High Expression of ARHGEF5 Predicts Unfavorable Prognosis in Acute Myeloid Leukemia

High Expression of ARHGEF5 Predicts Unfavorable Prognosis in Acute Myeloid Leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article High Expression of ARHGEF5 Predicts Unfavorable Prognosis in Acute Myeloid Leukemia Haitao Xu, Dangui Chen, Jia Lu, Long Zhong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4491434/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Acute myeloid leukemia (AML) represents a hematological neoplasm that is defined by high heterogeneity. Therefore, identifying new molecular markers for predicting the prognosis and optimizing therapeutic interventions for patients suffering from AML is crucial. Although an increase in Rho guanine nucleotide exchange factor 5 (ARHGEF5) expression level was observed in multiple cancer types, its involvement in AML remains unexplored. We obtained data on the gene expression of patients by accessing "the Cancer Genome Atlas (TCGA)" database to determine ARHGEF5 and AML correlation. Next, a Wilcoxon rank-sum test was conducted for comparing ARHGEF5 expression in patients with AML and normal samples. Additionally, we determined the correlation between ARHGEF5 and patient survival through the Kaplan-Meier (K-M) method as well as Cox regression analysis (CRA). Moreover, a nomogram was constructed using CRA for the prediction of the ARHGEF5 effect on patient prognosis. Next, we determined the pathway and function enriched by ARHGEF5-related genes as well as the association between ARHGEF5 and immune cells using the GO and KEGG pathway enrichment, protein-protein interaction network, and single sample gene set enrichment analyses. The findings indicate a significant ARHGEF5 overexpression in various cancers, including AML, compared to normal samples. Furthermore, the results demonstrated a significant association between ARHGEF5 overexpression and poor prognosis of 151 patients suffering from AML, patients with age ≤ 60, patients harboring mutations in NPM1, FLT3 mutation-positive, and patients harboring wild-type RAS ( P < 0.05). CRA showed that an increase in ARHGEF5 expression level could independently predict the patient's prognosis. The nomogram prognostic model was constructed by incorporating the age and cytogenetics risk of patients. Further, we identified 412 differentially expressed genes (DEGs) between the groups with high and low expression of ARHGEF5 . Specifically, 216 of these DEGs were observed to be overexpressed, while 196 were suppressed. ARHGEF5 overexpression could be a biomarker for predicting unfavorable outcomes among patients with AML. In addition, these DEGs and pathways could clarify the mechanisms behind AML onset and progression. Acute myeloid leukemia ARHGEF5 The Cancer Genome Atlas R packages Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 INTRODUCTION Acute myeloid leukemia (AML), a very aggressive and heterogeneous neoplasm, is defined by varied patient prognoses and a high mortality rate. Currently, the risk stratification and therapeutic approaches for patients suffering from AML are based on the abnormalities in their cytogenetic and molecular features; however, the precise underlying mechanisms of this disease remain unclear[ 1 , 2 ]. The advent of targeted agents has improved the outcomes of personalized therapy and survival rates; however, the outcomes of monotherapy or monotherapy combined with traditional chemotherapy are unsatisfactory[ 3 ]. Therefore, screening novel biomarkers could enhance our comprehension of the molecular basis behind AML. This would facilitate diagnosing, predicting the prognosis and therapeutic response, residual monitoring, and developing targeted drugs. Rho guanine nucleotide exchange factor 5 ( ARHGEF5) , a guanine nucleotide exchange factors (GEFs) Dbl family member, manages Rho GTPases regulation [ 4 ]. ARHGEF5 comprises two isoforms encoded by a single mRNA, and transforming immortalized mammary (TIM) is the short isoform of ARHGEF5 . A high TIM expression level was observed in lung carcinoma, and TIM could activate RhoA in vivo, thereby regulating the reorganization of the RhoA-mediated stress fiber[ 5 ]. A study has shown that TIM could be involved in breast cancer progression[ 6 ]. Studies have shown a significant increase in ARHGEF5 expression in cell lines and tissues of patients with lung adenocarcinoma. In fact, ARHGEF5 overexpression exhibits a correlation to a shorter survival time[ 7 , 8 ]. ARHGEF5 activates RhoA to promote thick stress fiber formation and links the Src and PI3K pathways to promote Src-induced podosome formation[ 9 ]. A study has shown that the Src-ARHGEF5-PI3K complex is expressed in LuM1 cells, a highly metastatic colon adenocarcinoma, while it is not expressed in NM11 cells, which exhibit moderate metastatic potential[ 10 ]. However, the ARHGEF5 expression profile in patients with AML and its significance in predicting their prognosis is still unclear. Herein, the analysis of ARHGEF5 expression was conducted first in patients diagnosed with AML. Next, the functional enrichment analysis of ARHGEF5 was performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, Gene set enrichment analysis (GSEA), immune cell infiltration (ICI), and protein-protein interaction (PPI) network analyses. The study ended with conducting Kaplan-Meier (KM) analysis and Cox regression analysis (CRA), followed by constructing a prognostic nomogram model aimed at ascertaining the clinical function of ARHGEF5 in patients suffering from AML. The findings of our study proposed that ARHGEF5 may be involved in AML. The utilization of this biomarker can be a prognostic indicator and aid in identifying treatment targets for individuals diagnosed with AML. Herein, we elucidated the significance of ARHGEF5 in AML and its potential implications for the research and treatment of patients suffering from AML. MATERIALS AND METHODS The Collection and Processing of Data The present study obtained data pertaining to gene expression patterns and clinical information of patients from two databases, namely "the Cancer Genome Atlas" (TCGA; https://portal.gdc.cancer.gov/repository ) and "the Genotype-Tissue Expression Project" (GTEx; https://commonfund.nih.gov/gtex ). Subsequently, the level 3 HTSeq-FPK format data went through normalization to transcripts per million (TPM) reads. RNA-sequencing data in TPM format was obtained from the UCSC-Xena ( https://xenabrowser.net/datapages/ ) and the GTEx databases to facilitate pan-cancer analysis. Differentially expressed genes (DEGs) Analysis First, we categorized patients with AML from TCGA into low- (LAEG) and high- ARHGEF5 expression groups (HAEG) using the median ARHGEF5 expression score as the threshold value. Next, we employed the "DESeq2" R package for DEGs screening between both groups[ 11 ]. We identified DEGs based on the following thresholds: "adjusted P -value 1." Finally, heat maps were constructed to visualize the top ten DEGs. Functional Enrichment Analysis The study performed functional enrichment analysis on the DEGs meeting the criteria of "|logFC| >1.5" and "p-adj < 0.05". Subsequently, a GO functional analysis was conducted, wherein enriched GO terms were identified across the cellular component (CC), molecular function (MF), and biological process (BP) categories. Additionally, KEGG pathway enrichment analysis was conducted utilizing the "ClusterProfiler" R package [ 12 ]. GSEA We performed GSEA through the "ClusterProfiler" R package (3.6.3) to identify the differences in functions and pathways between both groups [ 12 ]. " p-adj < 0.05" and "FDR q < 0.25" indicated a significant difference. Analyzing PPI Network The study established a PPI network utilizing the DEGs through the web-based STRING ( http://string-db.org/ ) database, applying a confidence score > 0.4 and default parameters. Subsequently, the PPI network visualization was performed utilizing the "Cytoscape" software (version 3.5.0)[ 13 ]. Finally, the significant modules in the PPI network were identified utilizing MCODE (version 1.8.0) [ 14 ]. The criteria employed for this analysis were "MCODE scores > 3" and default parameters. Analysis of ICI The single-sample GSEA (ssGSEA) was conducted via the "GSVA" R package to analyze 24 immune cell types and their relative enrichment score for determining ICI degree in patients having breast cancer [ 15 ]. Next, the association between ARHGEF5 expression and these immune cells was determined through Spearman's correlation analysis. Finally, we compared ICI levels in patients between both groups utilizing the Wilcoxon rank-sum test (WRST). Survival Analysis We performed survival analysis based on the KM method and log-rank test, setting the cut-off as the median ARHGEF5 expression value. Next, we performed univariate CRA (UCRA) and multivariate CRA (MCRA) to determine the influence of clinical features on patient outcomes. Finally, we performed MCRA on prognostic factors with P < 0.05 identified using the UCRA. We visualized the forest plot using the "ggplot2" R package. Constructing and Validating the Nomogram We constructed a nomogram using prognostic factors, which could independently predict the overall survival (OS) of patients identified using MCRA. Next, we used calibration plots to determine the performance, and the concordance index (C-index) was utilized to measure the nomogram discriminatory ability. The RMS (version 5.1-3) R package was utilized for generating the nomogram and calibration plots. The study also evaluated the nomogram accuracy in predicting patient prognosis through a time-dependent receiver-operating characteristic (ROC) curve, employing the "timeROC" package. Statistical Analysis We statistically analyzed the data using R (version 3.6.2)[ 16 ]. First, a paired t-test and WRST were conducted to establish the statistical significance of MCTS1 expression in paired and non-paired samples, respectively. Subsequently, the study conducted Kruskal-Wallis, Wilcoxon signed-rank, and logistic regression analyses to examine the association between clinical/cytogenetic features and ARHGEF5 expression. Next, we employed CRA and the KM method to evaluate the prognostic factors. Finally, we used MCRA to determine the effect of ARHGEF5 expression and other clinical features on the patient's survival. P < 0.05 was deemed significant for all analyses. RESULTS ARHGEF5 expression in Pan-Cancer and patients with AML We retrieved RNA-seq data of patients from TCGA and GTEx using the UCSC-XENA, which were uniformly processed via the toil pipeline, and revealed that ARHGEF5 was significantly overexpressed level in 20 types of cancer (Fig. 1 A), including patients with AML from TCGA, compared to normal samples from TCGA and GTEx (Fig. 1 B). In addition, ROC analysis demonstrated that the sensitivity and specificity of ARHGEF5 in predicting the patient's outcomes was high (AUC 0.872; Fig. 1 C). Identifying DEGs in patients with AML in both groups LAEG and HAEG were compared for differences in median mRNA expression levels. We identified 412 DEGs between both groups, of which 216 were upregulated and 196 were downregulated based on gene expression RNA-seq-HTSeqCounts with significance (Fig. 2 A). The heatmap shows the top five DEGs (up- and down-regulated) in patients in both groups (Fig. 2 B). Functional enrichment analysis of DEGs To identify the functions enriched by these 412 DEGs among patients with AML, GO and KEGG enrichment analyses were conducted (Fig. 3 ), revealing that these DEGs showed significant enrichment in the GO-BP terms, including pattern specification process, synapse organization, and regulating transmembrane ion transport. In addition, the GO-CC terms, such as extracellular matrix containing collagen and transporter, as well as ion channel complexes, and the GO-MF, such as the passive transmembrane transporter, substrate-specific channel, and ion channel activities, were enriched by these DEGs. In addition, the KEGG pathways, including the interaction between cytokine-cytokine receptor, the cAMP signaling pathway, and chemical carcinogenesis, were enriched by these DEGs. Next, GSEA was conducted for the identification of the biological pathways involved in AML in patients expressing varying ARHGEF5 levels. We compared the datasets of patients in both groups to identify signaling pathways involved in AML. Enrichment of these pathways in the MSigDB collection (C2.all.v7.0.symbols.gmt) was observed to differ significantly (FDR < 0.05, p-adj < 0.05, Fig. 4 ). The results revealed an association between ARHGEF5 and cytotoxicity mediated by natural killer (NK) cells, the notch, t-cell receptor, and nod-like receptor signaling pathways and the interaction between the cytokines and cytokine receptors. PPI enrichment analysis in patients with AML A PPI network of ARHGEF5 and probably co-expressing genes with ARHGEF5-associated DEGs were built utilizing the STRING database and a threshold value of 0.4. We identified 412 DEGs. The constructed PPI network comprised 303 nodes and 389 edges and was visualized through Cytoscape-MCODE (Figure S1A). The MCODE score of the module, which was considered the most significant, was 4.667. This module had ten nodes and 21 edges (Figure S1B). Analyzing ICI in patients with AML Spearman's correlation analysis showed the relation between ARHGEF5 expression and ICI in patients having AML quantified via ssGSEA. ARHGEF5 exhibited a positive correlation with active dendritic cells (aDC), cytotoxic cells, NK cells, plasmacytoid dendritic cells (pDC), T helper cells, and follicular helper T cells (TFH). The relation between ARHGEF5 expression, clinical characteristics, and cytogenetic risk Table 1 lists the primary clinical features of patients with AML from TCGA. We analyzed 151 patients with AML, comprising 68 females and 83 males. The average age of patients was 56.7 years. ARHGEF5 expression was low in 75 (49.7%) patients and high in 76 (50.3%) patients with AML. The correlation analysis results indicated a significant relation between ARHGEF5 expression and cytogenetic risk ( P = 0.011) and harboring mutations in FLT3 ( P < 0.001) and NPM1 ( P = 0.018). The WRST was conducted for the comparison of the differences in ARHGEF5 expression patterns across patients with varying clinical and pathological features and indicated that ARHGEF5 was significantly overexpressed in patients in the Black or African American, del7 and complex karyotype, high-risk cytogenetics groups and patients who are harboring mutations in FLT3 and NPM1, and patients harboring wild-type IDH1R140 . The relation between ARHGEF5 expression and poor prognosis of patients with AML The Kaplan-Meier analysis yielded findings indicating that the patients belonging to the HAEG exhibited a significantly worse prognosis relative to those in the LAEG (hazard ratio: 1.79 (1.17–2.73); P = 0.007; Fig. 7 A). Additional analysis indicated that the prognosis of male patients ( P = 0.025), patients aged ≤ 60 ( P = 0.03), intermediate cytogenetic risk ( P = 0.003), the M2 subtype ( P = 0.025), normal karyotype ( P = 0.003), patients harboring mutations in FLT3 ( P = 0.003) and NPM1 ( P = 0.027), and patients harboring wild-type RAS ( P = 0.011) in the HAEG was poor (Fig. 7 B-I). The CRA results revealed that patients aged > 60 years, intermediate/poor cytogenetics, and patients expressing high ARHGEF5 levels were significant risk factors for AML (Fig. 8 A). Furthermore, MCRA showed that ARHGEF5 overexpression could be an independent risk factor for predicting patient prognosis (Fig. 8 B). ARHGEF5 Prognostic model for patients with AML We performed MCRA for constructing a nomogram to improve the accuracy of predicting the patient's prognosis. Three independent prognostic factors, including the patient's age, cytogenetic risk, as well as ARHGEF5 expression, were incorporated into the prognostic model. The column chart model revealed that higher scores were correlated to poor patient prognosis (Fig. 9 A). Additionally, we used the calibration plot to determine the nomogram's predictive efficacy. The bootstrap-corrected C-index of the nomogram was 0.715 (95% CI = 0.690–0.754), thus revealing that the nomogram accuracy in predicting the patient's OS was moderate (Fig. 9 B). DISCUSSION AML can be defined as a heterogeneous clonal disease characterized by the clonal proliferation of primitive hematopoietic stem cells or progenitor cells[ 17 ]. Despite the availability of various therapeutic strategies, the success rate of these treatments in patients with AML is low, and the mortality rate is high due to cancer relapse[ 18 ]. Establishing a standardized therapeutic strategy for relapsed AML is challenging due to genetic and clinical heterogeneity[ 19 ]. Molecular targets are likely to become an established strategy for both induction and consolidation therapy, in addition to maintenance therapy followed by consolidation therapy [ 20 ]. Several studies have focused on assessing genetic alterations at the molecular level for predicting outcomes and identifying prognostic markers[ 21 ]. Recently, there has been a significant emphasis on investigating epigenetic mutations in DNMT3A, TET2 , and ASXL1 [ 22 ]; however, the underlying immunological mechanisms of AML pathogenesis are poorly understood. Our results revealed an increase in ARHGEF5 expression in patients with AML. Furthermore, the study revealed a significant association between ARHGEF5 overexpression and complex chromosomal karyotypes, poor risk classification, and an unfavorable prognosis. The above-mentioned results indicated that ARHGEF5 overexpression might be a probable prognostic biomarker used for individuals suffering from AML. ARHGEF5 belongs to the GEFs family that regulates Rho GTPases[ 4 , 23 ]. Mounting evidence has demonstrated that ARHGEF5 promotes the metastasis and infiltration of cancer cells by activating Rho GTPase, which alters cell adhesion and cytoskeletal functions[ 24 , 25 ]. Debily et al. demonstrated an increase in ARHGEF5 expression level in breast cancer, and a high ARHGEF5 expression level could significantly impact breast cancer progression[ 6 ]. In addition, ARHGEF5 could alter the growth characteristics and the development of tumors in mice[ 23 ]. Compared to normal lung tissue, a significant elevation in ARHGEF5 expression in non-small cell lung cancer cell lines was detected [ 5 ]. However, the comprehension of the expression and prognostic implication of ARHGEF5 among individuals diagnosed with AML remains restricted. The findings of our study indicate a significant elevation in the expression levels of ARHGEF5 in individuals diagnosed with AML. Moreover, high ARHGEF5 expression is strongly correlated with intermediate-to-high cytogenetic risk and poor patient prognosis. Our findings revealed unfavorable survival outcomes for patients with overexpressed ARHGEF5 . MCRA revealed that high ARHGEF5 expression could independently predict patient prognosis. Furthermore, we constructed a nomogram prediction model, revealing the significance of ARHGEF5 expression in predicting the patient's prognosis. Taken together, these results indicate that ARHGEF5 could be used for predictive adverse prognosis of patients suffering from AML. The prognosis of patients harboring FLT3 mutations and expressing high ARHGEF5 levels was found to be poor. In addition, the occurrence of FLT3 mutations in patients having AML can be 10%-30%, while being relatively low in the elderly population [ 26 , 27 ]. FLT3 mutations, such as internal tandem duplications (ITD) and point mutations in the tyrosine kinase domain, result in the constant activation of receptors independent of ligands[ 28 ]. Moreover, a study has highlighted an association between ITD mutations, increased incidence of cancer relapse, and poor OS of patients[ 29 ]. Our results revealed that patients diagnosed with AML and possessing FLT3/ITD mutations but without ARHGEF5 mutations exhibited a more favorable prognosis in comparison to those with both FLT3/ITD and ARHGEF5 mutations. However, additional investigations are necessary to validate the impact of upregulated ARHGEF5 expression in patients diagnosed with AML harboring FLT3/ITD mutation and their underlying mechanisms. ARHGEF5 overexpression in AML is closely associated with the cAMP and Notch pathways. Activated cAMP signaling pathway could inhibit p53 accumulation in acute lymphoblastic leukemia cells due to DNA damage and apoptosis[ 30 ]. Maintaining c-Myc and Bcl2 expression in the HL60 human promyelocytic leukemia cell line may require reciprocal Notch signaling, which could contribute to both cell proliferation and survival[ 31 ]. Yan et al. showed that the Notch signaling pathway-related genes could mediate drug resistance in patients with AML [ 32 ]. We demonstrated a potential correlation between ARHGEF5 and the Notch signaling pathway, indicating that ARHGEF5 could be involved in developing and maintaining leukemia cells. Hence, further research is necessary to corroborate these findings and explore the fundamental regulatory mechanisms implicated in ARHGEF5 and the Notch signaling pathway. The involvement of the tumor immune microenvironment is of critical importance in tumorigenesis and tumor progression. The ssGSEA algorithm was utilized to generate a comprehensive map of 22 distinct immune cell types. Subsequently, the correlation between ARHGEF5 expression and the identified immune cell types was analyzed, revealing a significant association between ARHGEF5 and various immune cell subtypes, comprising aDC, cytotoxic cells, NK cells, pDC, Th cells, and Tfh cells. These immune cells are involved in tumorigenesis and cancer development. Therefore, our results suggest that ARHGEF5 could impact AML onset and progression by regulating ICI. Our study findings indicate that ARHGEF5 may be a prognostic factor for unfavorable outcomes in patients suffering from AML, even following the adjustment for routine clinical features. CRA indicated that patients expressing high ARHGEF5 levels, above the age of 60, and intermediate/poor cytogenetics could independently predict the poor OS of patients. For the accuracy improvement of ARHGEF5 in predicting prognosis, a nomogram model was constructed by combining ARHGEF5 , cytogenetic risk, and age. The C-index of the nomogram model for the OS prediction was 0.715 (0.690–0.754). The calibration plot showed an agreement between 1-year and 3-year OS predictions by nomogram and actual observations. These findings suggested that ARHGEF5 could act a biomarker for accurately predicting the prognosis and stratifying patients having AML based on the NCCN cytogenetically group. However, our study has a few limitations. First, we performed bioinformatics analysis on publicly available databases. Therefore, it is necessary to validate the significance of ARHGEF5 in predicting patient prognosis using clinical AML samples. Additionally, experimental validation is necessary to identify the underlying mechanisms of ARHGEF5, the genetic alterations, and the pathways identified by GSEA. In conclusion, our study showed a significant ARHGEF5 overexpression in patients with AML. Further, a correlation was observed between high ARHGEF5 expression, disease progression, and poor patient survival. Dysregulated notch and cAMP pathways could be involved in high ARHGEF5-mediated leukemogenesis. These results shed light on the pathogenesis and molecular targets of AML. However, additional studies are required to identify the specific mechanism underlying AML progression. Table 1 Correlation between ARHGEF5 expression and clinical features in patients with AML based on TCGA. Characteristic levels Low expression of ARHGEF5 High expression of ARHGEF5 p n 75 76 Gender, n (%) Female 34 (22.5%) 34 (22.5%) 1.000 Male 41 (27.2%) 42 (27.8%) Race, n (%) Asian 0 (0%) 1 (0.7%) 0.062 Black or African American 3 (2%) 10 (6.7%) White 71 (47.7%) 64 (43%) Age, n (%) 60 30 (19.9%) 33 (21.9%) WBC count(x10^9/L), n (%) 20 33 (22%) 40 (26.7%) PB blasts(%), n (%) 70 40 (26.5%) 39 (25.8%) Cytogenetic risk, n (%) Favorable 21 (14.1%) 10 (6.7%) 0.069 Intermediate 38 (25.5%) 44 (29.5%) Poor 15 (10.1%) 21 (14.1%) FAB classifications, n (%) M0 5 (3.3%) 10 (6.7%) 0.436 M1 17 (11.3%) 18 (12%) M2 23 (15.3%) 15 (10%) M3 5 (3.3%) 10 (6.7%) M4 16 (10.7%) 13 (8.7%) M5 6 (4%) 9 (6%) M6 1 (0.7%) 1 (0.7%) M7 1 (0.7%) 0 (0%) Cytogenetics, n (%) Normal 31 (23%) 38 (28.1%) 0.011 + 8 5 (3.7%) 3 (2.2%) del(5) 1 (0.7%) 0 (0%) del(7) 3 (2.2%) 3 (2.2%) inv(16) 7 (5.2%) 1 (0.7%) t(15;17) 2 (1.5%) 9 (6.7%) t(8;21) 7 (5.2%) 0 (0%) t(9;11) 0 (0%) 1 (0.7%) Complex 10 (7.4%) 14 (10.4%) FLT3 mutation, n (%) Negative 12 (8.2%) 33 (22.4%) < 0.001 Positive 61 (41.5%) 41 (27.9%) RAS mutation, n (%) Negative 3 (2%) 5 (3.3%) 0.719 Positive 72 (48%) 70 (46.7%) NPM1 mutation, n (%) Negative 10 (6.7%) 23 (15.3%) 0.018 Positive 65 (43.3%) 52 (34.7%) OS event, n (%) Alive 31 (20.5%) 23 (15.2%) 0.212 Dead 44 (29.1%) 53 (35.1%) Age, meidan (IQR) 55 (39.5, 66.5) 57.5 (44, 66.25) 0.503 Declarations Author Contribution Dangui Chen revised the whole manuscript, worked the new figures, critically improved the text. Haitao Xu supervised, provided resources and wrote the final draft. All authors reviewed the manuscript. Data Availability The gene expression profiles, as well as clinical information, may be accessible on the GDC online platform (https://portal.gdc.cancer.gov/). In this research, publicly accessible datasets were used to conduct the analyses. This information may be accessed at the following link: https://portal.gdc.cancer.gov/repositoryhttps://commonfund.nih.gov/gtexhttps://xenabrowser.net/datapages/ References Hou HA and Tien HF. Genomic landscape in acute myeloid leukemia and its implications in risk classification and targeted therapies. J Biomed Sci. 2020; 27(1):81. Ribeiro S, Eiring AM and Khorashad JS. Genomic Abnormalities as Biomarkers and Therapeutic Targets in Acute Myeloid Leukemia. Cancers (Basel). 2021; 13(20). Dohner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, Ebert BL, Fenaux P, Godley LA, Hasserjian RP, Larson RA, Levine RL, Miyazaki Y, et al. 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Prognostic significance of activating FLT3 mutations in younger adults (16 to 60 years) with acute myeloid leukemia and normal cytogenetics: a study of the AML Study Group Ulm. Blood. 2002; 100(13):4372-4380. Naderi EH, Ugland HK, Diep PP, Josefsen D, Ruud E, Naderi S and Blomhoff HK. Selective inhibition of cell death in malignant vs normal B-cell precursors: implications for cAMP in development and treatment of BCP-ALL. Blood. 2013; 121(10):1805-1813. Li GH, Fan YZ, Liu XW, Zhang BF, Yin DD, He F, Huang SY, Kang ZJ, Xu H, Liu Q, Wu YL, Niu XL, Zhang L, et al. Notch signaling maintains proliferation and survival of the HL60 human promyelocytic leukemia cell line and promotes the phosphorylation of the Rb protein. Mol Cell Biochem. 2010; 340(1-2):7-14. Yan S, Ma D, Ji M, Guo D, Dai J, Zhao P and Ji C. Expression profile of Notch-related genes in multidrug resistant K562/A02 cells compared with parental K562 cells. Int J Lab Hematol. 2010; 32(2):150-158. Additional Declarations No competing interests reported. Supplementary Files FigureS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Jul, 2024 Reviews received at journal 20 Jul, 2024 Reviewers agreed at journal 06 Jul, 2024 Reviews received at journal 30 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers invited by journal 14 Jun, 2024 Editor assigned by journal 30 May, 2024 Submission checks completed at journal 30 May, 2024 First submitted to journal 28 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4491434","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311795085,"identity":"b4904ec5-0b5a-4a5a-8fe7-c2d620292bdb","order_by":0,"name":"Haitao Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDCCAyBUwMDAz97Y+PAD8VoMGBgkew43G0sQq4UBpMXgRnqbAA8xOviOnz144IPB4cQNNx+2MUgw2MnpNhDQInkmL+HgDIO0xJm3E9seFDAkG5sdIKDF4ECOwWEeA5vEvtuJ7QYSDAcStxHUcv6NweE/BhKJDTcPtknwEKXlBtAWBqAtE24wEqlF8sYbg4M9BmnGM3sSgYFsQIRf+M7nGH/4UXFYtp/9+MOHHyrs5AhqQXcnacpHwSgYBaNgFOAAAOHCS3/oLnvsAAAAAElFTkSuQmCC","orcid":"","institution":"Anqing Medical Center Affiliated to Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Xu","suffix":""},{"id":311795087,"identity":"ac7f6c91-345a-45b9-80bf-0ffdc48682a2","order_by":1,"name":"Dangui Chen","email":"","orcid":"","institution":"Anqing Medical Center Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dangui","middleName":"","lastName":"Chen","suffix":""},{"id":311795088,"identity":"788c5547-a3b8-4164-985e-9f568f145c0f","order_by":2,"name":"Jia Lu","email":"","orcid":"","institution":"Anqing Medical Center Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Lu","suffix":""},{"id":311795089,"identity":"cb0678b5-2f87-47d0-a637-d849c63a8b73","order_by":3,"name":"Long Zhong","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2024-05-28 13:54:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4491434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4491434/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58308557,"identity":"a76a9286-787d-4b1d-aa18-b17284f32d13","added_by":"auto","created_at":"2024-06-13 18:48:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":319899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eARHGEF5\u003c/em\u003e expression in patients suffering from AML. (A) Comparison of the high- or low ARHGEF5 expression in different cancer tissues of patients to normal tissues from TCGA. (B) \u003cem\u003eARHGEF5\u003c/em\u003e overexpression in patients with AML compared to normal tissues. (C) The ROC curve indicates that \u003cem\u003eARHGEF5\u003c/em\u003e could serve as a potential diagnostic marker.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/b53ed15f5f3c6cd9b03e39d1.jpg"},{"id":58308556,"identity":"dc900678-f051-4f50-b5bd-e8e859ee9b64","added_by":"auto","created_at":"2024-06-13 18:48:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":322963,"visible":true,"origin":"","legend":"\u003cp\u003e412 DEGs were identified in patients in both groups. (A) The volcano plot shows DEGs, including 216 upregulated and 196 downregulated DEGs. Descending order of normalized expression from red to blue. (B) Ten DEGs were revealed by heat map: five overexpressed and five suppressed genes.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/af70fbaf82eb30ea1dd88bba.jpg"},{"id":58308559,"identity":"81b019e1-9630-46d8-bc90-90e92271930d","added_by":"auto","created_at":"2024-06-13 18:48:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":289842,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG pathway enrichment analyses of DEGs in patients having AML in both groups. (A-C) GO analysis reveals the BP, CC, and MF enriched by DEGs with ARHGEF5. (D) KEGG analysis of DEGs with ARHGEF5 showed significantly enriched KEGG pathways.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/7d7086e5c1d08c910b7ad29a.jpg"},{"id":58308560,"identity":"b739376a-d924-44bc-9cb4-170196870efa","added_by":"auto","created_at":"2024-06-13 18:48:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":586823,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA enrichment plots. (A) NK-cell mediated cytotoxicity. (B) the notch and (C) nod-like receptor signaling pathways. (D) the interaction between cytokines and cytokine receptors. (E) the t-cell receptor signaling pathway. NES, normalized enrichment scores; FDR, false discovery rate.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/842763c260581f03f7c944da.jpg"},{"id":58308555,"identity":"cf8c3fd9-be2d-4d20-b2de-d17e191b9323","added_by":"auto","created_at":"2024-06-13 18:48:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":715079,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between \u003cem\u003eARHGEF5\u003c/em\u003e expression and ICI. (A-F) Relation between E2F2 expression with ICI, including (A) aDC, (B), Cytotoxic cells, (C) NK cells, (D) pDC, (E) Th cells, (F) Tfh cells. (G) Correlation between \u003cem\u003eARHGEF5\u003c/em\u003e expression and 24 tumor-infiltrating lymphocytes.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/f670c35f3b35a1bc3d6be361.jpg"},{"id":58308562,"identity":"1de07a93-de36-4743-87f8-37dfe854a4a3","added_by":"auto","created_at":"2024-06-13 18:48:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":333235,"visible":true,"origin":"","legend":"\u003cp\u003eRelation between \u003cem\u003eARHGEF5\u003c/em\u003e expression, clinical features, and cytogenetic risks. (A) Correlation between \u003cem\u003eARHGEF5\u003c/em\u003eexpression and race, (B) cytogenetics, (C) cytogenetic risk, (D) FLT3, (E) NPM1, and (F) IDH1 R140 mutations.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/8e090016a8aae8906dac76b3.jpg"},{"id":58308561,"identity":"6fba8408-35d7-4dab-ac44-df38052b825c","added_by":"auto","created_at":"2024-06-13 18:48:12","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":745144,"visible":true,"origin":"","legend":"\u003cp\u003eRelation between \u003cem\u003eARHGEF5\u003c/em\u003e expression, clinical features, and cytogenetic risks. (A) Correlation between \u003cem\u003eARHGEF5\u003c/em\u003eexpression and race, (B) cytogenetics, (C) cytogenetic risk, (D) FLT3, (E) NPM1, and (F) IDH1 R140 mutations.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/376c667f3ccfa8012606be57.jpg"},{"id":58308558,"identity":"9af69a27-ad9b-40e3-8e78-3c78749922c3","added_by":"auto","created_at":"2024-06-13 18:48:12","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":345968,"visible":true,"origin":"","legend":"\u003cp\u003eUCRA and MCRA show a correlation between clinical features and the OS of patients with AML. (A)The forest plot shows a UCRA and (B) MCRA of OS.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/ea9a49da6bf20d9046a71437.jpg"},{"id":58308563,"identity":"c00983c9-0875-4e7e-ab12-da2eff8dc9dd","added_by":"auto","created_at":"2024-06-13 18:48:13","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":182607,"visible":true,"origin":"","legend":"\u003cp\u003eA prognosis-predictive model of \u003cem\u003eARHGEF5\u003c/em\u003e in patients with AML. (A) Nomogram and B) Calibration plot predicting the 1- and 3-year OS probability among patients having AML.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/ccbbeed1082c9b6a4e7701d0.jpg"},{"id":58308585,"identity":"41ccec9a-445c-491b-97e3-8f977af98f92","added_by":"auto","created_at":"2024-06-13 18:48:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4494087,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/627721ef-f411-421a-9ca6-6b8c60334a46.pdf"},{"id":58308554,"identity":"14a772d7-210c-487a-b25c-e4ebfc46b13c","added_by":"auto","created_at":"2024-06-13 18:48:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":240688,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4491434/v1/df0043a898a23cce1e48a418.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"High Expression of ARHGEF5 Predicts Unfavorable Prognosis in Acute Myeloid Leukemia","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAcute myeloid leukemia (AML), a very aggressive and heterogeneous neoplasm, is defined by varied patient prognoses and a high mortality rate. Currently, the risk stratification and therapeutic approaches for patients suffering from AML are based on the abnormalities in their cytogenetic and molecular features; however, the precise underlying mechanisms of this disease remain unclear[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The advent of targeted agents has improved the outcomes of personalized therapy and survival rates; however, the outcomes of monotherapy or monotherapy combined with traditional chemotherapy are unsatisfactory[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, screening novel biomarkers could enhance our comprehension of the molecular basis behind AML. This would facilitate diagnosing, predicting the prognosis and therapeutic response, residual monitoring, and developing targeted drugs.\u003c/p\u003e \u003cp\u003eRho guanine nucleotide exchange factor 5 (\u003cem\u003eARHGEF5)\u003c/em\u003e, a guanine nucleotide exchange factors (GEFs) Dbl family member, manages Rho GTPases regulation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. \u003cem\u003eARHGEF5\u003c/em\u003e comprises two isoforms encoded by a single mRNA, and transforming immortalized mammary (TIM) is the short isoform of \u003cem\u003eARHGEF5\u003c/em\u003e. A high \u003cem\u003eTIM\u003c/em\u003e expression level was observed in lung carcinoma, and TIM could activate RhoA in vivo, thereby regulating the reorganization of the RhoA-mediated stress fiber[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A study has shown that TIM could be involved in breast cancer progression[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies have shown a significant increase in \u003cem\u003eARHGEF5\u003c/em\u003e expression in cell lines and tissues of patients with lung adenocarcinoma. In fact, \u003cem\u003eARHGEF5\u003c/em\u003e overexpression exhibits a correlation to a shorter survival time[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cem\u003eARHGEF5\u003c/em\u003e activates RhoA to promote thick stress fiber formation and links the Src and PI3K pathways to promote Src-induced podosome formation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A study has shown that the Src-ARHGEF5-PI3K complex is expressed in LuM1 cells, a highly metastatic colon adenocarcinoma, while it is not expressed in NM11 cells, which exhibit moderate metastatic potential[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the \u003cem\u003eARHGEF5\u003c/em\u003e expression profile in patients with AML and its significance in predicting their prognosis is still unclear.\u003c/p\u003e \u003cp\u003eHerein, the analysis of \u003cem\u003eARHGEF5\u003c/em\u003e expression was conducted first in patients diagnosed with AML. Next, the functional enrichment analysis of \u003cem\u003eARHGEF5\u003c/em\u003e was performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, Gene set enrichment analysis (GSEA), immune cell infiltration (ICI), and protein-protein interaction (PPI) network analyses. The study ended with conducting Kaplan-Meier (KM) analysis and Cox regression analysis (CRA), followed by constructing a prognostic nomogram model aimed at ascertaining the clinical function of \u003cem\u003eARHGEF5\u003c/em\u003e in patients suffering from AML. The findings of our study proposed that \u003cem\u003eARHGEF5\u003c/em\u003e may be involved in AML. The utilization of this biomarker can be a prognostic indicator and aid in identifying treatment targets for individuals diagnosed with AML. Herein, we elucidated the significance of \u003cem\u003eARHGEF5\u003c/em\u003e in AML and its potential implications for the research and treatment of patients suffering from AML.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Collection and Processing of Data\u003c/h2\u003e \u003cp\u003eThe present study obtained data pertaining to gene expression patterns and clinical information of patients from two databases, namely \"the Cancer Genome Atlas\" (TCGA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/repository\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/repository\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and \"the Genotype-Tissue Expression Project\" (GTEx; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://commonfund.nih.gov/gtex\u003c/span\u003e\u003cspan address=\"https://commonfund.nih.gov/gtex\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Subsequently, the level 3 HTSeq-FPK format data went through normalization to transcripts per million (TPM) reads. RNA-sequencing data in TPM format was obtained from the UCSC-Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the GTEx databases to facilitate pan-cancer analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed genes (DEGs) Analysis\u003c/h2\u003e \u003cp\u003eFirst, we categorized patients with AML from TCGA into low- (LAEG) and high-\u003cem\u003eARHGEF5\u003c/em\u003e expression groups (HAEG) using the median ARHGEF5 expression score as the threshold value. Next, we employed the \"DESeq2\" R package for DEGs screening between both groups[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We identified DEGs based on the following thresholds: \"adjusted \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\" and \"|log2-fold-change (FC)|\u0026gt;1.\" Finally, heat maps were constructed to visualize the top ten DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe study performed functional enrichment analysis on the DEGs meeting the criteria of \"|logFC| \u0026gt;1.5\" and \"p-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05\". Subsequently, a GO functional analysis was conducted, wherein enriched GO terms were identified across the cellular component (CC), molecular function (MF), and biological process (BP) categories. Additionally, KEGG pathway enrichment analysis was conducted utilizing the \"ClusterProfiler\" R package [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGSEA\u003c/h2\u003e \u003cp\u003eWe performed GSEA through the \"ClusterProfiler\" R package (3.6.3) to identify the differences in functions and pathways between both groups [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. \" p-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05\" and \"FDR q\u0026thinsp;\u0026lt;\u0026thinsp;0.25\" indicated a significant difference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalyzing PPI Network\u003c/h2\u003e \u003cp\u003eThe study established a PPI network utilizing the DEGs through the web-based STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database, applying a confidence score\u0026thinsp;\u0026gt;\u0026thinsp;0.4 and default parameters. Subsequently, the PPI network visualization was performed utilizing the \"Cytoscape\" software (version 3.5.0)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Finally, the significant modules in the PPI network were identified utilizing MCODE (version 1.8.0) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The criteria employed for this analysis were \"MCODE scores\u0026thinsp;\u0026gt;\u0026thinsp;3\" and default parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of ICI\u003c/h2\u003e \u003cp\u003eThe single-sample GSEA (ssGSEA) was conducted via the \"GSVA\" R package to analyze 24 immune cell types and their relative enrichment score for determining ICI degree in patients having breast cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Next, the association between \u003cem\u003eARHGEF5\u003c/em\u003e expression and these immune cells was determined through Spearman's correlation analysis. Finally, we compared ICI levels in patients between both groups utilizing the Wilcoxon rank-sum test (WRST).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003eWe performed survival analysis based on the KM method and log-rank test, setting the cut-off as the median \u003cem\u003eARHGEF5\u003c/em\u003e expression value. Next, we performed univariate CRA (UCRA) and multivariate CRA (MCRA) to determine the influence of clinical features on patient outcomes. Finally, we performed MCRA on prognostic factors with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 identified using the UCRA. We visualized the forest plot using the \"ggplot2\" R package.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eConstructing and Validating the Nomogram\u003c/h2\u003e \u003cp\u003eWe constructed a nomogram using prognostic factors, which could independently predict the overall survival (OS) of patients identified using MCRA. Next, we used calibration plots to determine the performance, and the concordance index (C-index) was utilized to measure the nomogram discriminatory ability. The RMS (version 5.1-3) R package was utilized for generating the nomogram and calibration plots. The study also evaluated the nomogram accuracy in predicting patient prognosis through a time-dependent receiver-operating characteristic (ROC) curve, employing the \"timeROC\" package.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe statistically analyzed the data using R (version 3.6.2)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. First, a paired t-test and WRST were conducted to establish the statistical significance of MCTS1 expression in paired and non-paired samples, respectively. Subsequently, the study conducted Kruskal-Wallis, Wilcoxon signed-rank, and logistic regression analyses to examine the association between clinical/cytogenetic features and \u003cem\u003eARHGEF5\u003c/em\u003e expression. Next, we employed CRA and the KM method to evaluate the prognostic factors. Finally, we used MCRA to determine the effect of ARHGEF5 expression and other clinical features on the patient's survival. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed significant for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eARHGEF5\u003c/b\u003e \u003cb\u003eexpression in Pan-Cancer and patients with AML\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe retrieved RNA-seq data of patients from TCGA and GTEx using the UCSC-XENA, which were uniformly processed via the toil pipeline, and revealed that ARHGEF5 was significantly overexpressed level in 20 types of cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), including patients with AML from TCGA, compared to normal samples from TCGA and GTEx (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In addition, ROC analysis demonstrated that the sensitivity and specificity of \u003cem\u003eARHGEF5\u003c/em\u003e in predicting the patient's outcomes was high (AUC 0.872; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying DEGs in patients with AML in both groups\u003c/h2\u003e \u003cp\u003eLAEG and HAEG were compared for differences in median mRNA expression levels. We identified 412 DEGs between both groups, of which 216 were upregulated and 196 were downregulated based on gene expression RNA-seq-HTSeqCounts with significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The heatmap shows the top five DEGs (up- and down-regulated) in patients in both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eTo identify the functions enriched by these 412 DEGs among patients with AML, GO and KEGG enrichment analyses were conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), revealing that these DEGs showed significant enrichment in the GO-BP terms, including pattern specification process, synapse organization, and regulating transmembrane ion transport. In addition, the GO-CC terms, such as extracellular matrix containing collagen and transporter, as well as ion channel complexes, and the GO-MF, such as the passive transmembrane transporter, substrate-specific channel, and ion channel activities, were enriched by these DEGs. In addition, the KEGG pathways, including the interaction between cytokine-cytokine receptor, the cAMP signaling pathway, and chemical carcinogenesis, were enriched by these DEGs.\u003c/p\u003e \u003cp\u003eNext, GSEA was conducted for the identification of the biological pathways involved in AML in patients expressing varying \u003cem\u003eARHGEF5\u003c/em\u003e levels. We compared the datasets of patients in both groups to identify signaling pathways involved in AML. Enrichment of these pathways in the MSigDB collection (C2.all.v7.0.symbols.gmt) was observed to differ significantly (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results revealed an association between \u003cem\u003eARHGEF5\u003c/em\u003e and cytotoxicity mediated by natural killer (NK) cells, the notch, t-cell receptor, and nod-like receptor signaling pathways and the interaction between the cytokines and cytokine receptors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePPI enrichment analysis in patients with AML\u003c/h2\u003e \u003cp\u003eA PPI network of \u003cem\u003eARHGEF5\u003c/em\u003e and probably co-expressing genes with ARHGEF5-associated DEGs were built utilizing the STRING database and a threshold value of 0.4. We identified 412 DEGs. The constructed PPI network comprised 303 nodes and 389 edges and was visualized through Cytoscape-MCODE (Figure S1A). The MCODE score of the module, which was considered the most significant, was 4.667. This module had ten nodes and 21 edges (Figure S1B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAnalyzing ICI in patients with AML\u003c/h2\u003e \u003cp\u003eSpearman's correlation analysis showed the relation between ARHGEF5 expression and ICI in patients having AML quantified via ssGSEA. ARHGEF5 exhibited a positive correlation with active dendritic cells (aDC), cytotoxic cells, NK cells, plasmacytoid dendritic cells (pDC), T helper cells, and follicular helper T cells (TFH).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe relation between\u003c/b\u003e \u003cb\u003eARHGEF5\u003c/b\u003e \u003cb\u003eexpression, clinical characteristics, and cytogenetic risk\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the primary clinical features of patients with AML from TCGA. We analyzed 151 patients with AML, comprising 68 females and 83 males. The average age of patients was 56.7 years. \u003cem\u003eARHGEF5\u003c/em\u003e expression was low in 75 (49.7%) patients and high in 76 (50.3%) patients with AML. The correlation analysis results indicated a significant relation between \u003cem\u003eARHGEF5\u003c/em\u003e expression and cytogenetic risk (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and harboring mutations in \u003cem\u003eFLT3\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003eNPM1\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e \u003cp\u003eThe WRST was conducted for the comparison of the differences in \u003cem\u003eARHGEF5\u003c/em\u003e expression patterns across patients with varying clinical and pathological features and indicated that \u003cem\u003eARHGEF5\u003c/em\u003e was significantly overexpressed in patients in the Black or African American, del7 and complex karyotype, high-risk cytogenetics groups and patients who are harboring mutations in FLT3 and NPM1, and patients harboring wild-type \u003cem\u003eIDH1R140\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe relation between\u003c/b\u003e \u003cb\u003eARHGEF5\u003c/b\u003e \u003cb\u003eexpression and poor prognosis of patients with AML\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe Kaplan-Meier analysis yielded findings indicating that the patients belonging to the HAEG exhibited a significantly worse prognosis relative to those in the LAEG (hazard ratio: 1.79 (1.17\u0026ndash;2.73); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Additional analysis indicated that the prognosis of male patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), patients aged\u0026thinsp;\u0026le;\u0026thinsp;60 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), intermediate cytogenetic risk (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), the M2 subtype (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), normal karyotype (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), patients harboring mutations in FLT3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and \u003cem\u003eNPM1\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), and patients harboring wild-type \u003cem\u003eRAS\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) in the HAEG was poor (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-I).\u003c/p\u003e \u003cp\u003eThe CRA results revealed that patients aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years, intermediate/poor cytogenetics, and patients expressing high \u003cem\u003eARHGEF5\u003c/em\u003e levels were significant risk factors for AML (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Furthermore, MCRA showed that \u003cem\u003eARHGEF5\u003c/em\u003e overexpression could be an independent risk factor for predicting patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cb\u003eARHGEF5\u003c/b\u003e \u003cb\u003ePrognostic model for patients with AML\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe performed MCRA for constructing a nomogram to improve the accuracy of predicting the patient's prognosis. Three independent prognostic factors, including the patient's age, cytogenetic risk, as well as \u003cem\u003eARHGEF5\u003c/em\u003e expression, were incorporated into the prognostic model. The column chart model revealed that higher scores were correlated to poor patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Additionally, we used the calibration plot to determine the nomogram's predictive efficacy. The bootstrap-corrected C-index of the nomogram was 0.715 (95% CI\u0026thinsp;=\u0026thinsp;0.690\u0026ndash;0.754), thus revealing that the nomogram accuracy in predicting the patient's OS was moderate (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAML can be defined as a heterogeneous clonal disease characterized by the clonal proliferation of primitive hematopoietic stem cells or progenitor cells[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite the availability of various therapeutic strategies, the success rate of these treatments in patients with AML is low, and the mortality rate is high due to cancer relapse[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Establishing a standardized therapeutic strategy for relapsed AML is challenging due to genetic and clinical heterogeneity[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Molecular targets are likely to become an established strategy for both induction and consolidation therapy, in addition to maintenance therapy followed by consolidation therapy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Several studies have focused on assessing genetic alterations at the molecular level for predicting outcomes and identifying prognostic markers[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recently, there has been a significant emphasis on investigating epigenetic mutations in \u003cem\u003eDNMT3A, TET2\u003c/em\u003e, and \u003cem\u003eASXL1\u003c/em\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; however, the underlying immunological mechanisms of AML pathogenesis are poorly understood. Our results revealed an increase in \u003cem\u003eARHGEF5\u003c/em\u003e expression in patients with AML. Furthermore, the study revealed a significant association between ARHGEF5 overexpression and complex chromosomal karyotypes, poor risk classification, and an unfavorable prognosis. The above-mentioned results indicated that \u003cem\u003eARHGEF5\u003c/em\u003e overexpression might be a probable prognostic biomarker used for individuals suffering from AML.\u003c/p\u003e \u003cp\u003e \u003cem\u003eARHGEF5\u003c/em\u003e belongs to the GEFs family that regulates Rho GTPases[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Mounting evidence has demonstrated that \u003cem\u003eARHGEF5\u003c/em\u003e promotes the metastasis and infiltration of cancer cells by activating Rho GTPase, which alters cell adhesion and cytoskeletal functions[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Debily et al. demonstrated an increase in \u003cem\u003eARHGEF5\u003c/em\u003e expression level in breast cancer, and a high \u003cem\u003eARHGEF5\u003c/em\u003e expression level could significantly impact breast cancer progression[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, \u003cem\u003eARHGEF5\u003c/em\u003e could alter the growth characteristics and the development of tumors in mice[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Compared to normal lung tissue, a significant elevation in \u003cem\u003eARHGEF5\u003c/em\u003e expression in non-small cell lung cancer cell lines was detected [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the comprehension of the expression and prognostic implication of \u003cem\u003eARHGEF5\u003c/em\u003e among individuals diagnosed with AML remains restricted. The findings of our study indicate a significant elevation in the expression levels of ARHGEF5 in individuals diagnosed with AML. Moreover, high \u003cem\u003eARHGEF5\u003c/em\u003e expression is strongly correlated with intermediate-to-high cytogenetic risk and poor patient prognosis.\u003c/p\u003e \u003cp\u003eOur findings revealed unfavorable survival outcomes for patients with overexpressed \u003cem\u003eARHGEF5\u003c/em\u003e. MCRA revealed that high \u003cem\u003eARHGEF5\u003c/em\u003e expression could independently predict patient prognosis. Furthermore, we constructed a nomogram prediction model, revealing the significance of \u003cem\u003eARHGEF5\u003c/em\u003e expression in predicting the patient's prognosis. Taken together, these results indicate that \u003cem\u003eARHGEF5\u003c/em\u003e could be used for predictive adverse prognosis of patients suffering from AML.\u003c/p\u003e \u003cp\u003eThe prognosis of patients harboring \u003cem\u003eFLT3\u003c/em\u003e mutations and expressing high \u003cem\u003eARHGEF5\u003c/em\u003e levels was found to be poor. In addition, the occurrence of \u003cem\u003eFLT3\u003c/em\u003e mutations in patients having AML can be 10%-30%, while being relatively low in the elderly population [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. \u003cem\u003eFLT3\u003c/em\u003e mutations, such as internal tandem duplications (ITD) and point mutations in the tyrosine kinase domain, result in the constant activation of receptors independent of ligands[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, a study has highlighted an association between ITD mutations, increased incidence of cancer relapse, and poor OS of patients[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our results revealed that patients diagnosed with AML and possessing FLT3/ITD mutations but without ARHGEF5 mutations exhibited a more favorable prognosis in comparison to those with both FLT3/ITD and ARHGEF5 mutations. However, additional investigations are necessary to validate the impact of upregulated \u003cem\u003eARHGEF5\u003c/em\u003e expression in patients diagnosed with AML harboring FLT3/ITD mutation and their underlying mechanisms.\u003c/p\u003e \u003cp\u003e \u003cem\u003eARHGEF5\u003c/em\u003e overexpression in AML is closely associated with the cAMP and Notch pathways. Activated cAMP signaling pathway could inhibit p53 accumulation in acute lymphoblastic leukemia cells due to DNA damage and apoptosis[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Maintaining c-Myc and Bcl2 expression in the HL60 human promyelocytic leukemia cell line may require reciprocal Notch signaling, which could contribute to both cell proliferation and survival[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Yan et al. showed that the Notch signaling pathway-related genes could mediate drug resistance in patients with AML [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We demonstrated a potential correlation between ARHGEF5 and the Notch signaling pathway, indicating that \u003cem\u003eARHGEF5\u003c/em\u003e could be involved in developing and maintaining leukemia cells. Hence, further research is necessary to corroborate these findings and explore the fundamental regulatory mechanisms implicated in \u003cem\u003eARHGEF5\u003c/em\u003e and the Notch signaling pathway.\u003c/p\u003e \u003cp\u003eThe involvement of the tumor immune microenvironment is of critical importance in tumorigenesis and tumor progression. The ssGSEA algorithm was utilized to generate a comprehensive map of 22 distinct immune cell types. Subsequently, the correlation between \u003cem\u003eARHGEF5\u003c/em\u003e expression and the identified immune cell types was analyzed, revealing a significant association between \u003cem\u003eARHGEF5\u003c/em\u003e and various immune cell subtypes, comprising aDC, cytotoxic cells, NK cells, pDC, Th cells, and Tfh cells. These immune cells are involved in tumorigenesis and cancer development. Therefore, our results suggest that \u003cem\u003eARHGEF5\u003c/em\u003e could impact AML onset and progression by regulating ICI.\u003c/p\u003e \u003cp\u003eOur study findings indicate that \u003cem\u003eARHGEF5\u003c/em\u003e may be a prognostic factor for unfavorable outcomes in patients suffering from AML, even following the adjustment for routine clinical features. CRA indicated that patients expressing high \u003cem\u003eARHGEF5\u003c/em\u003e levels, above the age of 60, and intermediate/poor cytogenetics could independently predict the poor OS of patients. For the accuracy improvement of \u003cem\u003eARHGEF5\u003c/em\u003e in predicting prognosis, a nomogram model was constructed by combining \u003cem\u003eARHGEF5\u003c/em\u003e, cytogenetic risk, and age. The C-index of the nomogram model for the OS prediction was 0.715 (0.690\u0026ndash;0.754). The calibration plot showed an agreement between 1-year and 3-year OS predictions by nomogram and actual observations. These findings suggested that \u003cem\u003eARHGEF5\u003c/em\u003e could act a biomarker for accurately predicting the prognosis and stratifying patients having AML based on the NCCN cytogenetically group.\u003c/p\u003e \u003cp\u003eHowever, our study has a few limitations. First, we performed bioinformatics analysis on publicly available databases. Therefore, it is necessary to validate the significance of \u003cem\u003eARHGEF5\u003c/em\u003e in predicting patient prognosis using clinical AML samples. Additionally, experimental validation is necessary to identify the underlying mechanisms of ARHGEF5, the genetic alterations, and the pathways identified by GSEA.\u003c/p\u003e \u003cp\u003eIn conclusion, our study showed a significant \u003cem\u003eARHGEF5\u003c/em\u003e overexpression in patients with AML. Further, a correlation was observed between high \u003cem\u003eARHGEF5\u003c/em\u003e expression, disease progression, and poor patient survival. Dysregulated notch and cAMP pathways could be involved in high ARHGEF5-mediated leukemogenesis. These results shed light on the pathogenesis and molecular targets of AML. However, additional studies are required to identify the specific mechanism underlying AML progression.\u003c/p\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between \u003cem\u003eARHGEF5\u003c/em\u003e expression and clinical features in patients with AML based on TCGA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow expression of ARHGEF5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh expression of ARHGEF5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count(x10^9/L), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePB blasts(%), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytogenetic risk, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAB classifications, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytogenetics, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edel(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edel(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003einv(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et(15;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et(8;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et(9;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLT3 mutation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAS mutation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPM1 mutation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS event, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, meidan (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (39.5, 66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.5 (44, 66.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDangui Chen revised the whole manuscript, worked the new figures, critically improved the text. Haitao Xu supervised, provided resources and wrote the final draft. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe gene expression profiles, as well as clinical information, may be accessible on the GDC online platform (https://portal.gdc.cancer.gov/). In this research, publicly accessible datasets were used to conduct the analyses. This information may be accessed at the following link: https://portal.gdc.cancer.gov/repositoryhttps://commonfund.nih.gov/gtexhttps://xenabrowser.net/datapages/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHou HA and Tien HF. Genomic landscape in acute myeloid leukemia and its implications in risk classification and targeted therapies. J Biomed Sci. 2020; 27(1):81.\u003c/li\u003e\n\u003cli\u003eRibeiro S, Eiring AM and Khorashad JS. Genomic Abnormalities as Biomarkers and Therapeutic Targets in Acute Myeloid Leukemia. 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Oncol Rev. 2012; 6(1):e8.\u003c/li\u003e\n\u003cli\u003eFrohling S, Schlenk RF, Breitruck J, Benner A, Kreitmeier S, Tobis K, Dohner H, Dohner K and leukemia AMLSGUAm. Prognostic significance of activating FLT3 mutations in younger adults (16 to 60 years) with acute myeloid leukemia and normal cytogenetics: a study of the AML Study Group Ulm. Blood. 2002; 100(13):4372-4380.\u003c/li\u003e\n\u003cli\u003eNaderi EH, Ugland HK, Diep PP, Josefsen D, Ruud E, Naderi S and Blomhoff HK. Selective inhibition of cell death in malignant vs normal B-cell precursors: implications for cAMP in development and treatment of BCP-ALL. Blood. 2013; 121(10):1805-1813.\u003c/li\u003e\n\u003cli\u003eLi GH, Fan YZ, Liu XW, Zhang BF, Yin DD, He F, Huang SY, Kang ZJ, Xu H, Liu Q, Wu YL, Niu XL, Zhang L, et al. Notch signaling maintains proliferation and survival of the HL60 human promyelocytic leukemia cell line and promotes the phosphorylation of the Rb protein. Mol Cell Biochem. 2010; 340(1-2):7-14.\u003c/li\u003e\n\u003cli\u003eYan S, Ma D, Ji M, Guo D, Dai J, Zhao P and Ji C. Expression profile of Notch-related genes in multidrug resistant K562/A02 cells compared with parental K562 cells. Int J Lab Hematol. 2010; 32(2):150-158.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute myeloid leukemia, ARHGEF5, The Cancer Genome Atlas, R packages, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4491434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4491434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcute myeloid leukemia (AML) represents a hematological neoplasm that is defined by high heterogeneity. Therefore, identifying new molecular markers for predicting the prognosis and optimizing therapeutic interventions for patients suffering from AML is crucial. Although an increase in Rho guanine nucleotide exchange factor 5 (ARHGEF5) expression level was observed in multiple cancer types, its involvement in AML remains unexplored. We obtained data on the gene expression of patients by accessing \"the Cancer Genome Atlas (TCGA)\" database to determine \u003cem\u003eARHGEF5\u003c/em\u003e and AML correlation. Next, a Wilcoxon rank-sum test was conducted for comparing \u003cem\u003eARHGEF5\u003c/em\u003e expression in patients with AML and normal samples. Additionally, we determined the correlation between \u003cem\u003eARHGEF5\u003c/em\u003e and patient survival through the Kaplan-Meier (K-M) method as well as Cox regression analysis (CRA). Moreover, a nomogram was constructed using CRA for the prediction of the \u003cem\u003eARHGEF5\u003c/em\u003e effect on patient prognosis. Next, we determined the pathway and function enriched by ARHGEF5-related genes as well as the association between \u003cem\u003eARHGEF5\u003c/em\u003e and immune cells using the GO and KEGG pathway enrichment, protein-protein interaction network, and single sample gene set enrichment analyses. The findings indicate a significant \u0026nbsp;\u003cem\u003eARHGEF5\u003c/em\u003e overexpression in various cancers, including AML, compared to normal samples. Furthermore, the results demonstrated a significant association between \u003cem\u003eARHGEF5\u003c/em\u003e overexpression and poor prognosis of 151 patients suffering from AML, patients with age ≤ 60, patients harboring mutations in NPM1, FLT3 mutation-positive, and patients harboring wild-type RAS (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). CRA showed that an increase in \u003cem\u003eARHGEF5\u003c/em\u003e expression level could independently predict the patient's prognosis. The nomogram prognostic model was constructed by incorporating the age and cytogenetics risk of patients. Further, we identified 412 differentially expressed genes (DEGs) between the groups with high and low expression of \u003cem\u003eARHGEF5\u003c/em\u003e. Specifically, 216 of these DEGs were observed to be overexpressed, while 196 were suppressed. \u003cem\u003eARHGEF5\u003c/em\u003e overexpression could be a biomarker for predicting unfavorable outcomes among patients with AML. In addition, these DEGs and pathways could clarify the mechanisms behind AML onset and progression.\u003c/p\u003e","manuscriptTitle":"High Expression of ARHGEF5 Predicts Unfavorable Prognosis in Acute Myeloid Leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 18:48:04","doi":"10.21203/rs.3.rs-4491434/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-22T09:06:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-20T16:41:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313786302242561548719060323330127052170","date":"2024-07-06T08:40:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-30T13:20:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33837073207958249571398785998573913609","date":"2024-06-21T23:23:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-14T18:36:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-30T12:58:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-30T12:57:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2024-05-28T13:53:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"034e194e-2361-4efc-84f0-82ec46c45233","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-18T05:36:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 18:48:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4491434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4491434","identity":"rs-4491434","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}