LST1 Expression Correlates with Immune Infiltration and Predicts Poor Prognosis in Acute Myeloid Leukemia

LST1 Expression Correlates with Immune Infiltration and Predicts Poor Prognosis in Acute Myeloid Leukemia | 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 LST1 Expression Correlates with Immune Infiltration and Predicts Poor Prognosis in Acute Myeloid Leukemia Haitao Xu, Dangui Chen, Long Zhong, Lihong Wang, Fei Chen, Jia Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4515325/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Clinical management of acute myeloid leukemia (AML) poses significant challenges due to its poor prognosis and heterogeneous nature. Discovering new biomarkers is crucial for improving risk assessment and customizing treatment approaches. While leukocyte-specific transcript 1 (LST1) is implicated in inflammation and immune regulation, its function in AML remains ambiguous. In this investigation, we conduct a comprehensive investigation into LST1 expression profiles, clinical implications, functional pathways, and immune interactions in AML, leveraging multi-omics data and experimental validations. Our examination shows increased levels of LST1 expression in AML when compared to regular hematopoietic tissues, a discovery validated by RT-qPCR and Western blot analyses in a separate group. Elevated LST1 levels correlate with distinct clinicopathological features, including increased white blood cell counts, non-M3 FAB subtype, and intermediate/poor cytogenetic risk. Importantly, heightened LST1 levels predict unfavorable overall survival outcomes across various subgroups, independently of age and cytogenetic risk. We develop an integrative nomogram incorporating LST1 expression, demonstrating robust prognostic efficacy for patient survival. Transcriptomic profiling identifies 275 differentially expressed genes between LST1-high and -low AML cases, enriched in cytokine signaling, immune modulation, cell adhesion, and oncogenic pathways. Furthermore, LST1 exhibits significant associations with the infiltration of diverse immune cell subsets within the AML microenvironment, particularly myeloid cells and regulatory T cells (Tregs). In conclusion, our study establishes LST1 as a novel prognostic indicator with immunological relevance in AML, emphasizing its potential therapeutic implications. Further mechanistic elucidation of LST1 in AML pathogenesis is crucial for its clinical translation. LST1 AML biomarker immune infiltration prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Acute myeloid leukemia (AML) is marked by the existence of abnormal myeloid progenitor cell proliferation, which gives rise to a wide variety of hematologic malignancies, impairing the differentiation and uncontrolled proliferation[ 1 , 2 ].Despite progress in treatment and support, the prognosis for AML patients stays bleak, with a mere 29.5% survival rate after 5 years[ 3 ].This grim reality is attributed to the considerable genetic and biological heterogeneity intrinsic to AML, leading to variable treatment responses and increased susceptibility to relapse[ 4 , 5 ].While existing risk stratification methods incorporate cytogenetic and molecular markers, their prognostic accuracy is limited, underscoring the urgent need for innovative biomarkers capable of more precisely predicting clinical outcomes and guiding personalized therapeutic approaches. Leukocyte-specific transcript 1 (LST1), also called B144, encodes a transmembrane protein predominantly expressed in hematopoietic lineage cells[ 6 ]. Recent investigations highlight the crucial function of LST1 in controlling the immune cell migration and stimulation, thereby shaping the tumor microenvironment[ 7 ]. Aberrant LST1 expression has been implicated in various cancers, encompassing bladder cancer, germline tumor, and B-cell acute lymphoblastic leukemia, where it correlates with disease progression and unfavorable outcomes[ 8 – 10 ]. However, the association between LST1 expression and AML prognosis remains largely unexplored. Exploring the intersection of LST1 expression and immune cell infiltration could offer a valuable understanding of the pathobiology of AML and aid in pinpointing potential therapeutic targets, considering the substantial impact of the immune system on AML development and response to treatment. This research suggests that the levels of LST1 expression could be connected with the immunological cell presence in the AML tumor microenvironment, potentially acting as a dependable predictive marker for patients. Using transcriptomic data from The Cancer Genome Atlas (TCGA) and additional datasets, we aim to ascertain the link between LST1 expression and the composition of immune cells, along with its predictive value for overall survival (OS) and other clinicopathological factors in patients with AML. Our plan is to use a thorough bioinformatics strategy, including analyzing functional enrichment and examining protein-protein interaction (PPI) networks, to unveil the molecular mechanisms involved in the LST1 impact on AML development and immune regulation. Our study holds promise for developing a novel predictive model incorporating LST1 levels, thereby enhancing risk stratification and facilitating the design of personalized immunotherapeutic strategies for AML patients. Materials and Methods RNA Seq Data Acquisition and Bioinformatics Analysis Utilizing the UCSC XENA platform[ 11 ], the Pan-cancer RNA-seq datasets from TCGA and Genotype-Tissue Expression (GTEx) were obtained and standardized with the Toil help. The Level 3 HTSeq-FPKM and HTSeq-Count data for AML specimens were acquired from the TCGA data repository for further examination. All procedures conducted in this study adhered to the established guidelines outlined by TCGA and GTEx. Differential Gene Expression Analysis The DESeq 2 R package was used to evaluate differences in LST1 expression patterns in AML samples (HTSeq-Count), with a 50% threshold used to distinguish between low and high levels of expression[ 12 ]. This analysis aided in identifying genes that exhibited differential expression (DEGs). Following this, a visual representation in the form of a heat map was generated to visually illustrate the top 10 differently expressed genes (DEGs). Functional Enrichment Analysis Genes that met the criteria of having an absolute value of logFC > 2 and a padj less than 0.05 were chosen for analysis of functional enrichment. The ClusteProfiler package in R was employed to execute an examination of cellular component (CC), molecular function (MF), and biological process (BP) categories in Gene Ontology (GO), along with pathway analysis from the Kyoto Encyclopedia of Genes and Genomes (KEGG)[ 13 ]. The process of Gene Set Enrichment Analysis (GSEA) GSEA involves the analysis of gene sets to determine enrichment levels. We used the R software ClusteProfiler (v3.14.3) to analyze the variations in functions and pathways between LST1 high and low-expression groups employing GSEA [ 13 ]. Each analysis included permuting the gene set 1,000 times. Significant outcomes were determined based on adjusted P-values < 0.05 and False Discovery Rate (FDR) q-values < 0.25. Assessment of using Single-Sample Gene Set Enrichment Analysis (ssGSEA) The connection between immune cell infiltration and LST1 expression was evaluated by performing ssGSEA in R (version 3.6.3) with the GSVA package. We utilized a detailed array of 24 unique infiltrating immune cell types, as outlined in a previous publication[ 14 ]. To ascertain the connection between LST1 expression levels and the enrichment scores of 24 different immune cells types, a Spearman correlation analysis was applied. Enrichment scores were contrasted between groups with high and low LST1 expression levels using the Wilcoxon rank-sum test. Analyzing PPI Networks The Search Tool for the Retrieval of Interacting Genes (STRING) database was utilized to predict the interaction network of genes with altered expression levels[ 15 ].A threshold of 0.4 for the interaction score was used as the cutoff point. Afterward, the PPI network was displayed with Cytoscape (version 3.7.1)[ 16 ].The PPI network was analyzed employing MCODE (version 1.6.1) to detect crucial components[ 17 ], considering MCODE scores higher than 5, a minimum degree of 2, a node score cutoff of 0.2, a maximum depth of 100, and a k-score of 2. Pathway and process enrichment analysis of the identified modules were conducted employing Metascape. Predictive Model Development and Prediction With the utilization of the RMS R package (version 5.1-3), a nomogram was created to customize the forecast of OS in individuals diagnosed with AML. This nomogram incorporated essential clinical characteristics and was accompanied by calibration plots to assess its performance. Calibration curves were generated through plotting the nomogram-projected likelihood versus actual rates, with the ideal predictive values depicted by the 45° diagonal line. We assessed the nomogram's ability to discriminate by calculating the C-index with 1000 bootstrap samples. Additionally, we compared the nomogram's predictive performance with single prognostic factors through C-index and receiver operating characteristic (ROC) analysis. Statistical analyses were executed employing a two-tailed method, with a significance level set at 0.05. RT-qPCR for Quantitative Reverse Transcription Cells were employed to isolate total RNA with Trizol reagents depending on the manufacturer’s recommendations. Following this, 1 microgram of mRNA underwent reverse transcription to cDNA with a high-capacity cDNA kit as per the manufacturer's recommendations. The recovered cDNA was subsequently amplified employing qPCR with the SYBR Premix Ex Taq kit following the manufacturer's recommendations and examined on the ABI7300 Sequence Detection System. The qPCR procedure commenced with a denaturation phase lasting 10 minutes at 95°C, followed by 40 cycles of 15-second denaturation at 95°C and 1-minute annealing/extension at 60°C. Every test was done three times, and the data was analyzed using the 2^−ΔΔCT technique. For qRT-PCR analysis, the following primers were utilized: human LST1 (Forward: 5′- TCAGAGCAGGAACTCCACTAT − 3′, Reverse: 5′- CAGCAATGCAGGCATAGTC − 3′) and human GAPDH (Forward: 5′- TCAAGAAGGTGGTGAAGCAGG − 3′, Reverse: 5′- TCAAAGGTGGAGGAGTGGGT − 3′). Western Blot Analysis Cellular proteins were extracted utilizing a cold RIPA lysis buffer, and their quantities were estimated employing the BCA Protein Assay Kit. Then, 30 micrograms of proteins were extracted employing 10% SDS-PAGE gels and subsequently put onto 0.45-millimeter PVDF membranes (Millipore, USA). After a 2-hour period, during which 5% non-fat milk at the ambient temperature was employed, the membranes were then incubated overnight at 4°C with primary antibodies. Subsequent to washing the membranes three times with TBST buffer, they were then exposed to secondary antibodies linked to HRP for 1 hour at ambient temperature. The ECL detection system was utilized to visualize particular bands. The primary antibodies utilized were anti-LST1 (Abcam, 21361-1-AP, 1:2000) and anti-GAPDH (Proteintech, AB-P-R 001, 1:1000). Statistical Analysis The statistical analyses and figures were applied using R software, specifically version 3.6.2. The expression levels of LST1 in unpaired specimens were examined employing the Wilcoxon rank-sum test, whereas, for paired specimens, the Wilcoxon signed-rank test was deployed. The connection between clinical and cytogenetic features and LST1 expression was assessed using statistical methods, including the Kruskal-Wallis and Wilcoxon signed-rank tests and logistic regression analysis. Cox regression analysis and the Kaplan-Meier technique were employed to assess predictive factors like the LST1 expression level, with multivariate Cox analysis comparing its impact on survival to different clinical features. The median LST1 expression level served as the cut-off value. All statistical analyses were applied with a significance level set at P < 0.05. Furthermore, the pROC tool was employed for ROC analysis to evaluate how well the expression of the LST1 gene can distinguish between samples from individuals with AML and those who are healthy. The AUC score, which falls within the range of 0.5 to 1.0, reflects the capacity to differentiate between 50% and 100%. Results Evaluation of LST1 expression across diverse cancer types and AML RNA-seq information was obtained from the UCSC XENA repository in TCGA and GTEx variations and standardized through the Toil framework. Analyzing LST1 expression in normal specimens from TCGA and GTEx databases in contrast to cancer samples from TCGA showed a notable rise in LST1 levels across 23 various cancer types, such as AML. The association between LST1 and AML was further supported by an analysis of LST1 expression and its prognostic implications, utilizing data from GSE65409 within the Gene Expression Omnibus (GEO) repository. Consistent with TCGA observations, AML specimens exhibited elevated LST1 expression, with heightened LST1 levels significantly correlating with adverse prognosis among AML patients (refer to Fig. 1 C). Additionally, ROC analysis demonstrated the robust ability of LST1 expression to forecast outcomes, with an AUC of 0.980 (95% CI = 0.960–1.0), effectively distinguishing individuals with AML from healthy individuals (see Fig. 1 D). Furthermore, validation of increased LST1 expression in AML samples was achieved by obtaining bone marrow specimens from five healthy persons and five AML patients (refer to Fig. 1 E, Fig. 1 F-G). Detecting Differentially Expressed Genes in AML Specimens with Varied LST1 Levels An analysis was performed to identify differences in median mRNA levels by comparing gene expression profiles in groups with high and low LST1 expression. Utilizing RNA-seq-HTSeq-Counts data, we identified 275 genes exhibiting differential expression (DEGs). Among these, 142 genes demonstrated upregulation, while 133 genes displayed downregulation, illustrating significant disparities between the LST1-high and -low expression groups (|logFC| > 2, P < 0.05) (refer to Fig. 2 A). Furthermore, Fig. 2 B showcases the top five DEGs exhibiting upregulation and downregulation within the LST1-high and -low expression groups. Analyzing the enrichment of functions in DEGs and linked to variations in LST1 expression in AML We analyzed the impact of 275 DEGs linked to varying LST1 expression levels in AML through GO and KEGG functional enrichment studies with the clusterProfiler tool (see Supplementary Table 1, Fig. 3 A). Our analysis revealed associations with diverse BP, including the facilitation of cytokine production, modulation of immune effector processes, and augmentation of the MAPK cascade. CC included the extracellular matrix containing collagen, collagen trimers, and basement membrane components, while MF included roles in immune receptor function, transmembrane receptor protein tyrosine kinase activity, and pattern recognition receptor function. Furthermore, the KEGG analysis showed an increase in pathways like Cell adhesion molecules, Cytokine-cytokine receptor interaction, and ECM-receptor interaction. Additionally, a GSEA was applied to offer a deeper knowledge of the biological pathways linked to AML at various LST1 expression levels. The comparison of low and high LST1 expression datasets in AML unveiled important signaling pathways that were enriched. The abundance of pathways from the MSigDB Collection (C2.all.v7.0.symbols.gmt) manifested significant distinctions (FDR < 0.05, ADJ P < 0.05) (Supplementary Table 2 and Fig. 3 B). The phenotype showing decreased LST1 expression displayed numerous KEGG pathways connected with ECM-receptor interaction, focal adhesion, and pathways linked to cancer. Conversely, the heightened LST1 characteristic displayed notable enrichment in the chemokine and TOLL-like receptor signaling pathways (refer to Fig. 3 B). Relationship of LST1 Expression with Immune Cells Infiltration The connection between the LST1 gene expression levels and the presence of immunological cells is manifested through ssGSEA (Fig. 4 A). LST1 demonstrates positive associations with various immune cell types, including aDCs, eosinophils, iDCs, macrophages, neutrophils, TEM cells, Th17 cells, and Tregs. On the other hand, it shows negative associations with mast, NK, T helper, and TCM cells. Moreover, the Spearman correlation analysis reveals a strong connection between the LST1 expression levels and the immune cell infiltration in the AML microenvironment (Fig. 4 B). PPI Enrichment Analysis in AML A network was constructed with the assistance of STRING to elucidate the connection between LST1 and its potentially co-expressed genes from LST1-associated DEGs, using a threshold of 0.7. In total, 275 DEGs were identified (|log fold change (logFC)| > 2, P < 0.05). The PPI network generated comprised 210 nodes and 247 edges, visually represented using Cytoscape-MCODE (refer to Fig. 5 A). The module of greatest significance exhibited an MCODE score of 8.133, encompassing 12 nodes and 52 edges (refer to Fig. 5 B). Correlation of LST1 Gene Expression with Clinical Characteristics and Cytogenetic Risk Factors The primary clinical features of AML in TCGA are presented in Table 1. The 151 instances (68 females and 83 males) examined in this investigation had a mean age of 56.7 years. Out of the AML patients, 75 cases (49.3%) had decreased LST1 expression levels, with the remaining 76 cases (50.3%) exhibiting elevated levels. Strong correlations were found between LST1 levels and cytogenetics, FAB classification, and white blood cell (WBC) count through correlation analysis with a significance level of less than 0.001. Additionally, LST1 expression demonstrated significant associations with different factors, encompassing cytogenetic risk (P = 0.008) and NPM1 mutation (P = 0.014). Logistic regression was deployed to ascertain the connection between AML clinicopathological factors and the binary expression levels of LST1. As a result, there was a clear correlation between elevated levels of LST1 and an increase in WBC count over 20 x 10^9/L (with an odds ratio [OR] of 3.099; P < 0.001) and intermediate/normal cytogenetic risk (with an OR of 2.824; P = 0.020), as seen in Table 2. The Wilcoxon Rank Sum test was deployed to ascertain variations in LST1 expression levels among subjects exhibiting distinct clinicopathological features. The findings revealed that LST1 was significantly overexpressed in patients aged over 60, with WBC counts exceeding 20 × 10^9/L, non-M3 FAB classification, intermediate and poor cytogenetic risk, positive RAS mutation, and positive NPM1 mutation (refer to Fig. 6 A– 6 F). Effect of Elevated LST1 Levels on AML Outlook in Individuals with Varied Clinicopathological Conditions Kaplan-Meier analysis was applied to ascertain the connection between LST1 expression and prognosis in AML patients. Those who had high LST1 expression possessed a considerably poorer prognosis contrasted with those with low LST1 expression (Fig. 7 A). The hazard ratio (HR) was 2.029 (1.35–3.22), indicating a higher risk. The p-value was 0.001, indicating statistical significance. Subgroup analyses showed that higher levels of LST1 expression were linked to worse outcomes in specific subgroups defined by Age ≤ 60 (P = 0.011), Caucasian ancestry (P = 0.004), WBC counts ≤ 20 x 10^9/L (P 20% (P = 0.007), BM blasts > 70% (P = 0.002), FAB classification-M4 (P = 0.018), Intermediate Cytogenetic risk (P = 0.016), FLT3 mutation-positive (P = 0.019), NPM1 mutation-negative (P = 0.003), and RAS mutation-negative (P = 0.001) (see Fig. 7 B– 7 K). Furthermore, the forest plot depicted the predictive significance of LST1 across different AML subcategories through univariate and multivariate Cox regression analyses, corroborating the aforementioned findings (refer to Fig. 8 A-B). Further scrutiny via univariate Cox proportional hazards regression revealed that high LST1 expression, poor cytogenetic risk, intermediate cytogenetic risk, and age over 60 were all significant predictors of worse OS. A multivariate Cox regression analysis was conducted to include cytogenetic risk, age, and LST1. The findings indicated that people above the age of 60 (P < 0.001), with unfavorable or moderate cytogenetic risk (P = 0.006 and P = 0.03, respectively), and elevated LST1 levels (P = 0.028) were autonomous prognostic elements associated with poorer survival rates (P < 0.05). Prognostic Model of LST1 in AML A nomogram was constructed utilizing the RMS R program (refer to Fig. 9 A) to enhance the prediction of the AML patient's prognosis, employing the outcomes acquired from the Cox regression examination. The model incorporated three distinct prognostic indicators, namely age, cytogenetic risk, and LST1 expression, based on their statistical significance threshold of 0.2. These variables were assigned scores using a scoring system determined using multivariate Cox regression analysis. The total scores from all variables were adjusted to fall within a scale of 0 to 100. Following the calculation of the overall score, the chances of survival for AML patients at 1, 3, and 5 years were ascertained via tracing the cumulative score axis to the result axis. The nomogram's C-index, adjusted for bootstrap, was 0.728 (95% CI = 0.695–0.768), indicating a moderate level of accuracy in predicting OS in AML patients. The probability of surviving for one year was found to be less than 50%, with the chances of surviving for three and five years being less than 20% and 10%, respectively. The predicted outcomes from the nomogram calibration graph for OS were consistent with the actual outcomes for each patient (refer to Fig. 9 B). Discussion This investigation provides a comprehensive examination of the expression pattern, clinical relevance, functional pathways, and immune associations of LST1 in AML, leveraging multi-omics data alongside experimental validation. The main discoveries can be outlined as follows: 1) Expression Pattern Our research revealed a notable increase in LST1 concentrations in AML in contrast to healthy blood samples, validated through RT-qPCR and Western blot methods.2) Clinical Importance: High LST1 expression was linked to aggressive clinical characteristics like high WBC counts, non-M3 FAB subtype, and intermediate/poor cytogenetic risk, indicating its potential as a predictive tool.3) Predictive Value: Increased LST1 levels were an independent predictor of worse OS in both the entire AML group and various subgroups, highlighting its importance as a prognostic marker in AML.4) Functional Pathways and Immune Connections: LST1 expression was linked to specific gene expression profiles and immune infiltration patterns in the AML environment, suggesting its potential role in AML development and immune response regulation. In conclusion, these results emphasize the diverse functions of LST1 in AML, showcasing its usefulness as a diagnostic and prognostic tool and its potential implications for understanding AML pathophysiology and immune regulation. Further research into the mechanistic underpinnings of LST1 dysregulation in AML is warranted, potentially opening avenues for targeted therapeutic interventions. These outcomes underscore the clinical relevance of LST1 as a new predictive biomarker in AML. While previous investigations have elucidated the predictive significance of various molecular markers in AML[ 18 – 21 ], including NPM1, FLT3-ITD, CEBPA, and WT1, these markers often possess limitations, primarily restricted to specific AML subtypes and fail to encompass the disease's intricate heterogeneity. In contrast, our investigation demonstrates that LST1 overexpression is a prevalent characteristic across diverse AML subgroups and independently predicts poor prognosis. Notably, LST1 retains its significant prognostic importance even after adjustment for established risk factors encompassing age and cytogenetics, indicating its additional prognostic utility. Furthermore, the integration of LST1 expression into a nomogram model shows promising performance in predicting patient survival. The incorporation of this system could aid in risk stratification and impact treatment decisions for AML. The heightened expression of LST1 in AML likely contributes to the disease's aggressive behavior. Acting as a transmembrane protein, LST1 is involved in both inflammatory and immune responses[ 7 ].Prior research has shown that LST1 can improve the movement and infiltration of cancer cells, along with the secretion of inflammatory cytokines[ 10 , 22 ].In the context of AML, elevated LST1 expression may boost the proliferation and survival of leukemic blasts, as well as their interaction with the bone marrow microenvironment. Pathways associated with cytokine signaling, immune regulation, cell adhesion, and oncogenesis were identified through functional enrichment analysis of genes that showed differential expression in relation to LST1. These pathways play critical roles in AML pathogenesis and progression[ 23 – 26 ]. Hence, targeting LST1 emerges as a potential therapeutic strategy in AML, with the potential to modulate these vital pathways. To get more knowledge into the biological involvements of LST1 in AML, we applied comprehensive analyses of LST1-associated DEGs and their corresponding pathways. GO enrichment analysis unveiled the significant contributions of DEGs associated with LST1 in processes pertinent to the immune system, including cytokine production, regulation of immune response elements, and initiation of the MAPK cascade. These findings are consistent with the established roles of LST1 in immune regulation[ 7 , 27 ]. Moreover, analysis of routes in the KEGG revealed a high number of pathways related to cancer in DEGs linked to LST1. These pathways encompass cell adhesion molecules, interactions between cytokines and their receptors, and interactions between the extracellular matrix and receptors. Such pathways play crucial roles in mediating the connection between AML cells and the bone marrow microenvironment, thereby promoting leukemia cell survival and resistance to therapeutic agents [ 28 – 30 ]. Interestingly, GSEA unveiled distinct pathway enrichment patterns in AML cases with high and low levels of LST1 expression. Cases exhibiting low levels of LST1 demonstrated an elevation in pathways linked to ECM-receptor interaction and focal adhesion, whereas cases with high levels of LST1 displayed a significant rise in pathways connected with chemokine signaling and Toll-like receptor signaling. These findings suggest that LST1 overexpression may rewire the signaling network in AML cells, leading to heightened inflammatory responses and immune evasion. Importantly, previous studies have documented the activation of chemokine and Toll-like receptor signaling in AML, promoting leukemia cell proliferation, migration, and survival[ 31 – 33 ]. The outcomes of our research offer a new understanding of the molecular pathways implicated in the impact of increased LST1 levels on the progression of AML. The connection between LST1 levels and immunological cell presence in the AML microenvironment is another important focus of our research. Through ssGSEA analysis, a robust correlation was discovered between increased LST1 concentrations and the existence of various immune cell populations, particularly MDSCs, Tregs, and fatigued T lymphocytes. These immunosuppressive cell subsets have been well-documented to facilitate immune evasion and disease progression in AML[ 34 – 36 ]. Conversely, LST1 expression exhibited an adverse connection with the presence of cytotoxic immune cells encompassing natural killer (NK) and CD8 + T cells, which possess an essential involvement in combating leukemia [ 37 , 38 ]. These findings imply that LST1 overexpression may contribute to shaping an immunosuppressive milieu in AML, thereby promoting leukemia cell survival and evasion from immune surveillance. Therefore, focusing on LST1 or its related immune pathways is seen as a hopeful approach to boost anti-leukemia defenses and enhance treatment results in AML. However, our investigations also have certain limitations that warrant acknowledgment. Firstly, despite analyzing a substantial number of AML cases from public datasets, our validation cohort was relatively small, underscoring the necessity for confirmation of results in larger independent cohorts. Furthermore, despite conducting functional enrichment analyses on genes and pathways linked with LST1, no experimental validation was undertaken. Upcoming research should utilize both in vitro and in vivo models to ascertain the precise involvements of LST1 in the progression and evolution of AML. In summary, our investigation unveils the widespread occurrence of LST1 overexpression in AML, serving as an independent predictor of unfavorable outcomes. We explore the relationship between LST1 levels and unique gene expression patterns, as well as immune cell infiltration in the AML environment, which could impact disease advancement and immune system avoidance. The results highlight the possibility of LST1 as a new predictive biomarker and target for treating AML. Further comprehensive exploration, both functionally and clinically, is imperative to delineate the precise role of LST1 in AML pathogenesis and to translate these insights into enhanced patient care. Declarations Acknowledgments We would like to extend our appreciation to all researchers and institutions for their efforts in making the original datasets employed in this work accessible to the public. Access to Information and Resources The research data produced and examined can be accessed in the TCGA and GEO repositories through the following URLs: [TCGA](https://portal.gdc.cancer.gov/); [GEO](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi). Funding Funding for this project was provided by The Anhui Health and Medical Research Foundation (Grant No. AHWJ2022c002). Ethics approval and consent to participate This study was approved by the ethics committee of the Anqing Municipal Hospital (ID: No. 2024–72). All the enrolled patients signed an informed consent form. Competing Interests The writers affirm that they possess no conflicting concerns. Author Contributions Haitao Xu originated and formulated the research. 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Restoring NK cell functions in AML relapse. Blood. 2022; 140(26):2765–2766. Zhong F, Yao F, Jiang J, Yu X, Liu J, Huang B and Wang X. CD8 + T cell-based molecular subtypes with heterogeneous immune landscapes and clinical significance in acute myeloid leukemia. Inflamm Res. 2024; 73(3):329–344. Tables Table 1. Evaluation of the Correlation between LST1 Gene Expression and Clinical Characteristics in Acute Myeloid Leukemia (AML) Specimens from the TCGA Database. Characteristic Low expression of LST1 High expression of LST1 p n 75 76 Gender, n (%) 0.457 Female 31 (20.5%) 37 (24.5%) Male 44 (29.1%) 39 (25.8%) Race, n (%) 0.154 Asian 0 (0%) 1 (0.7%) Black or African American 9 (6%) 4 (2.7%) White 64 (43%) 71 (47.7%) Age, n (%) 0.114 60 26 (17.2%) 37 (24.5%) WBC count(x10^9/L), n (%) < 0.001 20 25 (16.7%) 48 (32%) BM blasts(%), n (%) 0.665 20 47 (31.1%) 44 (29.1%) PB blasts(%), n (%) 1.000 70 39 (25.8%) 40 (26.5%) Cytogenetic risk, n (%) 0.018 Favorable 21 (14.1%) 10 (6.7%) Intermediate 33 (22.1%) 49 (32.9%) Poor 21 (14.1%) 15 (10.1%) FAB classifications, n (%) < 0.001 M0 10 (6.7%) 5 (3.3%) M1 18 (12%) 17 (11.3%) M2 22 (14.7%) 16 (10.7%) M3 15 (10%) 0 (0%) M4 4 (2.7%) 25 (16.7%) M5 2 (1.3%) 13 (8.7%) M6 2 (1.3%) 0 (0%) M7 1 (0.7%) 0 (0%) Cytogenetics, n (%) < 0.001 Normal 29 (21.5%) 40 (29.6%) +8 8 (5.9%) 0 (0%) del(5) 0 (0%) 1 (0.7%) del(7) 4 (3%) 2 (1.5%) inv(16) 0 (0%) 8 (5.9%) t(15;17) 11 (8.1%) 0 (0%) t(8;21) 7 (5.2%) 0 (0%) t(9;11) 0 (0%) 1 (0.7%) Complex 12 (8.9%) 12 (8.9%) FLT3 mutation, n (%) 0.247 Negative 53 (36.1%) 49 (33.3%) Positive 18 (12.2%) 27 (18.4%) IDH1 R132 mutation, n (%) 0.077 Negative 64 (43%) 72 (48.3%) Positive 10 (6.7%) 3 (2%) IDH1 R140 mutation, n (%) 0.819 Negative 68 (45.6%) 69 (46.3%) Positive 5 (3.4%) 7 (4.7%) IDH1 R172 mutation, n (%) 0.238 Negative 71 (47.7%) 76 (51%) Positive 2 (1.3%) 0 (0%) RAS mutation, n (%) 0.063 Negative 73 (48.7%) 69 (46%) Positive 1 (0.7%) 7 (4.7%) NPM1 mutation, n (%) 0.008 Negative 65 (43.3%) 52 (34.7%) Positive 9 (6%) 24 (16%) Age, meidan (IQR) 52 (36.5, 63.5) 60 (48, 68.5) 0.008 Table 2 . Logistic Regression Analysis Investigating the Association between Clinicopathological Factors of AML and the Expression of LST1. Characteristics Total (N) OR (95% CI) P value Gender (Male vs. Female) 150 0.685 (0.359 - 1.307) 0.251 Race (White vs. Asian&Black or African American) 149 1.938 (0.617 - 6.086) 0.257 Age (> 60 vs. 20 vs. <= 20) 149 3.099 (1.588 - 6.046) 20 vs. 70 vs. <= 70) 150 0.948 (0.499 - 1.800) 0.870 Cytogenetic risk (Intermediate/normal vs. Favorable) 112 2.824 (1.175 - 6.787) 0.020 FLT3 mutation (Positive vs. Negative) 146 1.452 (0.715 - 2.950) 0.302 IDH1 R132 mutation (Positive vs. Negative) 148 0.270 (0.071 - 1.026) 0.055 IDH1 R140 mutation (Positive vs. Negative) 148 1.400 (0.423 - 4.629) 0.581 IDH1 R172 mutation (Positive vs. Negative) 148 0.000 (0.000 - Inf) 0.996 RAS mutation (Positive vs. Negative) 149 7.515 (0.901 - 62.679) 0.043 NPM1 mutation (Positive vs. Negative) 149 2.831 (1.237 - 6.477) 0.014 Table 3. Univariate and Multivariate Cox Regression Analysis of Factors Influencing Overall Survival (OS) in AML Patients. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Age 140 60 61 3.333 (2.164-5.134) <0.001 2.791 (1.712-4.550) <0.001 Cytogenetic risk 138 Favorable 31 Reference Intermediate 76 2.957 (1.498-5.836) 0.002 2.162(1.079-4.335) 0.03 Poor 31 4.157 (1.944-8.893) <0.001 2.993(1.365-6.563) 0.006 LST1 140 Low 70 Reference High 70 2.038 (1.323-3.139) 0.001 1.763 (1.063-2.923) 0.028 FLT3 mutation 136 Negative 97 Reference Positive 39 1.271 (0.801-2.016) 0.309 NPM1 mutation 139 Negative 106 Reference Positive 33 1.137 (0.706-1.832) 0.596 Gender 140 Female 63 Reference Male 77 1.030 (0.674-1.572) 0.892 RAS mutation 139 Negative 131 Reference Positive 8 0.643 (0.235-1.760) 0.390 WBC count(x10^9/L) 139 20 64 1.161 (0.760-1.772) 0.490 PB blasts(%) 140 70 74 1.230 (0.806-1.878) 0.338 Race 137 Black or African American 10 Reference White 127 1.383 (0.506-3.780) 0.527 IDH1 R132 mutation 138 Negative 126 Reference Positive 12 0.588 (0.238-1.452) 0.249 IDH1 R140 mutation 138 Negative 127 Reference Positive 11 1.131 (0.565-2.264) 0.727 IDH1 R172 mutation 138 Negative 136 Reference Positive 2 0.610 (0.085-4.385) 0.623 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx Supplementary Table 1. Analyzing Gene Enrichment in Connection with LST1 Levels through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways. SupplementaryTable2.xlsx Supplementary Table 2. Conducting Gene Set Enrichment Analysis (GSEA) on Differentially Expressed Genes (DEGs) linked to LST1 Expression. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4515325","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315276316,"identity":"9ec15923-9023-4783-bccb-7b2f29469b8f","order_by":0,"name":"Haitao Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDCCAyBUwMDAz97Y+PAD8VoMGBgkew43G0sQq4UBpMXgRnqbAA8xOviOnz144IPB4cQNNx+2MUgw2MnpNhDQInkmL+HgDIO0xJm3E9seFDAkG5sdIKDF4ECOwWEeA5vEvtuJ7QYSDAcStxHUcv6NweE/BhKJDTcPtknwEKXlBtAWBqAtE24wEqlF8sYbg4M9BmnGM3sSgYFsQIRf+M7nGH/4UXFYtp/9+MOHHyrs5AhqQXcnacpHwSgYBaNgFOAAAOHCS3/oLnvsAAAAAElFTkSuQmCC","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Xu","suffix":""},{"id":315276317,"identity":"a02e515a-eb00-496f-9edd-dbb4587bfb71","order_by":1,"name":"Dangui Chen","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dangui","middleName":"","lastName":"Chen","suffix":""},{"id":315276318,"identity":"e388a73b-2a4b-4022-8d1f-c918b8d15f9c","order_by":2,"name":"Long Zhong","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Zhong","suffix":""},{"id":315276319,"identity":"a0bddfc3-5448-4416-9e87-ed06fd2209b9","order_by":3,"name":"Lihong Wang","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lihong","middleName":"","lastName":"Wang","suffix":""},{"id":315276320,"identity":"2f149a26-3f9c-4878-8cf6-62086f390ab4","order_by":4,"name":"Fei Chen","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Chen","suffix":""},{"id":315276321,"identity":"9a39835e-f9b2-4bed-9d05-c9cdb0ba9688","order_by":5,"name":"Jia Lu","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-06-02 02:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4515325/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4515325/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58752772,"identity":"5c04fd66-b05d-4ef9-80cd-ff3f83b04a18","added_by":"auto","created_at":"2024-06-20 16:15:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":436547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of LST1 in Acute Myeloid Leukemia (AML) Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Comparative examination of LST1 expression levels in various cancer tissues of AML patients versus normal tissues sourced from TCGA. AML patients exhibit increased levels of LST1 in comparison to normal tissues. (C) Validation of LST1 overexpression in AML using the GSE65409 dataset. A ROC curve was used to show how LST1 could be useful for diagnosing AML. (E) Quantitative LST1 mRNA expression via qRT-PCR in AML patient specimens (n = 5) versus controls (n = 5). Western blot was executed to measure the LST1 protein levels in individuals with AML. Significance levels: *, p \u0026lt; 0.01; ***, p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/38af90ca31f727588dfb0440.jpg"},{"id":58754188,"identity":"3e70d250-94a4-4fcc-9a64-5e72893dca73","added_by":"auto","created_at":"2024-06-20 16:23:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":872517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of Genes with Differential Expression (DEGs) in High and Low LST1 Expression Categories.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot displaying DEGs, including 142 elevated and 133 mitigated genes. Normalized expression levels are shown on a scale ranging from green to red. (B) Heat map showing 10 DEGs, with 5 genes being elevated and 5 genes being declined. Specimens are represented on the X-axis, with DEGs indicated on the Y-axis. Green and red tones indicate declined and raised genes, respectively.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/8a42a756dbb99ada4a3451ce.jpg"},{"id":58752783,"identity":"46039668-a3c6-4fe2-abf7-718d79219fa8","added_by":"auto","created_at":"2024-06-20 16:15:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":870291,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment Analysis of DEGs between High and Low LST1 Expression in AML Patients from TCGA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExamining differentially expressed genes (DEGs) employing the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) approaches. Performing Gene Set Enrichment Analysis (GSEA) on DEGs.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/b7c903043da5c423be17ff78.jpg"},{"id":58752781,"identity":"19a266a0-967b-46f6-b286-88ec3ebddf67","added_by":"auto","created_at":"2024-06-20 16:15:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1791329,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of LST1 Expression with Immune Infiltration in the AML Microenvironment. \u003c/strong\u003e(A)The relationship between the enrichment score of various tumor-infiltrating immune cells and the LST1 expression level is measured in Transcripts Per Million (TPM). (B) The connection between LST1 gene expression and the percentage of 24 distinct immune cell types. Each dot represents the absolute value of Spearman's correlation coefficient in terms of its magnitude.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/c0050d839eb21485cfe3332a.jpg"},{"id":58752778,"identity":"1c9024e0-9be7-43bf-8c3a-38cc69127baa","added_by":"auto","created_at":"2024-06-20 16:15:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1147644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe main module of the Protein-Protein Interaction (PPI) Network involving DEGs associated with LST1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The PPI network of differentially expressed genes (DEGs) was assembled employing Cytoscape. (B) The predominant module extracted from the PPI network comprises 12 nodes and 52 edges.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/0b03596b48309533b9c5dadb.jpg"},{"id":58752780,"identity":"a2de1cb6-3656-49f9-bc3d-41942d55bf53","added_by":"auto","created_at":"2024-06-20 16:15:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":192453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between LST1 Expression and Clinical Characteristics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformation is provided on (A) Age; (B) White Blood Cell (WBC) counts (20×10^9); (C) French-American-British (FAB) classification; (D) Cytogenetics risk; (E) RAS mutation; (F) NPM1 mutation. Significance levels: *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/6399c3d27db54a2e3150460f.jpg"},{"id":58752779,"identity":"7837a852-e536-4f9b-ba57-cec64cd1b7c5","added_by":"auto","created_at":"2024-06-20 16:15:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":542294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElevated LST1 Expression Linked to Inferior Overall Survival (OS) in AML Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival curves showing OS in acute myeloid leukemia (AML) patients who are 60 years old or younger, of Caucasian descent, have a white blood cell count of less than or equal to 20×10^9/L, bone marrow blasts exceeding 20%, peripheral blood blasts exceeding 70%, classified as FAB-M4, with intermediate cytogenetic risk, positive for FLT3 mutation, negative for NPM1 mutation, and negative for RAS mutation.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/0e553e74babcd8fffcd5e11e.jpg"},{"id":58754537,"identity":"8414fb27-77d8-4a4c-a69f-ea1f122c227b","added_by":"auto","created_at":"2024-06-20 16:31:24","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1124937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUnivariate and Multivariate Analysis of Clinicopathological Factors Correlating with OS in AML Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Forest plot according to univariate Cox analysis for OS. (B) Forest plot according to multivariate Cox analysis for OS.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/347633ae9f74b7cf27035d83.jpg"},{"id":58754191,"identity":"81bffec4-d9eb-416d-8267-f4d60aa2ddd0","added_by":"auto","created_at":"2024-06-20 16:23:25","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":201397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAML Prognostic Predictive Model Integrating LST1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Nomogram forecasting the likelihood of an OS of 1, 3, and 5 years for AML. (B) Calibration graph displaying the nomogram's accuracy in anticipating the likelihood of OS at 1, 3, and 5 years.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/a371dbed79d76dea575f1593.jpg"},{"id":68414444,"identity":"c1c8379d-9115-40af-8cd9-31fbae99279c","added_by":"auto","created_at":"2024-11-07 04:39:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7279685,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/d0ea9517-4d21-4f4c-ae6c-9c9103f3a98f.pdf"},{"id":58752777,"identity":"654006a8-02df-4b20-8ab2-ba53be3d2738","added_by":"auto","created_at":"2024-06-20 16:15:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":92540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e. Analyzing Gene Enrichment in Connection with LST1 Levels through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/db4c99a951a93de57f0725bf.xlsx"},{"id":58752773,"identity":"e4323669-7d67-4d91-b5c1-631831588ff4","added_by":"auto","created_at":"2024-06-20 16:15:24","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2. \u003c/strong\u003eConducting Gene Set Enrichment Analysis (GSEA) on Differentially Expressed Genes (DEGs) linked to LST1 Expression.\u003c/p\u003e","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4515325/v1/6e7cbff495577bbf996cb0ae.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"LST1 Expression Correlates with Immune Infiltration and Predicts Poor Prognosis in Acute Myeloid Leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is marked by the existence of abnormal myeloid progenitor cell proliferation, which gives rise to a wide variety of hematologic malignancies, impairing the differentiation and uncontrolled proliferation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Despite progress in treatment and support, the prognosis for AML patients stays bleak, with a mere 29.5% survival rate after 5 years[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].This grim reality is attributed to the considerable genetic and biological heterogeneity intrinsic to AML, leading to variable treatment responses and increased susceptibility to relapse[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].While existing risk stratification methods incorporate cytogenetic and molecular markers, their prognostic accuracy is limited, underscoring the urgent need for innovative biomarkers capable of more precisely predicting clinical outcomes and guiding personalized therapeutic approaches.\u003c/p\u003e \u003cp\u003eLeukocyte-specific transcript 1 (LST1), also called B144, encodes a transmembrane protein predominantly expressed in hematopoietic lineage cells[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent investigations highlight the crucial function of LST1 in controlling the immune cell migration and stimulation, thereby shaping the tumor microenvironment[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Aberrant LST1 expression has been implicated in various cancers, encompassing bladder cancer, germline tumor, and B-cell acute lymphoblastic leukemia, where it correlates with disease progression and unfavorable outcomes[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the association between LST1 expression and AML prognosis remains largely unexplored. Exploring the intersection of LST1 expression and immune cell infiltration could offer a valuable understanding of the pathobiology of AML and aid in pinpointing potential therapeutic targets, considering the substantial impact of the immune system on AML development and response to treatment.\u003c/p\u003e \u003cp\u003eThis research suggests that the levels of LST1 expression could be connected with the immunological cell presence in the AML tumor microenvironment, potentially acting as a dependable predictive marker for patients. Using transcriptomic data from The Cancer Genome Atlas (TCGA) and additional datasets, we aim to ascertain the link between LST1 expression and the composition of immune cells, along with its predictive value for overall survival (OS) and other clinicopathological factors in patients with AML. Our plan is to use a thorough bioinformatics strategy, including analyzing functional enrichment and examining protein-protein interaction (PPI) networks, to unveil the molecular mechanisms involved in the LST1 impact on AML development and immune regulation. Our study holds promise for developing a novel predictive model incorporating LST1 levels, thereby enhancing risk stratification and facilitating the design of personalized immunotherapeutic strategies for AML patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRNA Seq Data Acquisition and Bioinformatics Analysis\u003c/h2\u003e \u003cp\u003eUtilizing the UCSC XENA platform[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the Pan-cancer RNA-seq datasets from TCGA and Genotype-Tissue Expression (GTEx) were obtained and standardized with the Toil help. The Level 3 HTSeq-FPKM and HTSeq-Count data for AML specimens were acquired from the TCGA data repository for further examination. All procedures conducted in this study adhered to the established guidelines outlined by TCGA and GTEx.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Gene Expression Analysis\u003c/h2\u003e \u003cp\u003eThe DESeq 2 R package was used to evaluate differences in LST1 expression patterns in AML samples (HTSeq-Count), with a 50% threshold used to distinguish between low and high levels of expression[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This analysis aided in identifying genes that exhibited differential expression (DEGs). Following this, a visual representation in the form of a heat map was generated to visually illustrate the top 10 differently expressed genes (DEGs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGenes that met the criteria of having an absolute value of logFC\u0026thinsp;\u0026gt;\u0026thinsp;2 and a padj less than 0.05 were chosen for analysis of functional enrichment. The ClusteProfiler package in R was employed to execute an examination of cellular component (CC), molecular function (MF), and biological process (BP) categories in Gene Ontology (GO), along with pathway analysis from the Kyoto Encyclopedia of Genes and Genomes (KEGG)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eThe process of Gene Set Enrichment Analysis (GSEA)\u003c/h2\u003e \u003cp\u003eGSEA involves the analysis of gene sets to determine enrichment levels. We used the R software ClusteProfiler (v3.14.3) to analyze the variations in functions and pathways between LST1 high and low-expression groups employing GSEA [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Each analysis included permuting the gene set 1,000 times. Significant outcomes were determined based on adjusted P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and False Discovery Rate (FDR) q-values\u0026thinsp;\u0026lt;\u0026thinsp;0.25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of using Single-Sample Gene Set Enrichment Analysis (ssGSEA)\u003c/h2\u003e \u003cp\u003eThe connection between immune cell infiltration and LST1 expression was evaluated by performing ssGSEA in R (version 3.6.3) with the GSVA package. We utilized a detailed array of 24 unique infiltrating immune cell types, as outlined in a previous publication[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To ascertain the connection between LST1 expression levels and the enrichment scores of 24 different immune cells types, a Spearman correlation analysis was applied. Enrichment scores were contrasted between groups with high and low LST1 expression levels using the Wilcoxon rank-sum test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalyzing PPI Networks\u003c/h2\u003e \u003cp\u003eThe Search Tool for the Retrieval of Interacting Genes (STRING) database was utilized to predict the interaction network of genes with altered expression levels[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].A threshold of 0.4 for the interaction score was used as the cutoff point. Afterward, the PPI network was displayed with Cytoscape (version 3.7.1)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].The PPI network was analyzed employing MCODE (version 1.6.1) to detect crucial components[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], considering MCODE scores higher than 5, a minimum degree of 2, a node score cutoff of 0.2, a maximum depth of 100, and a k-score of 2. Pathway and process enrichment analysis of the identified modules were conducted employing Metascape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePredictive Model Development and Prediction\u003c/h2\u003e \u003cp\u003eWith the utilization of the RMS R package (version 5.1-3), a nomogram was created to customize the forecast of OS in individuals diagnosed with AML. This nomogram incorporated essential clinical characteristics and was accompanied by calibration plots to assess its performance. Calibration curves were generated through plotting the nomogram-projected likelihood versus actual rates, with the ideal predictive values depicted by the 45\u0026deg; diagonal line. We assessed the nomogram's ability to discriminate by calculating the C-index with 1000 bootstrap samples. Additionally, we compared the nomogram's predictive performance with single prognostic factors through C-index and receiver operating characteristic (ROC) analysis. Statistical analyses were executed employing a two-tailed method, with a significance level set at 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRT-qPCR for Quantitative Reverse Transcription\u003c/h2\u003e \u003cp\u003eCells were employed to isolate total RNA with Trizol reagents depending on the manufacturer\u0026rsquo;s recommendations. Following this, 1 microgram of mRNA underwent reverse transcription to cDNA with a high-capacity cDNA kit as per the manufacturer's recommendations. The recovered cDNA was subsequently amplified employing qPCR with the SYBR Premix Ex Taq kit following the manufacturer's recommendations and examined on the ABI7300 Sequence Detection System. The qPCR procedure commenced with a denaturation phase lasting 10 minutes at 95\u0026deg;C, followed by 40 cycles of 15-second denaturation at 95\u0026deg;C and 1-minute annealing/extension at 60\u0026deg;C. Every test was done three times, and the data was analyzed using the 2^\u0026minus;ΔΔCT technique. For qRT-PCR analysis, the following primers were utilized: human LST1 (Forward: 5\u0026prime;- TCAGAGCAGGAACTCCACTAT \u0026minus;\u0026thinsp;3\u0026prime;, Reverse: 5\u0026prime;- CAGCAATGCAGGCATAGTC \u0026minus;\u0026thinsp;3\u0026prime;) and human GAPDH (Forward: 5\u0026prime;- TCAAGAAGGTGGTGAAGCAGG \u0026minus;\u0026thinsp;3\u0026prime;, Reverse: 5\u0026prime;- TCAAAGGTGGAGGAGTGGGT \u0026minus;\u0026thinsp;3\u0026prime;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blot Analysis\u003c/h2\u003e \u003cp\u003eCellular proteins were extracted utilizing a cold RIPA lysis buffer, and their quantities were estimated employing the BCA Protein Assay Kit. Then, 30 micrograms of proteins were extracted employing 10% SDS-PAGE gels and subsequently put onto 0.45-millimeter PVDF membranes (Millipore, USA). After a 2-hour period, during which 5% non-fat milk at the ambient temperature was employed, the membranes were then incubated overnight at 4\u0026deg;C with primary antibodies. Subsequent to washing the membranes three times with TBST buffer, they were then exposed to secondary antibodies linked to HRP for 1 hour at ambient temperature. The ECL detection system was utilized to visualize particular bands. The primary antibodies utilized were anti-LST1 (Abcam, 21361-1-AP, 1:2000) and anti-GAPDH (Proteintech, AB-P-R 001, 1:1000).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analyses and figures were applied using R software, specifically version 3.6.2. The expression levels of LST1 in unpaired specimens were examined employing the Wilcoxon rank-sum test, whereas, for paired specimens, the Wilcoxon signed-rank test was deployed. The connection between clinical and cytogenetic features and LST1 expression was assessed using statistical methods, including the Kruskal-Wallis and Wilcoxon signed-rank tests and logistic regression analysis. Cox regression analysis and the Kaplan-Meier technique were employed to assess predictive factors like the LST1 expression level, with multivariate Cox analysis comparing its impact on survival to different clinical features. The median LST1 expression level served as the cut-off value. All statistical analyses were applied with a significance level set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Furthermore, the pROC tool was employed for ROC analysis to evaluate how well the expression of the LST1 gene can distinguish between samples from individuals with AML and those who are healthy. The AUC score, which falls within the range of 0.5 to 1.0, reflects the capacity to differentiate between 50% and 100%.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of LST1 expression across diverse cancer types and AML\u003c/h2\u003e \u003cp\u003eRNA-seq information was obtained from the UCSC XENA repository in TCGA and GTEx variations and standardized through the Toil framework. Analyzing LST1 expression in normal specimens from TCGA and GTEx databases in contrast to cancer samples from TCGA showed a notable rise in LST1 levels across 23 various cancer types, such as AML.\u003c/p\u003e \u003cp\u003eThe association between LST1 and AML was further supported by an analysis of LST1 expression and its prognostic implications, utilizing data from GSE65409 within the Gene Expression Omnibus (GEO) repository. Consistent with TCGA observations, AML specimens exhibited elevated LST1 expression, with heightened LST1 levels significantly correlating with adverse prognosis among AML patients (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Additionally, ROC analysis demonstrated the robust ability of LST1 expression to forecast outcomes, with an AUC of 0.980 (95% CI\u0026thinsp;=\u0026thinsp;0.960\u0026ndash;1.0), effectively distinguishing individuals with AML from healthy individuals (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, validation of increased LST1 expression in AML samples was achieved by obtaining bone marrow specimens from five healthy persons and five AML patients (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-G).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDetecting Differentially Expressed Genes in AML Specimens with Varied LST1 Levels\u003c/h2\u003e \u003cp\u003eAn analysis was performed to identify differences in median mRNA levels by comparing gene expression profiles in groups with high and low LST1 expression. Utilizing RNA-seq-HTSeq-Counts data, we identified 275 genes exhibiting differential expression (DEGs). Among these, 142 genes demonstrated upregulation, while 133 genes displayed downregulation, illustrating significant disparities between the LST1-high and -low expression groups (|logFC| \u0026gt; 2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB showcases the top five DEGs exhibiting upregulation and downregulation within the LST1-high and -low expression groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalyzing the enrichment of functions in DEGs and linked to variations in LST1 expression in AML\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe analyzed the impact of 275 DEGs linked to varying LST1 expression levels in AML through GO and KEGG functional enrichment studies with the clusterProfiler tool (see Supplementary Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Our analysis revealed associations with diverse BP, including the facilitation of cytokine production, modulation of immune effector processes, and augmentation of the MAPK cascade. CC included the extracellular matrix containing collagen, collagen trimers, and basement membrane components, while MF included roles in immune receptor function, transmembrane receptor protein tyrosine kinase activity, and pattern recognition receptor function. Furthermore, the KEGG analysis showed an increase in pathways like Cell adhesion molecules, Cytokine-cytokine receptor interaction, and ECM-receptor interaction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, a GSEA was applied to offer a deeper knowledge of the biological pathways linked to AML at various LST1 expression levels. The comparison of low and high LST1 expression datasets in AML unveiled important signaling pathways that were enriched. The abundance of pathways from the MSigDB Collection (C2.all.v7.0.symbols.gmt) manifested significant distinctions (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ADJ P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Table\u0026nbsp;2 and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The phenotype showing decreased LST1 expression displayed numerous KEGG pathways connected with ECM-receptor interaction, focal adhesion, and pathways linked to cancer. Conversely, the heightened LST1 characteristic displayed notable enrichment in the chemokine and TOLL-like receptor signaling pathways (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRelationship of LST1 Expression with Immune Cells Infiltration\u003c/h2\u003e \u003cp\u003eThe connection between the LST1 gene expression levels and the presence of immunological cells is manifested through ssGSEA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). LST1 demonstrates positive associations with various immune cell types, including aDCs, eosinophils, iDCs, macrophages, neutrophils, TEM cells, Th17 cells, and Tregs. On the other hand, it shows negative associations with mast, NK, T helper, and TCM cells. Moreover, the Spearman correlation analysis reveals a strong connection between the LST1 expression levels and the immune cell infiltration in the AML microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePPI Enrichment Analysis in AML\u003c/h2\u003e \u003cp\u003eA network was constructed with the assistance of STRING to elucidate the connection between LST1 and its potentially co-expressed genes from LST1-associated DEGs, using a threshold of 0.7. In total, 275 DEGs were identified (|log fold change (logFC)| \u0026gt; 2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The PPI network generated comprised 210 nodes and 247 edges, visually represented using Cytoscape-MCODE (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The module of greatest significance exhibited an MCODE score of 8.133, encompassing 12 nodes and 52 edges (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of LST1 Gene Expression with Clinical Characteristics and Cytogenetic Risk Factors\u003c/h2\u003e \u003cp\u003eThe primary clinical features of AML in TCGA are presented in Table\u0026nbsp;1. The 151 instances (68 females and 83 males) examined in this investigation had a mean age of 56.7 years. Out of the AML patients, 75 cases (49.3%) had decreased LST1 expression levels, with the remaining 76 cases (50.3%) exhibiting elevated levels. Strong correlations were found between LST1 levels and cytogenetics, FAB classification, and white blood cell (WBC) count through correlation analysis with a significance level of less than 0.001. Additionally, LST1 expression demonstrated significant associations with different factors, encompassing cytogenetic risk (P\u0026thinsp;=\u0026thinsp;0.008) and NPM1 mutation (P\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e \u003cp\u003eLogistic regression was deployed to ascertain the connection between AML clinicopathological factors and the binary expression levels of LST1. As a result, there was a clear correlation between elevated levels of LST1 and an increase in WBC count over 20 x 10^9/L (with an odds ratio [OR] of 3.099; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and intermediate/normal cytogenetic risk (with an OR of 2.824; P\u0026thinsp;=\u0026thinsp;0.020), as seen in Table\u0026nbsp;2. The Wilcoxon Rank Sum test was deployed to ascertain variations in LST1 expression levels among subjects exhibiting distinct clinicopathological features. The findings revealed that LST1 was significantly overexpressed in patients aged over 60, with WBC counts exceeding 20 \u0026times; 10^9/L, non-M3 FAB classification, intermediate and poor cytogenetic risk, positive RAS mutation, and positive NPM1 mutation (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEffect of Elevated LST1 Levels on AML Outlook in Individuals with Varied Clinicopathological Conditions\u003c/h2\u003e \u003cp\u003eKaplan-Meier analysis was applied to ascertain the connection between LST1 expression and prognosis in AML patients. Those who had high LST1 expression possessed a considerably poorer prognosis contrasted with those with low LST1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The hazard ratio (HR) was 2.029 (1.35\u0026ndash;3.22), indicating a higher risk. The p-value was 0.001, indicating statistical significance. Subgroup analyses showed that higher levels of LST1 expression were linked to worse outcomes in specific subgroups defined by Age\u0026thinsp;\u0026le;\u0026thinsp;60 (P\u0026thinsp;=\u0026thinsp;0.011), Caucasian ancestry (P\u0026thinsp;=\u0026thinsp;0.004), WBC counts\u0026thinsp;\u0026le;\u0026thinsp;20 x 10^9/L (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PB blasts\u0026thinsp;\u0026gt;\u0026thinsp;20% (P\u0026thinsp;=\u0026thinsp;0.007), BM blasts\u0026thinsp;\u0026gt;\u0026thinsp;70% (P\u0026thinsp;=\u0026thinsp;0.002), FAB classification-M4 (P\u0026thinsp;=\u0026thinsp;0.018), Intermediate Cytogenetic risk (P\u0026thinsp;=\u0026thinsp;0.016), FLT3 mutation-positive (P\u0026thinsp;=\u0026thinsp;0.019), NPM1 mutation-negative (P\u0026thinsp;=\u0026thinsp;0.003), and RAS mutation-negative (P\u0026thinsp;=\u0026thinsp;0.001) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eK).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the forest plot depicted the predictive significance of LST1 across different AML subcategories through univariate and multivariate Cox regression analyses, corroborating the aforementioned findings (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther scrutiny via univariate Cox proportional hazards regression revealed that high LST1 expression, poor cytogenetic risk, intermediate cytogenetic risk, and age over 60 were all significant predictors of worse OS. A multivariate Cox regression analysis was conducted to include cytogenetic risk, age, and LST1. The findings indicated that people above the age of 60 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with unfavorable or moderate cytogenetic risk (P\u0026thinsp;=\u0026thinsp;0.006 and P\u0026thinsp;=\u0026thinsp;0.03, respectively), and elevated LST1 levels (P\u0026thinsp;=\u0026thinsp;0.028) were autonomous prognostic elements associated with poorer survival rates (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Model of LST1 in AML\u003c/h2\u003e \u003cp\u003eA nomogram was constructed utilizing the RMS R program (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA) to enhance the prediction of the AML patient's prognosis, employing the outcomes acquired from the Cox regression examination. The model incorporated three distinct prognostic indicators, namely age, cytogenetic risk, and LST1 expression, based on their statistical significance threshold of 0.2. These variables were assigned scores using a scoring system determined using multivariate Cox regression analysis. The total scores from all variables were adjusted to fall within a scale of 0 to 100. Following the calculation of the overall score, the chances of survival for AML patients at 1, 3, and 5 years were ascertained via tracing the cumulative score axis to the result axis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe nomogram's C-index, adjusted for bootstrap, was 0.728 (95% CI\u0026thinsp;=\u0026thinsp;0.695\u0026ndash;0.768), indicating a moderate level of accuracy in predicting OS in AML patients. The probability of surviving for one year was found to be less than 50%, with the chances of surviving for three and five years being less than 20% and 10%, respectively. The predicted outcomes from the nomogram calibration graph for OS were consistent with the actual outcomes for each patient (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis investigation provides a comprehensive examination of the expression pattern, clinical relevance, functional pathways, and immune associations of LST1 in AML, leveraging multi-omics data alongside experimental validation. The main discoveries can be outlined as follows: 1) Expression Pattern Our research revealed a notable increase in LST1 concentrations in AML in contrast to healthy blood samples, validated through RT-qPCR and Western blot methods.2) Clinical Importance: High LST1 expression was linked to aggressive clinical characteristics like high WBC counts, non-M3 FAB subtype, and intermediate/poor cytogenetic risk, indicating its potential as a predictive tool.3) Predictive Value: Increased LST1 levels were an independent predictor of worse OS in both the entire AML group and various subgroups, highlighting its importance as a prognostic marker in AML.4) Functional Pathways and Immune Connections: LST1 expression was linked to specific gene expression profiles and immune infiltration patterns in the AML environment, suggesting its potential role in AML development and immune response regulation. In conclusion, these results emphasize the diverse functions of LST1 in AML, showcasing its usefulness as a diagnostic and prognostic tool and its potential implications for understanding AML pathophysiology and immune regulation. Further research into the mechanistic underpinnings of LST1 dysregulation in AML is warranted, potentially opening avenues for targeted therapeutic interventions.\u003c/p\u003e \u003cp\u003eThese outcomes underscore the clinical relevance of LST1 as a new predictive biomarker in AML. While previous investigations have elucidated the predictive significance of various molecular markers in AML[\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], including NPM1, FLT3-ITD, CEBPA, and WT1, these markers often possess limitations, primarily restricted to specific AML subtypes and fail to encompass the disease's intricate heterogeneity. In contrast, our investigation demonstrates that LST1 overexpression is a prevalent characteristic across diverse AML subgroups and independently predicts poor prognosis. Notably, LST1 retains its significant prognostic importance even after adjustment for established risk factors encompassing age and cytogenetics, indicating its additional prognostic utility. Furthermore, the integration of LST1 expression into a nomogram model shows promising performance in predicting patient survival. The incorporation of this system could aid in risk stratification and impact treatment decisions for AML.\u003c/p\u003e \u003cp\u003eThe heightened expression of LST1 in AML likely contributes to the disease's aggressive behavior. Acting as a transmembrane protein, LST1 is involved in both inflammatory and immune responses[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].Prior research has shown that LST1 can improve the movement and infiltration of cancer cells, along with the secretion of inflammatory cytokines[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].In the context of AML, elevated LST1 expression may boost the proliferation and survival of leukemic blasts, as well as their interaction with the bone marrow microenvironment. Pathways associated with cytokine signaling, immune regulation, cell adhesion, and oncogenesis were identified through functional enrichment analysis of genes that showed differential expression in relation to LST1. These pathways play critical roles in AML pathogenesis and progression[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Hence, targeting LST1 emerges as a potential therapeutic strategy in AML, with the potential to modulate these vital pathways.\u003c/p\u003e \u003cp\u003eTo get more knowledge into the biological involvements of LST1 in AML, we applied comprehensive analyses of LST1-associated DEGs and their corresponding pathways. GO enrichment analysis unveiled the significant contributions of DEGs associated with LST1 in processes pertinent to the immune system, including cytokine production, regulation of immune response elements, and initiation of the MAPK cascade. These findings are consistent with the established roles of LST1 in immune regulation[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, analysis of routes in the KEGG revealed a high number of pathways related to cancer in DEGs linked to LST1. These pathways encompass cell adhesion molecules, interactions between cytokines and their receptors, and interactions between the extracellular matrix and receptors. Such pathways play crucial roles in mediating the connection between AML cells and the bone marrow microenvironment, thereby promoting leukemia cell survival and resistance to therapeutic agents [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, GSEA unveiled distinct pathway enrichment patterns in AML cases with high and low levels of LST1 expression. Cases exhibiting low levels of LST1 demonstrated an elevation in pathways linked to ECM-receptor interaction and focal adhesion, whereas cases with high levels of LST1 displayed a significant rise in pathways connected with chemokine signaling and Toll-like receptor signaling. These findings suggest that LST1 overexpression may rewire the signaling network in AML cells, leading to heightened inflammatory responses and immune evasion. Importantly, previous studies have documented the activation of chemokine and Toll-like receptor signaling in AML, promoting leukemia cell proliferation, migration, and survival[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The outcomes of our research offer a new understanding of the molecular pathways implicated in the impact of increased LST1 levels on the progression of AML.\u003c/p\u003e \u003cp\u003eThe connection between LST1 levels and immunological cell presence in the AML microenvironment is another important focus of our research. Through ssGSEA analysis, a robust correlation was discovered between increased LST1 concentrations and the existence of various immune cell populations, particularly MDSCs, Tregs, and fatigued T lymphocytes. These immunosuppressive cell subsets have been well-documented to facilitate immune evasion and disease progression in AML[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Conversely, LST1 expression exhibited an adverse connection with the presence of cytotoxic immune cells encompassing natural killer (NK) and CD8\u0026thinsp;+\u0026thinsp;T cells, which possess an essential involvement in combating leukemia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings imply that LST1 overexpression may contribute to shaping an immunosuppressive milieu in AML, thereby promoting leukemia cell survival and evasion from immune surveillance. Therefore, focusing on LST1 or its related immune pathways is seen as a hopeful approach to boost anti-leukemia defenses and enhance treatment results in AML.\u003c/p\u003e \u003cp\u003eHowever, our investigations also have certain limitations that warrant acknowledgment. Firstly, despite analyzing a substantial number of AML cases from public datasets, our validation cohort was relatively small, underscoring the necessity for confirmation of results in larger independent cohorts. Furthermore, despite conducting functional enrichment analyses on genes and pathways linked with LST1, no experimental validation was undertaken. Upcoming research should utilize both in vitro and in vivo models to ascertain the precise involvements of LST1 in the progression and evolution of AML.\u003c/p\u003e \u003cp\u003eIn summary, our investigation unveils the widespread occurrence of LST1 overexpression in AML, serving as an independent predictor of unfavorable outcomes. We explore the relationship between LST1 levels and unique gene expression patterns, as well as immune cell infiltration in the AML environment, which could impact disease advancement and immune system avoidance. The results highlight the possibility of LST1 as a new predictive biomarker and target for treating AML. Further comprehensive exploration, both functionally and clinically, is imperative to delineate the precise role of LST1 in AML pathogenesis and to translate these insights into enhanced patient care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to extend our appreciation to all researchers and institutions for their efforts in making the original datasets employed\u0026nbsp;in this work accessible to the public.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to Information and Resources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data produced and examined can be accessed in the TCGA and GEO repositories through the following URLs: [TCGA](https://portal.gdc.cancer.gov/); [GEO](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this project was provided by The Anhui Health and Medical Research Foundation (Grant No. AHWJ2022c002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of the Anqing Municipal Hospital (ID: No. 2024\u0026ndash;72). All the enrolled patients signed an informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe writers affirm that they possess no conflicting concerns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaitao Xu originated and formulated the research. Dangui Chen, Lihong Wang, and Long Zhong performed the experiments and bioinformatics analyses. Fei Chen and Jia Lu contributed to data acquisition, analysis, and interpretation. Haitao Xu drafted the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBornhauser M, Schliemann C, Schetelig J, Rollig C, Kramer M, Glass B, Platzbecker U, Burchert A, Hanel M, Muller LP, Klein S, Bug G, Beelen D, et al. 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Blood. 2022; 139(20):3040\u0026ndash;3057.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorbecki J, Kupnicka P, Barczak K, Bosiacki M, Zietek P, Chlubek D and Baranowska-Bosiacka I. The Role of CXCR1, CXCR2, CXCR3, CXCR5, and CXCR6 Ligands in Molecular Cancer Processes and Clinical Aspects of Acute Myeloid Leukemia (AML). Cancers (Basel). 2023; 15(18).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaakhlagh S, Kashani B, Zandi Z, Bashash D, Moradkhani M, Nasrollahzadeh A, Yaghmaei M, Mousavi SA and Ghaffari SH. Toll-like receptor 4 signaling pathway is correlated with pathophysiological characteristics of AML patients and its inhibition using TAK-242 suppresses AML cell proliferation. Int Immunopharmacol. 2021; 90:107202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Kahiry WMA, Dammag EAM, Abdelsalam HST, Fadlallah HK and Owais MS. Toll-like receptor 9 negatively related to clinical outcome of AML patients. J Egypt Natl Canc Inst. 2020; 32(1):15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsuchiya H and Shiota G. Immune evasion by cancer stem cells. Regen Ther. 2021; 17:20\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrell PB and Kordasti S. Hostile Takeover: Tregs Expand in IFNgamma-Rich AML Microenvironment. Clinical cancer research: an official journal of the American Association for Cancer Research. 2022; 28(14):2986\u0026ndash;2988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhigarev D, Varshavsky A, MacFarlane AWt, Jayaguru P, Barreyro L, Khoreva M, Dulaimi E, Nejati R, Drenberg C and Campbell KS. Lymphocyte Exhaustion in AML Patients and Impacts of HMA/Venetoclax or Intensive Chemotherapy on Their Biology. Cancers (Basel). 2022; 14(14).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim S and Choi J. Restoring NK cell functions in AML relapse. Blood. 2022; 140(26):2765\u0026ndash;2766.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong F, Yao F, Jiang J, Yu X, Liu J, Huang B and Wang X. CD8\u0026thinsp;+\u0026thinsp;T cell-based molecular subtypes with heterogeneous immune landscapes and clinical significance in acute myeloid leukemia. Inflamm Res. 2024; 73(3):329\u0026ndash;344.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eEvaluation of the Correlation between LST1 Gene Expression and Clinical Characteristics in Acute Myeloid Leukemia (AML) Specimens from the TCGA Database.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003eLow expression of LST1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003eHigh expression of LST1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e31 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e37 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e44 (29.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e39 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e9 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e4 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e64 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e71 (47.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003e\u0026lt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e49 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e39 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e26 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e37 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eWBC count(x10^9/L), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003e\u0026lt;=20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e49 (32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e28 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003e\u0026gt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e25 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e48 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eBM blasts(%), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003e\u0026lt;=20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e28 (18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e32 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003e\u0026gt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e47 (31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e44 (29.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003ePB blasts(%), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003e\u0026lt;=70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e36 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e36 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003e\u0026gt;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e39 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e40 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eCytogenetic risk, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eFavorable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e21 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e10 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e33 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e49 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e21 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e15 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eFAB classifications, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e10 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e5 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e18 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e17 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e22 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e16 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e15 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e4 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e25 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e2 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e13 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e2 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eM7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eCytogenetics, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e29 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e40 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003e+8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e8 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003edel(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003edel(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e4 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e2 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003einv(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e8 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003et(15;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e11 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003et(8;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e7 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003et(9;11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eComplex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e12 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e12 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eFLT3 mutation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e53 (36.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e49 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e18 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e27 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eIDH1 R132 mutation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e64 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e72 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e10 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eIDH1 R140 mutation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e68 (45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e69 (46.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e5 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e7 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eIDH1 R172 mutation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e71 (47.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e76 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e2 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eRAS mutation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e73 (48.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e69 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e7 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eNPM1 mutation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e65 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e52 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e9 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e24 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.73036093418259%\"\u003e\n \u003cp\u003eAge, meidan (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e52 (36.5, 63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.416135881104033%\"\u003e\n \u003cp\u003e60 (48, 68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437367303609342%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Logistic Regression Analysis Investigating the Association between Clinicopathological Factors of AML and the Expression of LST1.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eTotal (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eGender (Male vs. Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e0.685 (0.359 - 1.307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eRace (White vs. Asian\u0026amp;Black or African American)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e1.938 (0.617 - 6.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eAge (\u0026gt; 60 vs. \u0026lt;= 60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e1.835 (0.952 - 3.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eWBC count(x10^9/L) (\u0026gt; 20 vs. \u0026lt;= 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e3.099 (1.588 - 6.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eBM blasts(%) (\u0026gt; 20 vs. \u0026lt;= 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e0.756 (0.392 - 1.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003ePB blasts(%) (\u0026gt; 70 vs. \u0026lt;= 70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e0.948 (0.499 - 1.800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eCytogenetic risk (Intermediate/normal vs. Favorable)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e2.824 (1.175 - 6.787)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eFLT3 mutation (Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e1.452 (0.715 - 2.950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eIDH1 R132 mutation (Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e0.270 (0.071 - 1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eIDH1 R140 mutation (Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e1.400 (0.423 - 4.629)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eIDH1 R172 mutation (Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e0.000 (0.000 - Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eRAS mutation (Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e7.515 (0.901 - 62.679)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eNPM1 mutation (Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.579124579124578%\"\u003e\n \u003cp\u003e2.831 (1.237 - 6.477)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Univariate and Multivariate Cox Regression Analysis of Factors Influencing Overall Survival (OS) in AML Patients.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"638\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.023474178403756%\" rowspan=\"2\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" rowspan=\"2\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003eTotal(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.020344287949925%\" colspan=\"2\" style=\"width: 43.284%;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.99374021909233%\" colspan=\"2\" style=\"width: 27.676%;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.5%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.227272727272727%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.04545454545455%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.227272727272727%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003e\u0026lt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e3.333 (2.164-5.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e2.791 (1.712-4.550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eCytogenetic risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eFavorable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e2.957 (1.498-5.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e2.162(1.079-4.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e4.157 (1.944-8.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e2.993(1.365-6.563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eLST1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e2.038 (1.323-3.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e1.763 (1.063-2.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eFLT3 mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e1.271 (0.801-2.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eNPM1 mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e1.137 (0.706-1.832)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e1.030 (0.674-1.572)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eRAS mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e0.643 (0.235-1.760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eWBC count(x10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003e\u0026lt;=20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003e\u0026gt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e1.161 (0.760-1.772)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003ePB blasts(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003e\u0026lt;=70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003e\u0026gt;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e1.230 (0.806-1.878)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n 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\u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e0.588 (0.238-1.452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eIDH1 R140 mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eIDH1 R172 mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.047021943573668%\" style=\"width: 16.2516%;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" style=\"width: 10.9417%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" style=\"width: 29.2851%;\"\u003e\n \u003cp\u003e0.610 (0.085-4.385)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 14.1598%;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.54858934169279%\" style=\"width: 20.2743%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.501567398119123%\" style=\"width: 7.4017%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"LST1, AML, biomarker, immune infiltration, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4515325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4515325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClinical management of acute myeloid leukemia (AML) poses significant challenges due to its poor prognosis and heterogeneous nature. Discovering new biomarkers is crucial for improving risk assessment and customizing treatment approaches. While leukocyte-specific transcript 1 (LST1) is implicated in inflammation and immune regulation, its function in AML remains ambiguous. In this investigation, we conduct a comprehensive investigation into LST1 expression profiles, clinical implications, functional pathways, and immune interactions in AML, leveraging multi-omics data and experimental validations. Our examination shows increased levels of LST1 expression in AML when compared to regular hematopoietic tissues, a discovery validated by RT-qPCR and Western blot analyses in a separate group. Elevated LST1 levels correlate with distinct clinicopathological features, including increased white blood cell counts, non-M3 FAB subtype, and intermediate/poor cytogenetic risk. Importantly, heightened LST1 levels predict unfavorable overall survival outcomes across various subgroups, independently of age and cytogenetic risk. We develop an integrative nomogram incorporating LST1 expression, demonstrating robust prognostic efficacy for patient survival. Transcriptomic profiling identifies 275 differentially expressed genes between LST1-high and -low AML cases, enriched in cytokine signaling, immune modulation, cell adhesion, and oncogenic pathways. Furthermore, LST1 exhibits significant associations with the infiltration of diverse immune cell subsets within the AML microenvironment, particularly myeloid cells and regulatory T cells (Tregs). In conclusion, our study establishes LST1 as a novel prognostic indicator with immunological relevance in AML, emphasizing its potential therapeutic implications. Further mechanistic elucidation of LST1 in AML pathogenesis is crucial for its clinical translation.\u003c/p\u003e","manuscriptTitle":"LST1 Expression Correlates with Immune Infiltration and Predicts Poor Prognosis in Acute Myeloid Leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 16:15:19","doi":"10.21203/rs.3.rs-4515325/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"034e194e-2361-4efc-84f0-82ec46c45233","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-19T09:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-20 16:15:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4515325","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4515325","identity":"rs-4515325","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}