced using a 150-bp paired-end read mode on an Illumina NovaSeq 6000. STAR (v2.7) was used to align the clean reads to the human reference genome GRCh38 after adapter sequencing, and low-quality bases were eliminated from the raw sequencing data using Cutadapt (v3.0) in two-pass mode. Each gene's raw count was determined using GENCODE v28 annotation and was then converted to transcripts per million for additional analysis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe NTUH Research Ethics Committee approved this study (approval number: 20220705RINB). Each participant provided written informed consent in compliance with the Declaration of Helsinki.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFisher's exact or χ\u0026sup2; test for categorical variables and the Mann\u0026ndash;Whitney U test for continuous variables were used in the statistical analyses. The time between diagnosis and leukemic transformation, death, or last follow-up was referred to as leukemia-free survival (LFS). The relationship between the date of diagnosis and the last follow-up or death from any cause was known as overall survival (OS). Survival curves were produced using Kaplan\u0026ndash;Meier analysis, and the log-rank test was used to determine significance. For both univariable and multivariable analyses, Cox proportional hazards models were used. A time-dependent covariate was thought to be allogeneic hematopoietic stem cell transplantation (HSCT) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For variant allele frequency exploration, maximally selected rank statistics were used [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In our analysis of the relationship between the variant allele frequency of mutated genes and survival, this approach was a suitable standardized two-sample linear rank statistic to determine the maximum standardized statistics of all potential cutoffs, which offered the best separation of the results into two groups [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. By selecting replacement samples of the same size from the original dataset, bootstrapping replicated the process of creating samples from an underlying population. The results were tested on individuals excluded from the bootstrap or original samples [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. All \u003cem\u003eP\u003c/em\u003e values were two-sided, and at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, they were deemed statistically significant. IBM SPSS Statistics v23 for Windows was used for all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eClinical characteristics and genetic profiles\u003c/h2\u003e\u003cp\u003eThe demographic features are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For the total cohort, the median age was 67.3 years, with a male predominance (63.7%). According to the WHO-2016 classification, half (49.9%) of the patients had MDS with excess blasts (EB), including EB1 (19.9%) and EB2 (30.0%). When classifying patients with ICC, there were 76 (13.7%) and 11 (2.0%) patients with MDS/AML who had myelodysplasia-related gene mutations or myelodysplasia-related cytogenetic abnormalities, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 21 (3.8%) and 36 (6.5%) patients met the diagnostic criteria for MDS with mutated \u003cem\u003eTP53\u003c/em\u003e and MDS/AML with mutated \u003cem\u003eTP5\u003c/em\u003e3, respectively, based on different blast percentages (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the WHO-2022 classification, 83 (15.0%) individuals had hypocellular marrow, 12 (2.2%) had significant BM fibrosis, and the increased blasts were grouped as hypoplastic MDS (MDS-h) or MDS with fibrosis (MDS-f) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of clinical characteristics between patients with high (\u0026gt;\u0026thinsp;1.5) or low (\u0026thinsp;≦\u0026thinsp;1.5) lymphocyte/monocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical characters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;554)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eL/M\u0026thinsp;\u0026le;\u0026thinsp;1.5\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;206)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL/M\u0026thinsp;\u0026gt;\u0026thinsp;1.5\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;348)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e201 (36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (39.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e120 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e353 (63.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (60.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e228 (65.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.3 (18.4\u0026ndash;94.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.6 (19.3\u0026ndash;94.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.6 (18.4\u0026ndash;94.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaboratory data*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.39 (0.6-32.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.44 (0.6-26.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.36 (0.6-32.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.55 (0-23.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.66 (0-15.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49 (0.01\u0026ndash;23.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17 (0.07\u0026ndash;10.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03 (0.07\u0026ndash;2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28 (0.08\u0026ndash;10.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyote, \u0026times;10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22 (0.01\u0026ndash;5.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.36 (0.03\u0026ndash;3.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15 (0.01\u0026ndash;5.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb, g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.1 (2.6\u0026ndash;17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.2 (3.4\u0026ndash;14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0 (2.6\u0026ndash;17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet, \u0026times;10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (1-721)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (7-655)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (1-721)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBM blast (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.4 (0-19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.0 (0-19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.9 (0-19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePB blast (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0-18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0\u0026ndash;17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0-18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPSS-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.093\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e152 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.075\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPSS-M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.053\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163 (29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016 WHO classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-5q\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-SLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-MLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-RS-SLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.00\u003c/b\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-RS-MLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-EB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-EB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-U\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e413 (74.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e159 (77.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e254 (73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edel(5q)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emutated \u003cem\u003eSF3B1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNOS, with SLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.073\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNOS, with MLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131 (23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emutated \u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS/AML\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-related genes mutations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-related cytogenetics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emutated \u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.083\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022 WHO classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-5q\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-\u003cem\u003eSF3B1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-LB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (25.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (20.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-IB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-IB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-bi\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e147 (26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107 (30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntensive chemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical trial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupportive care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e243 (43.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105 (51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138 (39.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther treatment\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eP\u003c/em\u003e values of \u0026lt;\u0026thinsp;0.05 are statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as n (%)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Median (range).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eOther treatment: include low-dose cytarabine, rabbit-derived anti-thymocyte globulin, cyclosporine, danazol, eltrombopag, erythropoietin-stimulating agents, thalidomide, steroid, venetoclax-based therapy and oral chemotherapy.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAbbreviations: ANC, absolute neutrophil count; ALC,\u0026nbsp;absolute lymphocyte count; BM, bone marrow;\u0026nbsp;Hb, hemoglobin; HMA, hypomethylating agent; HSCT, allogeneic hematopoietic stem cell transplantation; ICC, International Consensus Classification; IPSS-R, revised international prognosis scoring system; IPSS-M, molecular international prognosis scoring system; L/M, lymphocyte/monocyte ratio; MDS-RS, MDS with ring sideroblasts; MDS-EB, MDS with excess blasts; MDS-SLD, MDS with single lineage dysplasia; MDS-MLD, MDS with multilineage dysplasia; MDS-RS-SLD, MDS with\u0026nbsp;ring sideroblasts and\u0026nbsp;single lineage dysplasia; MDS-RS-MLD, MDS with ring sideroblasts and multilineage dysplasia; MDS-U, MDS, unclassifiable; MDS-5q, MDS with low blasts and isolated 5q deletion; MDS-\u003cem\u003eSF3B1\u003c/em\u003e, MDS with low blasts and \u003cem\u003eSF3B1\u0026nbsp;\u003c/em\u003emutation; MDS-LB and RS, MDS with low blasts and ring sideroblasts; MDS-LB, MDS with low blasts; MDS-h, hypoplastic MDS; MDS-IB1, MDS with increased blasts-1; MDS-IB2, MDS with increased blasts-2; MDS-f, MDS with fibrosis; MDS-bi\u003cem\u003eTP53\u003c/em\u003e, MDS with biallelic \u003cem\u003eTP53\u003c/em\u003e inactivation; NOS, not otherwise specified; PB, peripheral blood; WBC, while blood cell count\u003c/p\u003e\u003cp\u003eA total of 68.9% patients had IPSS-R intermediate-(25.5%), high-(20.9%), or very high-risk disease (22.5%), and a total of 61.0% patients had IPSS-M moderately high-(15.0%), high-(16.6%), or very high-risk (29.4%) disease (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding treatments, 41.7% of patients with EB received hypomethylating agents (HMA, 41.7%) or chemotherapy (5.4%), and 19.4% of patients in the intermediate-, high-, or very-high-risk IPSS-R group underwent allogeneic HSCT.\u003c/p\u003e\u003cp\u003eOverall, 78.2% had at least one gene mutation or cytogenetic abnormality. As shown in Supplemental Table\u0026nbsp;2, the most common mutation in this cohort was the \u003cem\u003eASXL1\u003c/em\u003e mutation (21.3%), followed by \u003cem\u003eTET2\u003c/em\u003e (15.5%), \u003cem\u003eSF3B1\u003c/em\u003e (13.9%), \u003cem\u003eRUNX1\u003c/em\u003e (12.3%), \u003cem\u003eSTAG2\u003c/em\u003e (11.9%), and \u003cem\u003eTP53\u003c/em\u003e (10.6%). When stratified based on the biological function of the affected genes, mutations in genes involved in epigenetic modifications (45.5%), including DNA methylation-related genes (26.7%) and chromatin-modifying genes (28.7%), were the most common, followed by mutations in the spliceosome complex genes (34.4%).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical and genetics differences between patients with high or low L/M ratio\u003c/h3\u003e\n\u003cp\u003eAs mentioned above, we used maximally selected rank statistics to determine the optimal L/M ratio cutoff that correlated with the outcomes. Differences in clinical characteristics and genetic profiles between patients with high (\u0026gt;\u0026thinsp;1.5) or low (\u0026le;\u0026thinsp;1.5) L/M ratio were explored. Specifically, patients with L/M\u0026thinsp;\u0026gt;\u0026thinsp;1.5 were significantly younger and had lower platelet counts at diagnosis. They had a higher prevalence of EB2 (WHO-2016), EB (ICC), and IB2 (ICC) subtypes, and a lower prevalence of MDS-SLD, MDS-RS-SLD, and \u003cem\u003eSF3B1\u003c/em\u003e-mutated subtypes (as defined by both ICC and WHO-2022, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Taken together, patients with L/M\u0026thinsp;\u0026gt;\u0026thinsp;1.5 had a lower proportion of low-risk IPSS-R or IPSS-M (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplemental Fig.\u0026nbsp;1). Furthermore, those with L/M\u0026thinsp;\u0026gt;\u0026thinsp;1.5 had more \u003cem\u003eU2AF1\u003c/em\u003e (9.5% \u003cem\u003evs.\u003c/em\u003e 4.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) and \u003cem\u003eSTAG2\u003c/em\u003e (14.7% \u003cem\u003evs.\u003c/em\u003e 7.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) mutations, while they had less \u003cem\u003eSF3B1\u003c/em\u003e (10.6% \u003cem\u003evs.\u003c/em\u003e 19.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) mutations compared with those with L/M\u0026thinsp;\u0026le;\u0026thinsp;1.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplemental Table\u0026nbsp;2). When categorized by the genetic functional group, patients with a high L/M ratio had more cohesion complex gene mutations (15.2% \u003cem\u003evs.\u003c/em\u003e 7.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Supplemental Table\u0026nbsp;2 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n\u003ch3\u003eSurvival impact of L/M ratio\u003c/h3\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier survival analysis showed that patients with L/M ratio\u0026thinsp;\u0026gt;\u0026thinsp;1.5 had LFS and OS of 31.5 and 34.9 months, respectively, which were significantly shorter than the LFS and OS of those with L/M ratio\u0026thinsp;\u0026le;\u0026thinsp;1.5 (78.7 months for LFS and OS, both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When censoring at the transplantation, individuals with high L/M ratio had a trend of inferior outcomes in both lower (very low, low, or intermediate-risk IPSS-R) or higher (high or very high-risk IPSS-R) risk group (in lower risk group: median LFS: 83.6 \u003cem\u003evs.\u003c/em\u003e 218.6 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.075; median OS: 102.4 \u003cem\u003evs.\u003c/em\u003e 218.6 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.080; in higher risk group: LFS: 10.5 \u003cem\u003evs.\u003c/em\u003e 15.1 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.086; median OS: 15.2 \u003cem\u003evs.\u003c/em\u003e 17.7 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.131) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). According to our previous studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], we defined MDS with del5(q), MDS with low blasts (MDS-LB), and MDS-LB and RS as low-risk MDS, whereas MDS with increased blasts and MDS-f were defined as high-risk MDS in the WHO-2022 classification. For the ICC, MDS with del(5q), MDS with mutated \u003cem\u003eSF3B1\u003c/em\u003e, and MDS, NOS with SLD or MLD were defined as low-risk MDS. The results of Cox regression analyses were internally validated using the bootstrapping method. In univariable analysis, in addition to older age, high ferritin levels, MDS classification based on the ICC or WHO-2022 classification, and risk stratification by the IPSS-R or IPSS-M, L/M\u0026thinsp;\u0026gt;\u0026thinsp;1.5 was associated with shorter LFS (hazard ratios [HR]: 1.422, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and OS (HR: 1.401, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) (Supplemental Table\u0026nbsp;3). Furthermore, we validated the thresholds relevant to the prognostic impact of ALC (1.5, or 1.2 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L) and AMC (0.2, or 0.3 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L), which had been reported previously [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] by using our cohort. No differences in survival were observed between the groups. As a continuous variable in the univariable analysis, a higher AMC was associated with shorter LFS and OS (both HR: 1.003, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplemental Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eVariables with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariable Cox regression analysis and allo-HSCT were used as covariates. Two models incorporating advanced ICC or WHO-2022 subtypes were used. For LFS, older age, high ferritin levels, advanced ICC or WHO-2022 subtypes, and higher IPSS-M scores were associated with worse outcomes (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). L/M ratio\u0026thinsp;\u0026gt;\u0026thinsp;1.5 had a trend toward shorter LFS (HR: 1.303, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.094; HR: 1.358, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.053), and HSCT may confer protective effects (HR: 0.597, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065; HR: 0.568, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For OS, older age, L/M\u0026thinsp;\u0026gt;\u0026thinsp;1.5 (HR: 1.484, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014; HR: 1.548, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), advanced ICC or WHO-2022 subtypes, and higher IPSS-M were independent poor prognostic factors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, HSCT could improve the outcomes of patients with a high L/M ratio. For patients who did not undergo HSCT, individuals with high L/M ratio had median LFS and OS of 28.8 and 31.3 months, respectively, which were significantly worse than those of patients with a low L/M ratio (median LFS and OS: 78.7 months, both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Supplemental Figs.\u0026nbsp;2A and 2B). At the same time, patients with high L/M ratio receiving HSCT had similar outcomes compared to those with low L/M ratio (median LFS 53.8 and 46.7 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.755; median OS 73.3 and 73.7 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.759, Supplemental Figs.\u0026nbsp;2C and 2D). However, HMA treatment could not abrogate the adverse effects of a high L/M ratio (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.056 for LFS and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023 for OS).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable analysis Cox regression analysis of the impact of different variables on the leukemia-free survival and overall survival of patients with myelodysplastic syndromes/neoplasms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLFS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eLFS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.027 (1.016\u0026ndash;1.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.032 (1.020\u0026ndash;1.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.027 (1.016\u0026ndash;1.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.032 (1.021\u0026ndash;1.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.859 (0.627\u0026ndash;1.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.894 (0.649\u0026ndash;1.232)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.855 (0.624\u0026ndash;1.170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.893 (0.648\u0026ndash;1.231)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFerritin\u003csup\u003e*\u003c/sup\u003e(X 10\u003csup\u003e2\u003c/sup\u003e ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.001 (1.000-1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000 (1.000-1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.078\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.001 (1.000-1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.000 (1.000-1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.065\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL/M\u0026thinsp;\u0026gt;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.303 (0.956\u0026ndash;1.775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.094\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.484 (1.084\u0026ndash;2.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.358 (0.996\u0026ndash;1.851)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.053\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.548 (1.130\u0026ndash;2.119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-risk MDS\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS with EB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.685 (1.074\u0026ndash;2.644)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.441 (0.905\u0026ndash;2.292)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS/AML\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.244 (1.344\u0026ndash;3.746)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.840 (1.082\u0026ndash;3.131)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMutated \u003cem\u003eTP53\u003c/em\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.666 (2.480\u0026ndash;8.779)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.743 (2.967\u0026ndash;11.118)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHO-2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-h, and \u003cem\u003eSF3B1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-risk MDS\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.030 (0.649\u0026ndash;1.634)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.086 (0.683\u0026ndash;1.727)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-risk MDS\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.826 (1.082\u0026ndash;3.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.592 (0.933\u0026ndash;2.718)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.088\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-bi\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.588 (2.283\u0026ndash;9.220)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.490 (2.664\u0026ndash;11.312)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPSS-M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery low/low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.517 (0.854\u0026ndash;2.695)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.611 (0.908\u0026ndash;2.859)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.518 (0.852\u0026ndash;2.704)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.598 (0.898\u0026ndash;2.845)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.081 (1.224\u0026ndash;3.536)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.952 (1.140\u0026ndash;3.344)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.023 (1.178\u0026ndash;3.475)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.872 (1.080\u0026ndash;3.244)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.729 (1.559\u0026ndash;4.776)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.898 (1.647\u0026ndash;5.099)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.755 (1.561\u0026ndash;4.861)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.866 (1.616\u0026ndash;5.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.119 (2.857\u0026ndash;9.174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.698 (2.593\u0026ndash;8.514)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.536 (3.098\u0026ndash;9.896)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.057 (2.804\u0026ndash;9.121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.959 (0.684\u0026ndash;1.346)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.833 (0.585\u0026ndash;1.186)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.070 (0.772\u0026ndash;1.485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.943 (0.671\u0026ndash;1.325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.597 (0.345\u0026ndash;1.032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.065\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.778 (0.449\u0026ndash;1.349)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.568 (0.330\u0026ndash;0.976)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.743 (0.432\u0026ndash;1.275)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eP\u003c/em\u003e values of \u0026lt;\u0026thinsp;0.05 are statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e*As continuous variables analysis.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eLow-risk MDS included MDS with del(5q), MDS-\u003cem\u003eSF3B1\u003c/em\u003e, and MDS, NOS with SLD or MLD.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003eMDS/AML with MDS-related gene mutations, MDS-related cytogenetic abnormalities, or not otherwise specified\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003eMDS or MDS/AML with mutated \u003cem\u003eTP53\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations: CI, confidence interval; EB, excess blasts; HR, Hazard ratios; HMA, hypomethylating agents; HSCT, allogeneic hematopoietic stem cell transplantation; ICC, International Consensus Classification; IPSS-M, Molecular International Prognostic Scoring System; L/M, lymphocyte/monocyte ratio; LFS, leukemia-free survival; MDS, myelodysplastic syndromes/neoplasms; MDS/AML, myelodysplastic syndromes/acute myeloid leukemia; OS, overall survival.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUsing multivariable analyses, we assessed the prognostic significance of AMC and L/M ratios by combining various variables. Even after controlling for AMC, the L/M ratio remained a valid indicator of poor prognosis for both LFS and OS (Supplemental Table\u0026nbsp;4). However, without the simultaneous assessment of lymphocyte counts, AMC alone did not demonstrate significant value in independently predicting outcomes (Supplemental Table\u0026nbsp;5).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFunctional analysis of patients with high or low L/M ratio\u003c/h2\u003e\u003cp\u003eTo clarify the potential biological mechanisms underlying the negative prognostic effect of a higher L/M ratio, we analyzed RNA sequencing data of BM samples from 66 and 44 patients with high and low L/M ratios, respectively. Differential expression analysis between patients with high \u003cem\u003evs.\u003c/em\u003e low L/M ratios was performed (Supplemental Fig.\u0026nbsp;3). The most significantly underexpressed functional pathways in patients with a high L/M ratio included IL-2\u0026ndash;STAT5, IL6-JAK-STAT3, and interferon-gamma/alpha responses that regulate the inflammatory response (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, patients with a high L/M ratio showed notable downregulation of the p53 pathway and positive enrichment of MYC target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated that an elevated L/M ratio of \u0026gt;\u0026thinsp;1.5 at diagnosis is an independent prognostic factor in patients with MDS, even after adjusting for established risk scoring systems such as IPSS-R and IPSS-M. Furthermore, we assessed the differences in clinical characteristics, genetic profiles, disease subtypes based on the WHO-2016 and WHO-2022 criteria, ICC, and the distribution of risk stratification using the IPSS-R and IPSS-M between patients with high and low L/M ratios. Individuals with a high L/M ratio exhibited higher frequencies of \u003cem\u003eU2AF1\u003c/em\u003e and \u003cem\u003eSTAG2\u003c/em\u003e mutations but fewer \u003cem\u003eSF3B1\u003c/em\u003e mutations and were more likely to present with advanced WHO-2022 or ICC subtypes.\u003c/p\u003e\u003cp\u003eIn recent years, our knowledge of the pathophysiology underlying MDS has advanced considerably, with disease pathogenesis largely driven by molecular alterations [\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In a more contemporary effort, Bernard et al. proposed the IPSS-M, a prognostic model that integrates clinical parameters, cytogenetic abnormalities, and somatic mutations in 31 genes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The absolute neutrophil count was excluded due to a lack of independent prognostic factors. A six-risk category schema was established, which had a higher prognostic predictive accuracy than the IPSS-R. Previously, we confirmed the prognostic value of the IPSS-M and validated its performance in an Asian cohort [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBoth the WHO-2022 classification [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and the novel ICC [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] introduced novel disease entities that incorporated the mutation status of \u003cem\u003eSF3B1\u003c/em\u003e and \u003cem\u003eTP53.\u003c/em\u003e Furthermore, the WHO-2022 classification evaluates BM cellularity and fibrosis to define MDS-h and MDS-f. However, the lack of comprehensive genetic sequencing technologies, including NGS or PCR for \u003cem\u003eKMT2A\u003c/em\u003e-PTD detection, prevents IPSS-M or novel classification systems from being widely used. Because patients with MDS have severe neutropenia and/or neutrophil dysfunction, they are more likely to experience infectious complications. Pollyea et al. discovered that compared to monocytes from healthy control participants, monocytes from patients with MDS had comparatively normal innate immune functions [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, monocytes from patients with MDS exhibit moderately elevated HLA-DR expression. These findings imply that monocytes help patients with MDS fight infections by compensating for other immune deficiencies [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Thus, several studies have documented the negative survival impact of monocytopenia in patients with MDS [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and it has been linked to negative clinical characteristics, such as greater severity of anemia, neutropenia, and thrombocytopenia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLymphocyte count is increasingly being recognized as an important prognostic marker in various types of cancer [\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Previous studies have suggested an association between poor prognosis and a lower ALC. Silzle et al. found that lymphopenia\u0026thinsp;\u0026lt;\u0026thinsp;1.2 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/l at diagnosis was associated with inferior outcomes in patients with MDS with low-risk IPSS-R [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In the very low- and low-risk IPSS-M groups, ALC\u0026thinsp;\u0026lt;\u0026thinsp;1.5 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/l was correlated with other severe cytopenias, fewer \u003cem\u003eSF3B1\u003c/em\u003e mutations, and shorter OS [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In WHO-2016 classification-defined MDS with ring sideroblasts, Mangaonkar et al. confirmed the negative prognostic predictive value of lymphopenia [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, we validated the prognostic impacts of AMC and ALC, which revealed that AMC did not have a survival effect as a dichotomous variable. In contrast, as a continuous variable, a higher AMC conferred poor outcomes in the univariable analysis. When using a cutoff value of 1.2 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L or 1.5 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L, no survival differences were found. The L/M ratio serves as a significant prognostic biomarker in various cancers, reflecting the interplay between the immune system and tumor biology. In the multivariable analysis, when considering the survival impact of lymphocytes and monocytes, significance was retained for the L/M ratio but not for AMC. This indicated that host immunity may be considered as a factor to incorporate into current risk stratification models, and lymphocytes and monocytes should be evaluated concomitantly. In summary, the current study adds to this knowledge by showing that the L/M ratio is a more accurate prognostic marker than AMC or ALC alone.\u003c/p\u003e\u003cp\u003eIn the past few years, various combinations of inflammatory parameters, including the ratios of neutrophils to lymphocytes, platelets to lymphocytes, and lymphocytes to monocytes, have been used to predict the prognosis of patients with hematological and oncological cancers [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. One of the new inflammatory indices, the hemoglobin, albumin, lymphocyte, and platelet (HALP) score, can predict the outcomes of lymphoma, kidney, and lung cancers [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Gursoy et al. showed that a high HALP score was linked to adverse clinicopathological features in patients with MDS [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study, we revealed the prognostic implications of the L/M ratio in the context of two novel classification systems (WHO-2022 or ICC). Patients with an L/M ratio\u0026thinsp;\u0026gt;\u0026thinsp;1.5 had more severe thrombocytopenia and a higher risk of mutational profile with more \u003cem\u003eU2AF1, STAG2\u003c/em\u003e, but fewer \u003cem\u003eSF3B1\u003c/em\u003e mutations. Furthermore, we found that HSCT could improve the survival of patients with a high L/M ratio. Thus, these routine laboratory tests may be widely applied and may help identify patients who may benefit from more aggressive therapy.\u003c/p\u003e\u003cp\u003eTo further clarify the mechanism underlying the adverse prognostic impact of a high L/M ratio in patients with MDS, we performed transcriptomic analysis and depicted differential gene expression. Gene set enrichment analysis revealed the downregulation of multiple immune and inflammatory signaling pathways, including interferon-alpha and gamma responses, IL6-JAK-STAT3, IL-2\u0026ndash;STAT5, and the p53 pathway. These findings suggest a state of immunosuppression or immune evasion in patients with high L/M ratios, which may contribute to disease progression. The underexpression of the interferon signaling axis, particularly type I interferons, may impair antigen presentation and immune surveillance, thereby promoting leukemic clonal expansion [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. STAT5 is essential for the development and functional maturation of multiple hematopoietic lineages, including B cells, T cells, natural killer cells, and erythroid progenitors. Loss-of-function STAT5 mutations are linked to impaired B cell adaptive immunity, immunosuppressive effects, and serious infections, which may contribute to poor outcomes [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Abnormal Stat3/5 signaling biosignature in patients with MDS has been reported to predict treatment response and outcomes [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The p53 pathway, a key tumor suppressor network, plays a critical role in genomic stability. Loss or dysfunction of p53 leads to enhanced self-renewal of leukemia-initiating cells [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and evasion of cancer surveillance [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Downregulation of the p53 pathway in patients with high L/M ratios may further imply impaired apoptotic regulation and increased genomic instability. MYC expression increased in patients with a high L/M ratio. Higher MYC expression is associated with the blockade of myeloid cell differentiation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], cooperation with other oncogenes [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and cancer metabolism [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], which may result in accelerated disease progression and reduced survival [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Together, these results provide molecular insights into the adverse prognosis of patients with MDS with a high L/M ratio and highlight the potential utility of immunomodulatory or p53-targeted strategies in this population. The study's retrospective design, inability to assess \u003cem\u003eTP53\u003c/em\u003e copy-neutral loss of heterozygosity, the requirement of five residual genes by the IPSS-M, and treatment regimen variability are some of its limitations. Although the survival effect of the L/M ratio was internally validated using the bootstrapping method, external validation is warranted to confirm our results. The underlying pathophysiology of a high L/M ratio leading to poor prognosis requires further exploration. Future studies involving functional immune profiling and validation in larger cohorts are warranted. However, our study provides a simple and highly applicable method for further identification of patients with MDS who are at a higher risk of progression. These findings have significant clinical implications in resource-limited settings. To find a cure, patients with high L/M ratios may be eligible for more intensive therapies.\u003c/p\u003e\u003cp\u003eIn conclusion, this study presents compelling evidence for the prognostic value of the L/M ratio in MDS, advocating its integration into clinical practice. Transcriptomic profiling suggests that altered immune signaling and deregulated oncogenic pathways may have clinical implications. The L/M ratio is a simple and easy-to-use laboratory test that helps differentiate patients with different survival rates, which may potentially improve patient outcomes. Further validation in larger prospective cohorts is essential to confirm their roles in guiding treatment decisions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure of conflicts of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the services provided by the Department of Laboratory Medicine and Medical Research, National Taiwan University Hospital, and Tai-Chen Cell Therapy Center. Moreover, we acknowledge the services provided by the DNA Sequencing Core of the First Core Laboratory of the National Taiwan University College of Medicine, Taiwan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding information\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by grants from the Ministry of Science and Technology (Taiwan) (MOST\u0026nbsp;104\u0026ndash;2314-B-002\u0026ndash;128-MY4,\u0026nbsp;106-2314-B-002-226-MY3,\u0026nbsp;108-2628-B-002-015,\u0026nbsp;109-2314-B-002-213, and 111-2314-B-002-279), and the\u0026nbsp;Ministry of Health and Welfare (Taiwan) (MOHW 107-TDU-B-211-114009 and 111-TDU-B-221-114001). This work was supported by the National Key Area International Cooperation Alliance: University Academic Alliance in, Kyushu-Okin Taiwan awa Open University, Medicine and Life Sciences Integrative Program,\u0026nbsp;which promotes international collaboration in advanced research.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWHL: Formal analysis, Investigation, Data Curation, Writing - Original Draft. XCHT, YYK, CLH, CYY, MCL, YLP, CYS, and MHT: Investigation. CCL, FMT, MYL, BSK, WCC, MY, and HFT: Resources. SCY and CTY: Investigation. HAH: Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Funding acquisition. All the authors have reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTefferi A, Vardiman JW. Myelodysplastic syndromes. N Engl J Med. 2009;361:1872-85.\u003c/li\u003e\n\u003cli\u003eGreenberg P, Cox C, LeBeau MM, Fenaux P, Morel P, Sanz G, et al. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood. 1997;89:2079-88.\u003c/li\u003e\n\u003cli\u003eGreenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sol\u0026eacute; F, et al. Revised international prognostic scoring system for myelodysplastic syndromes. 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Leuk Res. 2021;111:106733.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"blood-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"BLOOD RESEARCH","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"myelodysplastic syndromes/neoplasms, prognosis, risk stratification, lymphocyte, monocyte","lastPublishedDoi":"10.21203/rs.3.rs-7524368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7524368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eMyelodysplastic syndromes/neoplasms (MDS) represent a heterogeneous group of clonal hematopoietic disorders with variable prognosis. While several risk models exist, the prognostic role of immune-related biomarkers remains unclear. This study aimed to determine whether the lymphocyte-to-monocyte (L/M) ratio at diagnosis serves as an independent prognostic factor in MDS and to explore its biological correlates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA retrospective analysis of 554 patients with primary MDS diagnosed at the National Taiwan University Hospital was conducted. Patients were stratified by an L/M ratio cutoff of 1.5, determined by maximally selected rank statistics. Clinical, cytogenetic, and mutational profiles were assessed. Survival outcomes were analyzed using Kaplan–Meier methods and multivariable Cox regression incorporating IPSS-R, IPSS-M, and WHO-2022/ICC classifications. RNA sequencing was performed on diagnostic bone marrow samples to evaluate transcriptomic differences between groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003ePatients with L/M ratio \u0026gt; 1.5 were younger, had lower platelet counts, more advanced subtypes, and higher frequencies of \u003cem\u003eSTAG2\u003c/em\u003e and \u003cem\u003eU2AF1\u003c/em\u003e mutations. Elevated L/M ratio was significantly associated with inferior leukemia-free and overall survival, independent of established prognostic models. Adverse prognostic effects were mitigated by allogeneic hematopoietic stem cell transplantation but not by hypomethylating agents. Transcriptomic analysis revealed downregulation of inflammatory pathways (IL-2–STAT5, IL6–JAK–STAT3, interferon responses) and the p53 pathway, along with enrichment of MYC targets in the high L/M group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eAn elevated L/M ratio is an independent and readily available biomarker that predicts poor outcomes in MDS. Integration of this parameter into existing risk models may refine prognostication and guide treatment intensity. Transcriptomic findings suggest immune suppression and p53 deregulation underlie its adverse impact, highlighting potential therapeutic avenues.\u003c/p\u003e","manuscriptTitle":"Prognostic Impact of Lymphocyte to Monocyte Ratio in Patients with Myelodysplastic Neoplasms/Syndromes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 17:40:57","doi":"10.21203/rs.3.rs-7524368/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-10T11:48:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T11:39:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-21T02:47:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248039230540487770188592188107777018069","date":"2025-09-21T01:47:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8532758568947336331810529773463407231","date":"2025-09-21T00:18:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-19T06:20:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T13:04:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T11:18:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BLOOD RESEARCH","date":"2025-09-03T08:05:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"blood-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"BLOOD RESEARCH","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c5412512-f9a5-4207-b015-4da774c2539b","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-16T01:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 17:40:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7524368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7524368","identity":"rs-7524368","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}