{"paper_id":"3775b671-5007-46d2-8b23-faf5ec9e29fd","body_text":"Analysis of the differential expression and prognostic relationship of DEGs in AML based on TCGA database | 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 Analysis of the differential expression and prognostic relationship of DEGs in AML based on TCGA database Yue Gao, Yinnong Jia, Zhengmin Yu, Xinyu Ji, Xiaowen Liu, Lei Han, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-120259/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2023 Read the published version in European Journal of Medical Research → Version 2 posted 7 You are reading this latest preprint version Show more versions Abstract Background: Acute myeloid leukemia (AML) is a common and lethal haematological malignant hyperplastic disease originating from hematopoietic stem cells. The purpose of this study is to obtain the key differentially expressed gene (DEG) related to the survival of AML by The Cancer Genome Atlas (TCGA) database and to verify these genes by a clinical follow-up investigation, in order to identify valuable predictive and prognostic biomarkers for early diagnosis of AML and predict the survival rates. Methods: The RNA sequencing (RNA-Seq) data and clinical information of TCGA-LAML were downloaded from the TCGA database. After that we 1) screened the survival related DEGs by Cox regression analysis, 2) selected the cytogenetics risk related DEGs by DESeq2 R package, 3) and filtrated the genes in the top10 pathways of up-regulated and down-regulated of Normalization Enrichment Score (NES) by Gene Set Enrichment Analysis (GSEA). Finally, we focused the intersectional genes of above three parts as the key gene of our present study. The following Multivariate Cox regression analyses were performed to analyze these intersectional genes as independent factors. Proportional hazards assumption was evaluated to test Cox regression model by Schoenfeld residuals. The Kaplan–Meier survival curve and Nomogram were plotted to predict and compare the 1-year, 3-year, and 5-year survival rates of AML patients. The concordance index (C-index) and calibration curve were used to evaluate the prediction performance of nomogram. Results: A total of 151 RNA-Seq samples for 60488 genes and 200 clinical samples were selected from the TCGA database. 1005 survival related DEGs were obtained by Cox regression analyses ( P <0.05, exp (coef)>1), 2291 cytogenetics risk category related DEGs were identified using DESeq2 (log 2 Fold Change>1 and P <0.05), and 790 meaningful pathways of cytogenetics risk category were selected from GSEA (abs(NES)≥1, NOM p-val≤0.05 and FDR q-val≤0.25). Screened a key gene IGHM that is related to patient survival rate. The results showed that the IGHM expression and age displayed statistical associations with the survival time of patients ( P <0.05). The risk of death in the patients with high expression group of IGHM was 2.07 times higher than those in low expression group ( P <0.05). For survival, the 1, 3, and 5-year survival of patients in the high expression group are predicted to be 68%, 43%, and 30%. GSEA analysis revealed that IGHM were mainly enriched in the immune response (BP). Conclusion: High expression of IHGM gene is an independent risk factor for the prognosis of patients with AML and can be an important molecular marker for predicting the prognosis of patients with AML. Acute myeloid leukemia TCGA DEGs Survival Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Leukemia is a malignant disease of the hematopoietic system. Based on disease progression and cell types, leukemia can be classified as acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myelogenous leukemia (AML) and chronic myelogenous leukemia (CML)(1). AML is a group of malignant clonal diseases originating from bone marrow hematopoietic stem cells, characterized by abnormal differentiation of hematopoietic stem cells and excessive proliferation of myeloid progenitor cells(2). The abnormal accumulation of leukemia cells leads to the inhibition of normal hematopoietic cell growth and the infiltration of leukemia cells in the bone marrow, resulting in multiple organ failures and such symptoms as anemia, bleeding and infection(3). The onset of AML can cover all periods of lifetime, but it mostly occurs in the elder-age and male patients, the incidence of which usually increases following the age, with a median of 67 years at diagnosis(4, 5). According to the previous study, approximate 80% of AML occurs in adults (6). At present, with the development of new diagnosis and treatment technologies, the survival rate of AML patients has significantly improved, but the long-term survival rate of patients still remains poor. For patients < 60 years old, according to the related previous studies, the 5-year overall survival (OS) rate is less than 40%; for the majority of patients with AML (aged over 60 years old), the 5-year OS rate is only 10-20% (7, 8). Meanwhile, AML patients mainly have accompanied with poor prognosis. Even though most patients achieve a complete remission (CR) with intensive induction chemotherapy, over half of young adult patients and about 90% of elderly patients still succumb to the disease (9). The culprits of above phenomenon could be regarded as the limited current knowledge of the underlying molecular mechanisms and its progression of AML, and the low-effective early clinical diagnosis. Some studies show that patients with different subtypes, different karyotypes, different gene expressions and mutation types have different prognosis(10). Therefore, finding out effective target genes and prognostic molecules is of great significance to the early diagnosis and prognosis of patients with AML. With the developments of gene detecting and computing sciences, the high-throughput sequencing technologies and bioinformatics analyses have been widely used in clinical researches to screen meaningful oncogene and epigenetic changes. It is helpful to identify the differentially expressed genes (DEGs), functional pathways involved in AML carcinogenesis, and the biomarkers for diagnosis or prognosis. In order to conduct a comprehensive study of the human cancer genome, the United States initiated the establishment of The Cancer Genome Atlas (TCGA) database in 2006. The TCGA is a human cancer gene information database established and collaborated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), owning comprehensive and well-curated genomic data of over 11,000 tumors across 33 major cancer types. This database mainly include the repository containing genomic, transcriptomic, epigenetic, proteomics and clinical information of various kinds of cancer(11). We combined the RNA sequencing (RNA-Seq) data and clinical information of AML patients downloaded from TCGA database to perform our present study. By screen and analyzing the DEGs related to prognosis of AML patients, we tended to identify some novel AML-related genes. For further determination of the association between their aberrant expression levels and prognosis, the nomogram prognostic model was constructed to predict the long-term survival rate of AML patients. Moreover, we enriched DEGs-related signaling pathways in different cytogenetic groups using gene ontology (GO) enrichment analysis. Finally, 87 Chinese adult AML patients and 43 children AML patients were recruited to validate the clinical value for diagnosis of our findings. Materials And Methods Samples and data preprocessing The RNA-Seq data and clinical information of TCGA-LAML were downloaded from the TCGA database (https://portal.gdc.cancer.gov/), and the raw data included 151 RNA-Seq samples for 60,488 genes and 200 clinical samples. Firstly, we filtered the genes with low expression (counts per million (CPM)<1 in all samples), which reduced our RNA-Seq data to 56,822 genes. After excluding Asian race (2 cases), missing clinical information (2 cases), missing of Cytogenetics risk information (3 cases), 193 clinical samples were left. For matching the information from different databases, only 146 samples were selected because of the explicit intersection of RNA-Seq and Clinical data. Considering 9 patients were excluded because of the lack of follow-up information, we selected only 137 samples for the further investigation. On the other hand of genes filtration, RNA-Seq data was reduced to 37,362 genes after removing genes with null expression in over 50% samples. We also excluded the genes with their high or low z-score covering 95% samples, and the RNA-Seq data decreased from 37,297 to 16,226 genes. Finally, we merged RNA-Seq data with clinical information, then obtained 137 samples with 16,226 genes expression profiles for final analysis. Screen differentially expressed genes Cox proportional hazards model was used to evaluate the effects of gene expression level on the survival time of the patients with AML ( P -value < 0.05, exp (coef) >1). The DESeq2 R-package (v3.5.1) was introduced to perform differential gene expression analysis between the low-risk and high-risk cytogenetics groups. The enrolling conditions for the differentially expressed gene were log 2 Fold Change > 1 and adjusted P -value < 0.05. The Gene Set Enrichment Analysis (GSEA) of gene functional was performed by using ClusterProfiler R-package, and the meaningfully enriched pathways were screened with abs (NES) ≥ 1 (abs refers to the absolute value), NOM p-value ≤ 0.05, and FDR q-value ≤ 0.25 as threshold. Venn diagrams were performed by using the limma R-package to determine the intersection of survival related DEGs, cytogenetics risk related DEGs, and the genes in the top 10 pathways of up-regulation. By the result of Venn diagram, we further screened out the key genes of AML as our target for the present investigation. Survival analysis of DEGs Multivariate Cox regression analyses were performed to investigate the independent factors that affect patient prognosis. Proportional hazards assumption was evaluated to test Cox regression model by Schoenfeld residuals, and to analyze the impact of clinical characteristics and IGHM gene expression on the survival time of AML patients. The Kaplan–Meier survival curve and Nomogram were plotted to compare and predict the influence of IGHM on the 1-year, 3-year, and 5-year survival rates of AML patients. The concordance index (C-index) and calibration curve were used to evaluate the prediction performance of nomogram. All statistical tests were two-sided and P < 0.05 was considered to indicate a statistically significant difference. Results Samples clinical characteristics In the present study, a total of 137 AML patients were included in the analysis, including 77 males and 60 females, 126 whites and 11 blacks, with mean age of diagnosis was 54.44 ±15.85 years. Using current SWOG criteria for cytogenetics risk category, the sample can be divided roughly into three categories: 28 patient had favorable cytogenetics, 74 had intermediate-risk/normal cytogenetics, and 35 had poor cytogenetics. 48 cases survival and 89 cases death. The clinical characteristics of included AML patients are shown in Table 1. Identification and analysis of Survival- and cytogenetics- dual related DEGs For survival-related DEG analysis, the data was adjusted for gender (male, female (reference group)), age and race (white, Black or African American (reference group)). After screening by Cox proportional hazard regression analyses ( P -value < 0.05, exp (coef) >1), and 1022 survival related DEGs were obtained. 17 genes were lost by conversion of gene names, and finally 1005 survival-related DEGs were obtained. Considering the cytogenetics risk as an imperative clinical factor in AML , we further re-filtered the survival-related DEGs in different cytogenetics risk in order to find out the target genes which also associated with AML cytogenetics risk. The cytogenetics risk category usually can be divided into two groups: favorable intermediate/normal risk (104 cases) and poor risk (33 cases). Genes were considered up-regulated (down-regulated) if log 2 Fold Change in expression was higher (or lower) than 1 (abscissa), and adjusted P -value < 0.05 (ordinate), a total of 2291 cytogenetics risk category related DEGs were identified using DESeq2 (as shown in Fig 1). Because the large number of survival-related DEGs might cost a huge calculation power to analysis, we performed the functional enrichment analysis to enrich the genes in DESeq2 with high relevancy biological functions. A total of 790 meaningful pathways of cytogenetics risk category were listed by GSEA. The top 10 pathways of up-regulated and down-regulated gene sets of NES were marked and listed in Fig 2. Finally, a Venn diagram was drawn to reflect the intersection of survival related DEGs (Overall Survival), cytogenetics risk related DEGs (CR DESeq2), and functional enrichment analysis (Fig 3). A total of 42 intersecting DEGs were identified in the two sets, and there is only one gene in the CR GSEA top10 pathways of up-regulated among the 42 intersecting DEGs, namely IGHM. Survival analysis of IGHM Multivariate Cox regression analysis was used to analyze the influence of clinical characteristics and IGHM gene expression on the survival time of AML patients. Age, gender, and race were included as covariates, the survival rate of patients with different IGHM amounts of expression was analyzed by Cox proportional hazards model. The model results of hypothesis testing showing P = 0.21 (Fig 4). The Wald test value of the overall Cox regression model is 31.54, and its P -value < 0.001. The average survival time of patients based on all factors was 19 months (95%CI: 12~27 months), which is about 1.5 years. The results of multivariate analysis showed that the two factors, IGHM expression and age, have their statistical significance with the survival time of AML patients, all P -values were < 0.05 (Table 2). After adjusting for other variables, the Kaplan-Meier survival curve for each factor in different conditions is demonstrated in Fig 5. The survival rate of patients in the IGHM high expression group was statistically lower than that in low expression group ( P =0.041). The risk of death in IGHM high expression patients was 2.07 times higher than that in low expression ones ( P <0.05). The mean value 55 years of age was used as a cut-off point because of age is a continuous variable, these variables were converted into categorical variables by the cut function and the survival curve was plotted. Kaplan-Meier survival analysis further showed that the survival rate of old group is lower than that of young group, and the difference was statistically significant ( P <0.001). The risk of death in the patients with old group having 3.00 times the risk compared to young patients ( P <0.001). Establish and evaluate nomogram prognostic model The nomogram, which is also called alignment diagram, the advantage of application in medical research is to estimates the survival of individual patient by incorporating multiple clinical variables and their interdependent relationships(12). Its essence is the visualization of the built model, the closer the C-index of the nomogram is to 1, the better the accuracy of the model. In this study, the C-index of fitting the prediction model of Cox regression is 0.69, this result shows that the model has a good accuracy. The calibration curve is shown in Fig 6, and the predicted calibration curve is closer to the standard curve, it also shows that the prediction ability of nomogram is better. Nomogram results showed that the expression of IGHM in LAML patients has a great influence on their survival time. It is predicted that the 1-year, 3-year and 5-year survival of the patients with low expression group of IGHM are > 85%, > 70% and > 60%, and the 1, 3, and 5-year survival of patients in the high expression group are predicted to be 68%, 43%, and 30%,respectively (in Fig7). Analysis of IGHM -related signaling pathways After focusing IGHM as our target gene, we reviewed the results of GSEA analysis to evaluate the potential function of IGHM (in Table 3). Among the significant top 10 up-regulated pathways with statistical significance (abs (NES) ≥ 1, NOM p-value ≤ 0.05 and FDR q-value ≤ 0.25), IGHM participated in 7 pathways, including humoral immune response by circulating immunoglobulin, complement activation, phagocytosis recognition, B cell mediated immunity, B cell receptor signaling pathway, membrane invagination, and positive regulation of B cell activation. Discussion The present study analyzed the RNA-seq data and clinical information of 146 AML patients from TCGA database through bioinformatics analysis. In this study, we performed multivariate Cox regression analysis on prognostic factors, the findings suggested that age and IGHM expression are independent prognostic factors for patients with AML ( P < 0.05), which means IGHM could be a key gene which is statistically related to the AML patients’ survival. To our knowledge, it was the first study to confirm IGHM as a potential target gene to AML by using bioinformatics analysis, accompanied with further clinical sample verification. Previous studies has shown that IGHM (Immunoglobulin Heavy Constant Mu) is a gene marker for the short transition period in the differentiation and development of myeloid and lymphoid progenitor cells (13, 14). Normally, IGHM maintained in an inactive status, without antibody response. But in both B lymphocytic leukemia and myeloid leukemia, with the process of the cellular malignant transformation and abnormal clone amplification, the expression of IGHM was abnormally highly increased (15). In our study, the high expression of IGHM was identified as an independent risky factor for AML patients’ survival, which was highly consistent with the conclusion that high expressions of IGHM are more common in AML patients (15, 16). For the function of IGHM in coding protein, the IGHM defines the IgM isotype in B cells; aberrant expression of IGHM is closely associated with the misfunction of mu-chain of immunoglobulin, including mutations and rearrangements. A resent research suggests that patients with autosomal recessive agammaglobulinemic have IGHM gene mutations (17). Also, the mu-chain of immunoglobulin gene rearrangement was detected in myeloid leukemia cells from AML patients, and the survival rate of AML patients was significantly lower (13, 14, 16, 18, 19). Neeraj et al. also found that gene rearrangement of IGHM in the diffuse large B cell lymphoma (DLBCL) (20). Therefore, we believed the IGHM could be a functional and reasonable marker to evaluate the prognosis of AML. Our present study also analyses differences between the expression of IGHM in different cytogenetic risk groups, the results show that the expression of IGHM is higher in patients with high-risk group. Unlike most other solid tumors, many hematological malignancies strongly associated with single characteristic cytogenetic abnormalities. A previous study had shown that patients, younger than 65 years treated with standard chemotherapy with a favorable karyotype, have CR rates in the 85% to 90% range and a 5-year OS of 50% to 60% (21). Also, patients with intermediate-risk cytogenetics have CR rates of 65% to 75%, and a 5-year OS of 35% to 45%, while patients with poor cytogenetics have CR rates of 45% to 55% and a 5-year OS of only 10% to 20% (21). Therefore, the problem of AML with unfavorable-risk cytogenetics deserves special attention. The GSEA of our study showed that DEGs mainly enriched in immune response (BP) regulated the occurrence and development of AML and influence the prognosis of AML, which was consistent with the contributes of immune response to tumor progression and drug resistance in various cancers(22, 23), including lung cancer(24), breast cancer(25) and bladder cancer(26). We also found that IGHM genes were primarily enriched in the B cell mediated immunity and the B-cell receptor signaling pathway, these results indicate that the development and prognosis of AML may be related to these biological processes. There are several limitations existing in our study. Although the TCGA database contains information about Asians, blacks, whites and other multi-ethnic groups, it still focuses on whites, and Asians can be included in the later for further verification. Moreover, this study concerns only the TCGA database, and only the RNA-seq data and clinical information, the sample size is small, we hope that future research can combine multiple databases and include data on DNA sequencing, and methylation profiling, exome sequencing, miRNA expression, etc., in order to gain a more comprehensive understanding of AML pathogenesis. Finally, we did not check the status of mu-chain of immunoglobulin in our AML sample to evaluate the relationship between IGHM high expression and the rearrangement, because of the rare bone marrow samples of AML patients. Further investigations are expected to reveal the mechanism of how high IGHM expression leads to poor prognosis of AML. In conclusion, we confirmed that IGHM are independent factor for the prognosis in AML patients. Besides, we also performed a nomogram model for predicting the long-term survival rate of AML patients, patients with high expression of IGHM showed lower survival, and the expression of IGHM is higher in patients with high-risk cytogenetic group. The further molecular biology experiments and clinical studies are needed to verify the possibility of IGHM as a prognostic molecular marker for AML. Declarations Conflicts of Interest The authors declare no competing financial interest about the present study. Acknowledgements This study was financially supported by the National Natural Science Foundation of China (81703201 and 81602431), the Nature Science Foundation of Jiangsu Province (BK20171076), the Jiangsu Provincial Medical Innovation Team (CXTDA2017029), the Jiangsu Provincial Medical Youth Talent (QNRC2016548), the Program of Jiangsu Prevention Medicine Association (Y2018086). References Szczepanski T, van der Velden VH, & van Dongen JJ (2003) Classification systems for acute and chronic leukaemias. Best Pract Res Clin Haematol 16(4):561-582. Tamamyan G , et al. (2017) Frontline treatment of acute myeloid leukemia in adults. Crit Rev Oncol Hematol 110:20-34. Stefanidakis M , et al. (2009) Role of leukemia cell invadosome in extramedullary infiltration. Blood 114(14):3008-3017. Siegel R, Ma J, Zou Z, & Jemal A (2014) Cancer statistics, 2014. CA Cancer J Clin 64(1):9-29. Gyurkocza B , et al. (2010) Nonmyeloablative allogeneic hematopoietic cell transplantation in patients with acute myeloid leukemia. J Clin Oncol 28(17):2859-2867. Estey EH (2013) Acute myeloid leukemia: 2013 update on risk-stratification and management. Am J Hematol 88(4):318-327. Dohner H , et al. (2010) Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood 115(3):453-474. Dohner H, Weisdorf DJ, & Bloomfield CD (2015) Acute Myeloid Leukemia. N Engl J Med 373(12):1136-1152. Ferrara F & Schiffer CA (2013) Acute myeloid leukaemia in adults. Lancet 381(9865):484-495. Takahashi K , et al. (2017) Preleukaemic clonal haemopoiesis and risk of therapy-related myeloid neoplasms: a case-control study. Lancet Oncol 18(1):100-111. Tomczak K, Czerwinska P, & Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemporary oncology 19(1A):A68-77. Balachandran VP, Gonen M, Smith JJ, & DeMatteo RP (2015) Nomograms in oncology: more than meets the eye. Lancet Oncol 16(4):e173-180. Palumbo A, Minowada J, Erikson J, Croce CM, & Rovera G (1984) Lineage infidelity of a human myelogenous leukemia cell line. Blood 64(5):1059-1063. Ha K, Minden M, Hozumi N, & Gelfand EW (1984) Immunoglobulin gene rearrangement in acute myelogenous leukemia. Cancer Res 44(10):4658-4660. Williams L, Moscinski LC, & Medveczky PG (1995) Immunoglobulin germline mu transcripts in acute myelogenous leukemia cells vary in splicing pattern and are heterogeneous. Leukemia 9(12):2016-2022. Guo M, Dong L, & Huang S (1998) [Aberrant expression of immunoglobulin germline gene C mu in leukemias]. Zhonghua Xue Ye Xue Za Zhi 19(7):359-362. Silva P , et al. (2017) Autosomal recessive agammaglobulinemia due to defect in μ heavy chain caused by a novel mutation in the IGHM gene. Genes Immun 18(3):197-199. Dong L, Guo M, Huang SM, Jia SQ, & Wang H (1999) Transcripts of immunoglobulin germline mu: an amplified myeloid and B-lymphoid common gene program in various leukemias. Acta Haematol 101(3):119-123. Zhong L, Chen J, Huang X, Li Y, & Jiang T (2018) Monitoring immunoglobulin heavy chain and T-cell receptor gene rearrangement in cfDNA as minimal residual disease detection for patients with acute myeloid leukemia. Oncol Lett 16(2):2279-2288. Jain N , et al. (2019) Targetable genetic alterations of TCF4 (E2-2) drive immunoglobulin expression in diffuse large B cell lymphoma. Sci Transl Med 11(497). Orozco JJ & Appelbaum FR (2012) Unfavorable, complex, and monosomal karyotypes: the most challenging forms of acute myeloid leukemia. Oncology (Williston Park) 26(8):706-712. Miranda A , et al. (2019) Cancer stemness, intratumoral heterogeneity, and immune response across cancers. Proc Natl Acad Sci U S A 116(18):9020-9029. Astaneh M, Dashti S, & Esfahani ZT (2019) Humoral immune responses against cancer-testis antigens in human malignancies. Hum Antibodies 27(4):237-240. Sorich MJ, Rowland A, Karapetis CS, & Hopkins AM (2019) Evaluation of the Lung Immune Prognostic Index for Prediction of Survival and Response in Patients Treated With Atezolizumab for NSCLC: Pooled Analysis of Clinical Trials. J Thorac Oncol 14(8):1440-1446. Wagner J , et al. (2019) A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer. Cell 177(5):1330-1345 e1318. Flores-Martin JF , et al. (2019) A Combination of Positive Tumor HLA-I and Negative PD-L1 Expression Provides an Immune Rejection Mechanism in Bladder Cancer. Ann Surg Oncol 26(8):2631-2639. Tables Table 1 Clinical characteristics of included AML patients in TCGA-LAML Clinical Character Total（n = 146） Percentage (%) Mean age at diagnosis (mean ± SD) 54.44±15.85 Gender Males 82 56.16 Females 64 43.84 Race Whites 135 Blacks 13 Cytogenetics risk category Favorable 30 20.55 Intermediate/Normal 80 54.79 Poor 36 24.66 Status Survival 53 36.30 Death 93 63.70 SD: standard deviation Table 2. multivariate Cox regression analysis of survival time in LAML patients Factor coef Exp(coef) [95%CI] Se(coef) Z P value IGHM 0.73 2.07 [1.03, 4.15] 0.36 2.04 0.041 Age 0.04 1.04 [1.02, 1.06] 0.01 4.74 <0.001 Sex -0.15 0.86 [0.55, 1.34] 0.23 -0.68 0.499 Race -0.09 0.91 [0.33, 2.55] 0.52 -0.17 0.863 Table 3 Significant top up-regulated pathways participated by IGHM Pathway ES NES NOM p-val FDR q-val size 1.GO_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN 0.75 2.29 <0.001 <0.01 138 2.GO_COMPLEMENT_ACTIVATION 0.71 2.2 <0.001 <0.01 157 3.GO_PHAGOCYTOSIS_RECOGNITION 0.76 2.19 <0.001 <0.01 72 4.GO_B_CELL_MEDIATED_IMMUNITY 0.67 2.1 <0.001 <0.01 206 5.GO_B_CELL_RECEPTOR_SIGNALING_PATHWAY 0.66 1.98 <0.001 <0.01 112 6.GO_MEMBRANE_INVAGINATION 0.64 1.95 <0.001 <0.01 123 7.GO_POSITIVE_REGULATION_OF_B_CELL_ACTIVATION 0.64 1.94 <0.001 <0.01 131 NES: normalized enrichment score; NOM: nominal; FDR: false discovery rate. Gene sets with abs (NES) ≥ 1, NOM p-val ≤ 0.05 and FDR q-val ≤ 0.25 were considered as significant. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2023 Read the published version in European Journal of Medical Research → Version 2 posted Editorial decision: Major revision 03 Feb, 2023 Reviews received at journal 27 Nov, 2022 Reviewers agreed at journal 13 Nov, 2022 Reviewers invited by journal 02 Jul, 2022 Editor assigned by journal 28 Jun, 2022 Submission checks completed at journal 28 Jun, 2022 First submitted to journal 26 Jun, 2022 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-120259\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[{\"code\":1,\"date\":\"2020-12-04 23:05:25\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research 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Prevention\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hengdong\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":120354459,\"identity\":\"3d5ed572-f511-4fa3-8813-532349ffae8b\",\"order_by\":7,\"name\":\"Baoli Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Nanjing Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Baoli\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":120354460,\"identity\":\"cf7afd0e-0737-4d3b-92fa-e79625feb2a0\",\"order_by\":8,\"name\":\"Ming Xu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYLCCBwwMcmzszQdI0JLAwGDMx3MsgTQtifMkchSIUy3f3v7wQULF4fQ2hhwGhh8V2whrMThzxtgg4czh3DaGswcYe87cJkKLRA6bRGIbUAtjXwIzYxsRWuRnpD8DaUlnY+YxIE4Lw40EM5CWBDY2YrVA/ZJu2MbDlnCQKL+AQ+xDhbW8/PzHBx/8qCDGYcjgAInqR8EoGAWjYBTgAgC9ejqqxr3AYgAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Jiangsu Provincial Center for Disease Control and Prevention\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ming\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2020-12-02 12:43:22\",\"currentVersionCode\":2,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-120259/v2\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-120259/v2\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s40001-023-01060-3\",\"type\":\"published\",\"date\":\"2023-02-27T19:29:04+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":23859996,\"identity\":\"b87ba4c3-7c51-4e62-a419-f9d990063fc1\",\"added_by\":\"auto\",\"created_at\":\"2022-07-14 14:55:25\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":85322,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVolcano plots of category related DEGs among LAML\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003eAbscissa: variation in gene expression between different samples\\u003c/p\\u003e\\u003cp\\u003eOrdinate: the significance cytogenetics risk of the expression differences\\u003c/p\\u003e\\u003cp\\u003eRed dots represent upregulated genes (log2 Fold Change\\u0026gt;1, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt;0.05)\\u003c/p\\u003e\\u003cp\\u003eGreen dots show downregulated genes (log2 Fold Change\\u0026lt;1, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt;0.05)\\u003c/p\\u003e\\u003cp\\u003eBlack dots are genes with no significant difference\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-120259/v2/552002118c43e18c8d545517.png\"},{\"id\":23860302,\"identity\":\"93b7405e-d964-4bf2-a8c4-a0ae7c462b28\",\"added_by\":\"auto\",\"created_at\":\"2022-07-14 15:00:25\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":86621,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTop10 significant pathways of cytogenetics risk category among LAML by GSEA\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-120259/v2/c1a5a769ae1366e5e1e25923.png\"},{\"id\":23859999,\"identity\":\"b6c4f37b-f678-476c-bec9-14cd3f0c39c5\",\"added_by\":\"auto\",\"created_at\":\"2022-07-14 14:55:25\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":43979,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVenn diagram\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003eCR GSEA Top10 up-regulated: Top10 significant pathways gene-sets of cytogenetics risk category among LAML by GSEA \\tOverall Survival: gene sets whose expression were significant associated with survival rate\\u003c/p\\u003e\\u003cp\\u003eCR DESeq2: gene sets whose expression were significant associated with cytogenetics risk category\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-120259/v2/b497294773a6151b2cfa41d9.png\"},{\"id\":23860303,\"identity\":\"dd1c61b1-d4f9-443d-ad6a-4e74c3bf096c\",\"added_by\":\"auto\",\"created_at\":\"2022-07-14 15:00:25\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":139576,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePH test for Cox model\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-120259/v2/8aaf2852716b41831d66d983.png\"},{\"id\":23859997,\"identity\":\"50d42562-d207-432c-9ba8-40c3eb01e2a7\",\"added_by\":\"auto\",\"created_at\":\"2022-07-14 14:55:25\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":87428,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAdjusted survival curves for LAML survival\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan class=\\\"ql-cursor\\\"\\u003e\\u003c/span\\u003e*: Reference group\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-120259/v2/075d422034d529f3ad793de8.png\"},{\"id\":23860440,\"identity\":\"d86ece61-3da8-4979-8ac2-298cb856257f\",\"added_by\":\"auto\",\"created_at\":\"2022-07-14 15:05:25\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":31690,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCalibration curve\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-120259/v2/a9d856e2037729e607d33767.png\"},{\"id\":23860002,\"identity\":\"655c7597-af17-4e9c-9da0-843944c45db8\",\"added_by\":\"auto\",\"created_at\":\"2022-07-14 14:55:25\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":64594,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eNomogram fitting the cox proportional risk model\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-120259/v2/cb0235ff4b63f40083f216db.png\"},{\"id\":44721432,\"identity\":\"7d34aaa4-48ca-46bc-a6e9-d125a3bce1d2\",\"added_by\":\"auto\",\"created_at\":\"2023-10-16 19:33:59\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":896260,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-120259/v2/bc783d10-acdc-46cc-8d7c-97fcc7dfa244.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Analysis of the differential expression and prognostic relationship of DEGs in AML based on TCGA database\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eLeukemia is a malignant disease of the hematopoietic system. Based on disease progression and cell types, leukemia can be classified as acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myelogenous leukemia (AML) and chronic myelogenous leukemia (CML)(1). AML is a group of malignant clonal diseases originating from bone marrow hematopoietic stem cells,\\u0026nbsp;characterized by abnormal differentiation of hematopoietic stem cells and excessive proliferation of myeloid progenitor cells(2). The abnormal accumulation of leukemia cells leads to the inhibition of normal hematopoietic cell growth and the infiltration of leukemia cells in the bone marrow, resulting in multiple organ failures and such symptoms as anemia, bleeding and infection(3). The onset of AML can cover all periods of lifetime, but it mostly occurs in the elder-age and male patients, the incidence of which usually increases following the age, with a median of 67 years at diagnosis(4, 5). According to the previous study, approximate 80% of AML occurs in adults\\u0026nbsp;(6).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAt present, with the development of new diagnosis and treatment technologies, the survival rate of AML patients has significantly improved, but the long-term survival rate of patients still remains poor. For patients \\u0026lt; 60 years old, according to the related previous studies, the 5-year overall survival (OS) rate is less than 40%; for the majority of patients with AML (aged over 60 years old), the 5-year OS rate is only 10-20%\\u0026nbsp;(7, 8). \\u0026nbsp;Meanwhile, AML patients mainly have accompanied with poor prognosis. Even though most patients achieve a complete remission (CR) with intensive induction chemotherapy, over half of young adult patients and about 90% of elderly patients still succumb to the disease\\u0026nbsp;(9). The culprits of above phenomenon could be regarded as the limited current knowledge of the underlying molecular mechanisms and its progression of AML, and the low-effective early clinical diagnosis. Some studies show that patients with different subtypes, different karyotypes, different gene expressions and mutation types have different prognosis(10).\\u0026nbsp;Therefore, finding out effective target genes and prognostic molecules is of great significance to the early diagnosis and prognosis of patients with AML.\\u003c/p\\u003e\\n\\u003cp\\u003eWith the developments of gene detecting and computing sciences, the high-throughput sequencing\\u0026nbsp;technologies and bioinformatics analyses have been widely used in clinical researches to screen meaningful oncogene and epigenetic changes. It is helpful to identify the differentially expressed genes (DEGs), functional pathways involved in AML carcinogenesis, and the biomarkers for diagnosis or prognosis. In order to conduct a comprehensive study of the human cancer genome, the United States initiated the establishment of The Cancer Genome Atlas (TCGA)\\u0026nbsp;database in 2006.\\u0026nbsp;The TCGA is a human cancer gene information database established and collaborated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), owning comprehensive and well-curated genomic data of over 11,000 tumors across 33 major cancer types. This database mainly include the repository containing genomic, transcriptomic, epigenetic, proteomics and clinical information of various kinds of cancer(11).\\u003c/p\\u003e\\n\\u003cp\\u003eWe combined the RNA sequencing (RNA-Seq) data and clinical information of AML patients downloaded from TCGA database to perform our present study. By screen and analyzing the DEGs related to prognosis of AML patients, we tended to identify some novel AML-related genes. For further determination of the association between their aberrant expression levels and prognosis, the nomogram prognostic model was constructed to predict the long-term survival rate of AML patients. Moreover, we enriched DEGs-related signaling pathways in different cytogenetic groups using gene ontology (GO) enrichment analysis. Finally, 87 Chinese adult AML patients and 43 children AML patients were recruited to validate the clinical value for diagnosis of our findings.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Materials And Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSamples and data preprocessing\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe RNA-Seq data and clinical information of TCGA-LAML were downloaded from the TCGA database (https://portal.gdc.cancer.gov/), and the raw data included 151 RNA-Seq samples for 60,488 genes and 200 clinical samples. Firstly, we filtered the genes with low expression (counts per million (CPM)\\u0026lt;1 in all samples), which reduced our RNA-Seq data to 56,822 genes. After excluding Asian race (2 cases), missing clinical information (2 cases), missing of Cytogenetics risk information (3 cases), 193 clinical samples were left. For matching the information from different databases, only 146 samples were selected because of the explicit intersection of RNA-Seq and Clinical data. Considering 9 patients were excluded because of the lack of follow-up information, we selected only 137 samples for the further investigation. On the other hand of genes filtration, RNA-Seq data was reduced to 37,362 genes after removing genes with null expression in over 50% samples. We also excluded the genes with their high or low z-score covering 95% samples, and the RNA-Seq data decreased from 37,297 to 16,226 genes. Finally, we merged RNA-Seq data with clinical information, then obtained 137 samples with 16,226 genes expression profiles for final analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eScreen differentially expressed genes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCox proportional hazards model was used to evaluate the effects of gene expression level on the survival time of the patients with AML (\\u003cem\\u003eP\\u003c/em\\u003e-value \\u0026lt; 0.05, exp (coef) \\u0026gt;1). The DESeq2 R-package (v3.5.1) was introduced to perform differential gene expression analysis between the low-risk and high-risk cytogenetics groups. The enrolling conditions for the differentially expressed gene were log\\u003csub\\u003e2\\u003c/sub\\u003e Fold Change \\u0026gt; 1 and adjusted \\u003cem\\u003eP\\u003c/em\\u003e-value \\u0026lt; 0.05. The Gene Set Enrichment Analysis (GSEA) of gene functional was performed by using ClusterProfiler R-package, and the meaningfully enriched pathways were screened with\\u0026nbsp;abs (NES) \\u0026ge; 1 (abs refers to the absolute value), NOM p-value \\u0026le; 0.05, and FDR q-value \\u0026le; 0.25 as threshold.\\u0026nbsp;Venn diagrams were performed by using the limma R-package to determine the intersection of\\u0026nbsp;survival related DEGs, cytogenetics risk related DEGs, and the genes in the top 10 pathways of up-regulation. By the result of Venn diagram, we further screened out the key genes of AML as our target for the present investigation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSurvival analysis of DEGs\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMultivariate Cox regression analyses were performed to investigate the independent factors that affect patient prognosis. Proportional hazards assumption was evaluated to test Cox regression model by Schoenfeld residuals, and to analyze the impact of clinical characteristics and IGHM gene expression on the survival time of AML patients. The\\u0026nbsp;Kaplan\\u0026ndash;Meier\\u0026nbsp;survival curve and Nomogram were plotted to compare and predict the influence of IGHM on the 1-year, 3-year, and 5-year survival rates of AML patients.\\u0026nbsp;The concordance index\\u0026nbsp;(C-index)\\u0026nbsp;and calibration curve were used to evaluate the prediction performance of nomogram. All statistical tests were two-sided and\\u003cem\\u003e\\u0026nbsp;P\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.05 was considered to indicate a statistically significant difference.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSamples clinical characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn the present study, a total of 137 AML patients were included in the analysis, including 77 males and 60 females, 126 whites and 11 blacks, with mean age of diagnosis was 54.44 \\u0026plusmn;15.85\\u0026thinsp;years. Using current SWOG criteria for cytogenetics risk category, the sample can be divided roughly into three categories: 28 patient had favorable cytogenetics, 74 had intermediate-risk/normal cytogenetics, and 35 had poor cytogenetics.\\u0026nbsp;48 cases survival and 89 cases death. The clinical characteristics of included AML patients are shown\\u0026nbsp;in Table 1.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIdentification and analysis\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;of Survival- and cytogenetics- dual related DEGs\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFor survival-related DEG analysis, the data was adjusted for gender (male, female (reference group)), age and race (white, Black or African American (reference group)). After screening by Cox proportional hazard regression analyses (\\u003cem\\u003eP\\u003c/em\\u003e-value \\u0026lt; 0.05, exp (coef) \\u0026gt;1), and 1022 survival related DEGs were obtained. 17 genes were lost by conversion of gene names, and finally 1005 survival-related DEGs were obtained.\\u003c/p\\u003e\\n\\u003cp\\u003eConsidering the cytogenetics risk as an imperative clinical factor in AML\\u003cstrong\\u003e,\\u003c/strong\\u003e we further re-filtered the survival-related DEGs in different cytogenetics risk in order to find out the target genes which also associated with AML cytogenetics risk. The cytogenetics risk category usually can be divided into two groups: favorable intermediate/normal risk (104 cases) and poor risk (33 cases). Genes were considered up-regulated (down-regulated) if log\\u003csub\\u003e2\\u003c/sub\\u003e Fold Change in expression was higher (or lower) than 1 (abscissa), and adjusted \\u003cem\\u003eP\\u003c/em\\u003e-value \\u0026lt; 0.05 (ordinate), a total of 2291 cytogenetics risk category related DEGs\\u0026nbsp;were identified using DESeq2 (as shown in Fig 1).\\u003c/p\\u003e\\n\\u003cp\\u003eBecause the large number of survival-related DEGs might cost a huge calculation power to analysis, we performed the functional\\u0026nbsp;enrichment analysis to enrich the genes in DESeq2 with high relevancy biological functions. A total of 790 meaningful pathways of cytogenetics risk category were listed by GSEA. The top 10 pathways of up-regulated and down-regulated gene sets of NES were marked and listed in Fig 2.\\u003c/p\\u003e\\n\\u003cp\\u003eFinally, a Venn diagram was drawn to reflect the intersection of survival related DEGs (Overall Survival), cytogenetics risk related DEGs (CR DESeq2), and functional enrichment analysis (Fig 3). A total of 42 intersecting DEGs were identified in the two sets, and there is only one gene in the\\u0026nbsp;CR GSEA\\u0026nbsp;top10 pathways of up-regulated among the 42 intersecting DEGs, namely IGHM.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSurvival analysis of IGHM\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMultivariate Cox regression analysis was used to analyze the influence of clinical characteristics and IGHM gene expression on the survival time of AML patients. Age, gender, and race were included as covariates, the survival rate of patients with different IGHM amounts of expression was analyzed by Cox proportional hazards model. The model results of hypothesis testing showing\\u0026nbsp;\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003e= 0.21 (Fig 4). The Wald test value of the overall Cox regression model is 31.54, and its \\u003cem\\u003eP\\u003c/em\\u003e-value \\u0026lt; 0.001. The average survival time of patients based on all factors was 19 months (95%CI: 12~27 months), which is about 1.5 years. The results of multivariate analysis showed that the two factors, IGHM expression and age, have their statistical significance with the survival time of AML patients,\\u0026nbsp;all \\u003cem\\u003eP\\u003c/em\\u003e-values were \\u0026lt; 0.05 (Table 2).\\u003c/p\\u003e\\n\\u003cp\\u003eAfter adjusting for other variables, the\\u0026nbsp;Kaplan-Meier\\u0026nbsp;survival curve for each factor in different conditions is demonstrated in Fig 5. The survival rate of patients in the IGHM high expression group was statistically lower than that in low expression group (\\u003cem\\u003eP\\u003c/em\\u003e =0.041). The risk of death in IGHM high expression patients was 2.07 times higher than that in low expression ones (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt;0.05). The mean value 55 years of age was used as a cut-off point because of age is a continuous variable, these variables were converted into categorical variables by the cut function and the survival curve was plotted.\\u0026nbsp;Kaplan-Meier survival analysis further showed\\u0026nbsp;that the\\u0026nbsp;survival rate\\u0026nbsp;of old group is lower than that of young group,\\u0026nbsp;and the difference was statistically significant (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt;0.001).\\u0026nbsp;The risk of death in the patients with old group having 3.00 times the risk compared to young patients (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt;0.001).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEstablish and evaluate nomogram prognostic model\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe nomogram, which is also called alignment diagram, the advantage of application in medical research is to\\u0026nbsp;estimates the survival of individual patient by incorporating multiple clinical variables and their interdependent relationships(12). Its essence is the visualization of the built model, the closer the C-index of the nomogram is to 1, the better the accuracy of the model. In this study, the C-index of fitting the prediction model of Cox regression is 0.69, this result shows that the model has a good accuracy.\\u0026nbsp;The calibration curve is shown in Fig 6, and the predicted calibration curve is closer to the standard curve,\\u0026nbsp;it also shows that the prediction ability of nomogram is better.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNomogram results showed that the expression of IGHM in LAML patients has a great influence on their survival time. It is predicted that the 1-year, 3-year and 5-year survival of the patients with low expression group of IGHM are \\u0026gt; 85%, \\u0026gt; 70% and \\u0026gt; 60%, and the 1, 3, and 5-year survival of patients in the high expression group are predicted to be 68%, 43%, and 30%,respectively (in Fig7).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp skip=\\\"true\\\"\\u003e\\u003cstrong\\u003eAnalysis of\\u003c/strong\\u003e\\u003cstrong\\u003e \\u003c/strong\\u003e\\u003cstrong\\u003eIGHM\\u003c/strong\\u003e\\u003cstrong\\u003e-related signaling pathways\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAfter focusing IGHM as our target gene, we reviewed the results of GSEA analysis to evaluate the potential function of IGHM (in Table 3). Among the significant top 10 up-regulated pathways with statistical significance (abs (NES) \\u0026ge; 1, NOM p-value \\u0026le; 0.05 and FDR q-value \\u0026le; 0.25), IGHM participated in 7 pathways, including humoral immune response by circulating immunoglobulin, complement activation, phagocytosis recognition, B cell mediated immunity, B cell receptor signaling pathway, membrane invagination, and positive regulation of B cell activation.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe present study analyzed the RNA-seq data and clinical information of 146 AML patients from TCGA database through bioinformatics analysis.\\u0026nbsp;In this study,\\u0026nbsp;we performed multivariate Cox regression analysis on prognostic factors, the findings suggested that age and IGHM expression are independent prognostic factors for patients with AML (\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.05), which means\\u0026nbsp;IGHM could be a key gene which is statistically related to the AML patients\\u0026rsquo; survival.\\u0026nbsp;To our knowledge, it was the first study to confirm IGHM as a potential target gene to AML by using bioinformatics analysis, accompanied with further clinical sample verification.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003ePrevious studies has shown that IGHM (Immunoglobulin Heavy Constant Mu) is a gene marker for the short transition period in the differentiation and development of myeloid and lymphoid progenitor cells\\u0026nbsp;(13, 14). Normally, IGHM maintained in an inactive status, without antibody response. But in\\u0026nbsp;both B lymphocytic leukemia and myeloid leukemia, with the process of the cellular malignant transformation and abnormal clone amplification, the expression of IGHM was abnormally highly increased\\u0026nbsp;(15). In our study, the high expression of IGHM was identified as an independent risky factor for AML patients\\u0026rsquo; survival, which was highly consistent with the conclusion that\\u0026nbsp;high expressions of IGHM are more common in AML patients\\u0026nbsp;(15, 16).\\u0026nbsp;For the function of IGHM in\\u0026nbsp;coding protein, the IGHM defines the IgM isotype in B cells; aberrant expression of IGHM is closely associated with the misfunction of mu-chain of immunoglobulin, including mutations and rearrangements. A resent research suggests that patients with autosomal recessive agammaglobulinemic have IGHM\\u0026nbsp;gene mutations\\u0026nbsp;(17). Also,\\u0026nbsp;the mu-chain of immunoglobulin gene rearrangement was detected in myeloid leukemia cells from AML patients,\\u0026nbsp;and the survival rate of AML patients was significantly lower\\u0026nbsp;(13, 14, 16, 18, 19).\\u0026nbsp;Neeraj et al.\\u0026nbsp;also\\u0026nbsp;found that gene rearrangement\\u0026nbsp;of IGHM\\u0026nbsp;in the\\u0026nbsp;diffuse large B cell lymphoma (DLBCL)\\u0026nbsp;(20). Therefore, we believed the IGHM could be a functional and reasonable marker to evaluate the prognosis of AML.\\u003c/p\\u003e\\n\\u003cp\\u003eOur present study also analyses differences between the expression of IGHM in different cytogenetic risk groups,\\u0026nbsp;the\\u0026nbsp;results show that the expression of IGHM is higher in patients with high-risk group.\\u0026nbsp;Unlike most other solid tumors, many hematological malignancies strongly associated with single characteristic cytogenetic abnormalities. A previous study had shown that patients, younger than 65 years treated with standard chemotherapy with a favorable karyotype, have CR rates in the 85% to 90% range and a 5-year OS of 50% to 60%\\u0026nbsp;(21). Also, patients with intermediate-risk cytogenetics have CR rates of 65% to 75%, and a 5-year OS of 35% to 45%, while patients with poor cytogenetics have CR rates of 45% to 55% and a 5-year OS of only 10% to 20%\\u0026nbsp;(21). Therefore, the problem of AML with unfavorable-risk cytogenetics deserves special attention.\\u0026nbsp;The GSEA of our study showed that DEGs mainly enriched in immune response (BP) regulated the occurrence and development of AML and influence the prognosis of AML, which was consistent with the contributes of immune response to tumor progression and drug resistance in various cancers(22, 23), including lung cancer(24), breast cancer(25)\\u0026nbsp;and bladder cancer(26). We also found that IGHM genes were primarily enriched in the B cell mediated immunity and the B-cell receptor signaling pathway, these results indicate that the development and prognosis of AML may be related to these biological processes.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThere are\\u0026nbsp;several limitations existing in our study.\\u0026nbsp;Although the TCGA database contains\\u0026nbsp;information about Asians, blacks, whites and other multi-ethnic groups, it still focuses on whites, and Asians can be included in the later for further verification. Moreover, this study concerns only the TCGA database, and only the RNA-seq data and clinical information, the sample size is small,\\u0026nbsp;we hope that future research can combine multiple databases and include data on DNA sequencing, and methylation profiling, exome sequencing, miRNA expression, etc., in order to gain a more comprehensive understanding of AML pathogenesis. Finally, we did not check the status of\\u0026nbsp;mu-chain of immunoglobulin in our AML sample to evaluate the relationship between IGHM high expression and the rearrangement, because of the rare bone marrow samples of AML patients. Further investigations are expected to\\u0026nbsp;reveal the mechanism of how high IGHM expression leads to poor prognosis of AML.\\u003c/p\\u003e\\n\\u003cp\\u003eIn conclusion, we confirmed that IGHM are independent factor for the prognosis in AML patients. Besides, we also performed a nomogram model for predicting the long-term survival rate of AML patients, patients with high expression of IGHM showed lower survival, and the expression of IGHM is higher in patients with high-risk cytogenetic group. The further molecular biology experiments and clinical studies are needed to verify the possibility of IGHM as a prognostic molecular marker for AML.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eConflicts of Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing financial interest about the present study.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was financially supported by the National Natural Science Foundation of China (81703201 and 81602431), the Nature Science Foundation of Jiangsu Province (BK20171076), the Jiangsu Provincial Medical Innovation Team (CXTDA2017029), the Jiangsu Provincial Medical Youth Talent (QNRC2016548), the Program of Jiangsu Prevention Medicine Association (Y2018086).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSzczepanski T, van der Velden VH, \\u0026amp; van Dongen JJ (2003) Classification systems for acute and chronic leukaemias. \\u003cem\\u003eBest Pract Res Clin Haematol\\u003c/em\\u003e 16(4):561-582.\\u003c/li\\u003e\\n\\u003cli\\u003eTamamyan G\\u003cem\\u003e, et al.\\u003c/em\\u003e (2017) Frontline treatment of acute myeloid leukemia in adults. \\u003cem\\u003eCrit Rev Oncol Hematol\\u003c/em\\u003e 110:20-34.\\u003c/li\\u003e\\n\\u003cli\\u003eStefanidakis M\\u003cem\\u003e, et al.\\u003c/em\\u003e (2009) Role of leukemia cell invadosome in extramedullary infiltration. \\u003cem\\u003eBlood\\u003c/em\\u003e 114(14):3008-3017.\\u003c/li\\u003e\\n\\u003cli\\u003eSiegel R, Ma J, Zou Z, \\u0026amp; Jemal A (2014) Cancer statistics, 2014. \\u003cem\\u003eCA Cancer J Clin\\u003c/em\\u003e 64(1):9-29.\\u003c/li\\u003e\\n\\u003cli\\u003eGyurkocza B\\u003cem\\u003e, et al.\\u003c/em\\u003e (2010) Nonmyeloablative allogeneic hematopoietic cell transplantation in patients with acute myeloid leukemia. \\u003cem\\u003eJ Clin Oncol\\u003c/em\\u003e 28(17):2859-2867.\\u003c/li\\u003e\\n\\u003cli\\u003eEstey EH (2013) Acute myeloid leukemia: 2013 update on risk-stratification and management. \\u003cem\\u003eAm J Hematol\\u003c/em\\u003e 88(4):318-327.\\u003c/li\\u003e\\n\\u003cli\\u003eDohner H\\u003cem\\u003e, et al.\\u003c/em\\u003e (2010) Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. \\u003cem\\u003eBlood\\u003c/em\\u003e 115(3):453-474.\\u003c/li\\u003e\\n\\u003cli\\u003eDohner H, Weisdorf DJ, \\u0026amp; Bloomfield CD (2015) Acute Myeloid Leukemia. \\u003cem\\u003eN Engl J Med\\u003c/em\\u003e 373(12):1136-1152.\\u003c/li\\u003e\\n\\u003cli\\u003eFerrara F \\u0026amp; Schiffer CA (2013) Acute myeloid leukaemia in adults. \\u003cem\\u003eLancet\\u003c/em\\u003e 381(9865):484-495.\\u003c/li\\u003e\\n\\u003cli\\u003eTakahashi K\\u003cem\\u003e, et al.\\u003c/em\\u003e (2017) Preleukaemic clonal haemopoiesis and risk of therapy-related myeloid neoplasms: a case-control study. \\u003cem\\u003eLancet Oncol\\u003c/em\\u003e 18(1):100-111.\\u003c/li\\u003e\\n\\u003cli\\u003eTomczak K, Czerwinska P, \\u0026amp; Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. \\u003cem\\u003eContemporary oncology\\u003c/em\\u003e 19(1A):A68-77.\\u003c/li\\u003e\\n\\u003cli\\u003eBalachandran VP, Gonen M, Smith JJ, \\u0026amp; DeMatteo RP (2015) Nomograms in oncology: more than meets the eye. \\u003cem\\u003eLancet Oncol\\u003c/em\\u003e 16(4):e173-180.\\u003c/li\\u003e\\n\\u003cli\\u003ePalumbo A, Minowada J, Erikson J, Croce CM, \\u0026amp; Rovera G (1984) Lineage infidelity of a human myelogenous leukemia cell line. \\u003cem\\u003eBlood\\u003c/em\\u003e 64(5):1059-1063.\\u003c/li\\u003e\\n\\u003cli\\u003eHa K, Minden M, Hozumi N, \\u0026amp; Gelfand EW (1984) Immunoglobulin gene rearrangement in acute myelogenous leukemia. \\u003cem\\u003eCancer Res\\u003c/em\\u003e 44(10):4658-4660.\\u003c/li\\u003e\\n\\u003cli\\u003eWilliams L, Moscinski LC, \\u0026amp; Medveczky PG (1995) Immunoglobulin germline mu transcripts in acute myelogenous leukemia cells vary in splicing pattern and are heterogeneous. \\u003cem\\u003eLeukemia\\u003c/em\\u003e 9(12):2016-2022.\\u003c/li\\u003e\\n\\u003cli\\u003eGuo M, Dong L, \\u0026amp; Huang S (1998) [Aberrant expression of immunoglobulin germline gene C mu in leukemias]. \\u003cem\\u003eZhonghua Xue Ye Xue Za Zhi\\u003c/em\\u003e 19(7):359-362.\\u003c/li\\u003e\\n\\u003cli\\u003eSilva P\\u003cem\\u003e, et al.\\u003c/em\\u003e (2017) Autosomal recessive agammaglobulinemia due to defect in \\u0026mu; heavy chain caused by a novel mutation in the IGHM gene. \\u003cem\\u003eGenes Immun\\u003c/em\\u003e 18(3):197-199.\\u003c/li\\u003e\\n\\u003cli\\u003eDong L, Guo M, Huang SM, Jia SQ, \\u0026amp; Wang H (1999) Transcripts of immunoglobulin germline mu: an amplified myeloid and B-lymphoid common gene program in various leukemias. \\u003cem\\u003eActa Haematol\\u003c/em\\u003e 101(3):119-123.\\u003c/li\\u003e\\n\\u003cli\\u003eZhong L, Chen J, Huang X, Li Y, \\u0026amp; Jiang T (2018) Monitoring immunoglobulin heavy chain and T-cell receptor gene rearrangement in cfDNA as minimal residual disease detection for patients with acute myeloid leukemia. \\u003cem\\u003eOncol Lett\\u003c/em\\u003e 16(2):2279-2288.\\u003c/li\\u003e\\n\\u003cli\\u003eJain N\\u003cem\\u003e, et al.\\u003c/em\\u003e (2019) Targetable genetic alterations of TCF4 (E2-2) drive immunoglobulin expression in diffuse large B cell lymphoma. \\u003cem\\u003eSci Transl Med\\u003c/em\\u003e 11(497).\\u003c/li\\u003e\\n\\u003cli\\u003eOrozco JJ \\u0026amp; Appelbaum FR (2012) Unfavorable, complex, and monosomal karyotypes: the most challenging forms of acute myeloid leukemia. \\u003cem\\u003eOncology (Williston Park)\\u003c/em\\u003e 26(8):706-712.\\u003c/li\\u003e\\n\\u003cli\\u003eMiranda A\\u003cem\\u003e, et al.\\u003c/em\\u003e (2019) Cancer stemness, intratumoral heterogeneity, and immune response across cancers. \\u003cem\\u003eProc Natl Acad Sci U S A\\u003c/em\\u003e 116(18):9020-9029.\\u003c/li\\u003e\\n\\u003cli\\u003eAstaneh M, Dashti S, \\u0026amp; Esfahani ZT (2019) Humoral immune responses against cancer-testis antigens in human malignancies. \\u003cem\\u003eHum Antibodies\\u003c/em\\u003e 27(4):237-240.\\u003c/li\\u003e\\n\\u003cli\\u003eSorich MJ, Rowland A, Karapetis CS, \\u0026amp; Hopkins AM (2019) Evaluation of the Lung Immune Prognostic Index for Prediction of Survival and Response in Patients Treated With Atezolizumab for NSCLC: Pooled Analysis of Clinical Trials. \\u003cem\\u003eJ Thorac Oncol\\u003c/em\\u003e 14(8):1440-1446.\\u003c/li\\u003e\\n\\u003cli\\u003eWagner J\\u003cem\\u003e, et al.\\u003c/em\\u003e (2019) A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer. \\u003cem\\u003eCell\\u003c/em\\u003e 177(5):1330-1345 e1318.\\u003c/li\\u003e\\n\\u003cli\\u003eFlores-Martin JF\\u003cem\\u003e, et al.\\u003c/em\\u003e (2019) A Combination of Positive Tumor HLA-I and Negative PD-L1 Expression Provides an Immune Rejection Mechanism in Bladder Cancer. \\u003cem\\u003eAnn Surg Oncol\\u003c/em\\u003e 26(8):2631-2639.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable 1 Clinical characteristics of included AML patients in TCGA-LAML\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" width=\\\"57.942238267148014%\\\"\\u003e\\n \\u003cp\\u003eClinical Character\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003eTotal（n = 146）\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003ePercentage (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003eMean age at diagnosis (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e54.44\\u0026plusmn;15.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003eGender\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003eMales\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e56.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003eFemales\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e43.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003eRace\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003eWhites\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e135\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003eBlacks\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003eCytogenetics risk category\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003eFavorable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e20.55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003eIntermediate/Normal\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e54.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003ePoor\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e24.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003eStatus\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003eSurvival\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e36.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"30.685920577617328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"27.256317689530686%\\\"\\u003e\\n \\u003cp\\u003eDeath\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"22.20216606498195%\\\"\\u003e\\n \\u003cp\\u003e93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"19.855595667870038%\\\"\\u003e\\n \\u003cp\\u003e63.70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eSD: standard deviation\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2. multivariate Cox regression analysis of survival time in LAML patients\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"19.791666666666668%\\\"\\u003e\\n \\u003cp\\u003eFactor\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"10.416666666666666%\\\"\\u003e\\n \\u003cp\\u003ecoef\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"28.125%\\\"\\u003e\\n \\u003cp\\u003eExp(coef) [95%CI]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.625%\\\"\\u003e\\n \\u003cp\\u003eSe(coef)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.458333333333334%\\\"\\u003e\\n \\u003cp\\u003eZ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.583333333333334%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003evalue\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"19.791666666666668%\\\"\\u003e\\n \\u003cp\\u003eIGHM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.416666666666666%\\\"\\u003e\\n \\u003cp\\u003e0.73\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"28.125%\\\"\\u003e\\n \\u003cp\\u003e2.07 [1.03, 4.15]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.625%\\\"\\u003e\\n \\u003cp\\u003e0.36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.458333333333334%\\\"\\u003e\\n \\u003cp\\u003e2.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.583333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.041\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"19.791666666666668%\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.416666666666666%\\\"\\u003e\\n \\u003cp\\u003e0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"28.125%\\\"\\u003e\\n \\u003cp\\u003e1.04 [1.02, 1.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.625%\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.458333333333334%\\\"\\u003e\\n \\u003cp\\u003e4.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.583333333333334%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"19.791666666666668%\\\"\\u003e\\n \\u003cp\\u003eSex\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.416666666666666%\\\"\\u003e\\n \\u003cp\\u003e-0.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"28.125%\\\"\\u003e\\n \\u003cp\\u003e0.86 [0.55, 1.34]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.625%\\\"\\u003e\\n \\u003cp\\u003e0.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.458333333333334%\\\"\\u003e\\n \\u003cp\\u003e-0.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.583333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.499\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"19.791666666666668%\\\"\\u003e\\n \\u003cp\\u003eRace\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.416666666666666%\\\"\\u003e\\n \\u003cp\\u003e-0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"28.125%\\\"\\u003e\\n \\u003cp\\u003e0.91 [0.33, 2.55]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.625%\\\"\\u003e\\n \\u003cp\\u003e0.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.458333333333334%\\\"\\u003e\\n \\u003cp\\u003e-0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.583333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.863\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3 Significant top up-regulated pathways participated by IGHM\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"105%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"56.70103092783505%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePathway\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.185567010309279%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eES\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.216494845360825%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNES\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNOM p-val\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFDR q-val\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.154639175257732%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003esize\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"56.70103092783505%\\\"\\u003e\\n \\u003cp\\u003e1.GO_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.185567010309279%\\\"\\u003e\\n \\u003cp\\u003e0.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.216494845360825%\\\"\\u003e\\n \\u003cp\\u003e2.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.154639175257732%\\\"\\u003e\\n \\u003cp\\u003e138\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"56.70103092783505%\\\"\\u003e\\n \\u003cp\\u003e2.GO_COMPLEMENT_ACTIVATION\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.185567010309279%\\\"\\u003e\\n \\u003cp\\u003e0.71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.216494845360825%\\\"\\u003e\\n \\u003cp\\u003e2.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.154639175257732%\\\"\\u003e\\n \\u003cp\\u003e157\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"56.70103092783505%\\\"\\u003e\\n \\u003cp\\u003e3.GO_PHAGOCYTOSIS_RECOGNITION\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.185567010309279%\\\"\\u003e\\n \\u003cp\\u003e0.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.216494845360825%\\\"\\u003e\\n \\u003cp\\u003e2.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.154639175257732%\\\"\\u003e\\n \\u003cp\\u003e72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"56.70103092783505%\\\"\\u003e\\n \\u003cp\\u003e4.GO_B_CELL_MEDIATED_IMMUNITY\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.185567010309279%\\\"\\u003e\\n \\u003cp\\u003e0.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.216494845360825%\\\"\\u003e\\n \\u003cp\\u003e2.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.154639175257732%\\\"\\u003e\\n \\u003cp\\u003e206\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"56.70103092783505%\\\"\\u003e\\n \\u003cp\\u003e5.GO_B_CELL_RECEPTOR_SIGNALING_PATHWAY\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.185567010309279%\\\"\\u003e\\n \\u003cp\\u003e0.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.216494845360825%\\\"\\u003e\\n \\u003cp\\u003e1.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.154639175257732%\\\"\\u003e\\n \\u003cp\\u003e112\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"56.70103092783505%\\\"\\u003e\\n \\u003cp\\u003e6.GO_MEMBRANE_INVAGINATION\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.185567010309279%\\\"\\u003e\\n \\u003cp\\u003e0.64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.216494845360825%\\\"\\u003e\\n \\u003cp\\u003e1.95\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.154639175257732%\\\"\\u003e\\n \\u003cp\\u003e123\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"56.70103092783505%\\\"\\u003e\\n \\u003cp\\u003e7.GO_POSITIVE_REGULATION_OF_B_CELL_ACTIVATION\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.185567010309279%\\\"\\u003e\\n \\u003cp\\u003e0.64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.216494845360825%\\\"\\u003e\\n \\u003cp\\u003e1.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.154639175257732%\\\"\\u003e\\n \\u003cp\\u003e131\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eNES: normalized enrichment score; NOM: nominal; FDR: false discovery rate. Gene sets with abs (NES) \\u0026ge; 1, NOM p-val \\u0026le; 0.05 and FDR q-val \\u0026le; 0.25 were considered as significant.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"european-journal-of-medical-research\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ejmr\",\"sideBox\":\"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)\",\"snPcode\":\"40001\",\"submissionUrl\":\"https://submission.nature.com/new-submission/40001/3\",\"title\":\"European Journal of Medical Research\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Acute myeloid leukemia, TCGA, DEGs, Survival Analysis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-120259/v2\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-120259/v2\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground: \\u003c/strong\\u003eAcute myeloid leukemia (AML) is a common and lethal haematological malignant hyperplastic disease originating from hematopoietic stem cells. The purpose of this study is to obtain the key differentially expressed gene (DEG) related to the survival of AML by The Cancer Genome Atlas (TCGA) database and to verify these genes by a clinical follow-up investigation, in order to identify valuable predictive and prognostic biomarkers for early diagnosis of AML and predict the survival rates.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eThe RNA sequencing (RNA-Seq) data and clinical information of TCGA-LAML were downloaded from the TCGA database. After that we 1) screened the survival related DEGs by Cox regression analysis, 2) selected the cytogenetics risk related DEGs by DESeq2 R package, 3) and filtrated the genes in the top10 pathways of up-regulated and down-regulated of Normalization Enrichment Score (NES) by Gene Set Enrichment Analysis (GSEA). Finally, we focused the intersectional genes of above three parts as the key gene of our present study. The following Multivariate Cox regression analyses were performed to analyze these intersectional genes as independent factors. Proportional hazards assumption was evaluated to test Cox regression model by Schoenfeld residuals. The Kaplan–Meier survival curve and Nomogram were plotted to predict and compare the 1-year, 3-year, and 5-year survival rates of AML patients. The concordance index (C-index) and calibration curve were used to evaluate the prediction performance of nomogram.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eResults: \\u003c/strong\\u003eA total of 151 RNA-Seq samples for 60488 genes and 200 clinical samples were selected from the TCGA database. 1005 survival related DEGs were obtained by Cox regression analyses (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.05, exp (coef)\\u0026gt;1), 2291 cytogenetics risk category related DEGs were identified using DESeq2 (log\\u003csub\\u003e2\\u003c/sub\\u003e\\u0026nbsp;Fold Change\\u0026gt;1 and \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.05), and 790 meaningful pathways of cytogenetics risk category were selected from GSEA (abs(NES)≥1, NOM p-val≤0.05 and FDR q-val≤0.25). Screened a key gene IGHM that is related to patient survival rate. The results showed that the IGHM expression and age displayed statistical associations with the survival time of patients (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.05). The risk of death in the patients with high expression group of IGHM was 2.07 times higher than those in low expression group (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.05). For survival, the 1, 3, and 5-year survival of patients in the high expression group are predicted to be 68%, 43%, and 30%. GSEA analysis revealed that IGHM were mainly enriched in the immune response (BP).\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eConclusion: \\u003c/strong\\u003eHigh expression of IHGM gene is an independent risk factor for the prognosis of patients with AML and can be an important molecular marker for predicting the prognosis of patients with AML.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Analysis of the differential expression and prognostic relationship of DEGs in AML based on TCGA database\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":2,\"date\":\"2022-07-14 14:55:23\",\"doi\":\"10.21203/rs.3.rs-120259/v2\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Major revision\",\"date\":\"2023-02-03T09:16:07+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2022-11-28T03:10:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"5970b233-f0bd-4d3c-a5c4-8687a3315595\",\"date\":\"2022-11-13T12:43:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2022-07-02T11:33:37+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2022-06-28T19:30:59+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2022-06-28T12:45:13+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"European Journal of Medical Research\",\"date\":\"2022-06-26T14:42:33+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"european-journal-of-medical-research\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ejmr\",\"sideBox\":\"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)\",\"snPcode\":\"40001\",\"submissionUrl\":\"https://submission.nature.com/new-submission/40001/3\",\"title\":\"European Journal of Medical Research\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"020f1005-3287-45c8-97d8-220524b5a1bb\",\"owner\":[],\"postedDate\":\"July 14th, 2022\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2023-10-16T19:33:07+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-120259\",\"link\":\"https://doi.org/10.1186/s40001-023-01060-3\",\"journal\":{\"identity\":\"european-journal-of-medical-research\",\"isVorOnly\":false,\"title\":\"European Journal of Medical Research\"},\"publishedOn\":\"2023-02-27 19:29:04\",\"publishedOnDateReadable\":\"February 27th, 2023\"},\"versionCreatedAt\":\"2022-07-14 14:55:23\",\"video\":\"\",\"vorDoi\":\"10.1186/s40001-023-01060-3\",\"vorDoiUrl\":\"https://doi.org/10.1186/s40001-023-01060-3\",\"workflowStages\":[]},\"version\":\"v2\",\"identity\":\"rs-120259\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-120259\",\"identity\":\"rs-120259\",\"version\":[\"v2\"]},\"buildId\":\"_2-kVJe1T_tPrBINL-cwx\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}