Prognostic significance of angiogenesis-associated molecules and immunologic characteristic in elderly patients with acute myeloid leukemia

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This study aims to comprehensively analyze angiogenesis-associated genes in relation to the prognosis of elderly patients with acute myeloid leukemia (AML). Methods: Leukemia gene expression data were obtained from the GSE37642 (training set) and TCGA_LAML (validation set) datasets. Angiogenesis-associated genes were identified using the GeneCards database. Univariate Cox regression and LASSO analyses were employed to identify angiogenesis-associated genes linked to AML prognosis. A prognostic signature was constructed based on the selected genes, and its biological functions were analyzed. Finally, we predicted AML drug sensitivity and evaluated differences in drug activity based on the prognostic signature. Results: Five angiogenesis-related genes associated with AML prognosis were identified: ECM1, EGLN1, FKBP5, FOXP1, and SIRT2. Kaplan-Meier analyses confirmed their prognostic value. A prognostic signature based on these genes demonstrated commendable efficacy in predicting patient outcomes. This signature was found to be an independent risk factor for AML and revealed distinct immune profiles. Furthermore, the signature was implicated in the tumor immune microenvironment, with high-risk patients exhibiting elevated levels of immune cell infiltration. Drug sensitivity analysis revealed negative correlations between FOXP1 and Daporinad, ABT737, and BI.2536, while SIRT2 showed positive correlations with ABT737, BI.2536, and ULK1_4989. Conclusion: We have constructed an angiogenesis-related gene prognostic signature that enriches the prognostic assessment system for AML and provides novel therapeutic directions for this disease. Elderly patients with acute myeloid leukemia Angiogenesis-related genes Prognostic signature Immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Acute myeloid leukemia (AML) is a hematological malignancy characterized by the dysdifferentiation and clonal expansion of myeloid progenitor cells, leading to their accumulation in peripheral blood and bone marrow [ 1 ]. This disease primarily affects the elderly, with a median age of 68 years at diagnosis [ 2 ]. Significant advances in AML treatment have been achieved through the development of hypomethylating agents (HMAs) such as decitabine and azacitidine [ 3 , 4 ], as well as combination therapies involving HMAs with venetoclax [ 5 , 6 ] or ivosidenib in IDH1-mutated AML [ 7 ]. However, outcomes for elderly patients with AML (LAML) remain unsatisfactory due to the toxicity associated with intensive chemotherapy and frequent comorbidities [ 8 , 9 ]. Consequently, there is an urgent need to discover novel therapeutic targets and interventions to improve LAML outcomes. In tumors, neovascularization mechanisms are hyperactivated, resulting in vascular dysfunction characterized by high permeability and poor pericellular attachment, which contribute to tumor metastasis and growth [ 10 ]. As such, anti-angiogenic or vessel-targeted therapies have emerged as attractive approaches in combinatorial tumor treatment regimens, including chemotherapy and immunotherapy [ 11 , 12 ]. While numerous studies have elucidated the role of angiogenesis in solid tumors, recent decades have seen increasing research focused on clarifying its effects in hematological malignancies, including B-cell non-Hodgkin lymphoma [ 13 ], acute promyelocytic leukemia [ 14 ], and multiple myeloma [ 15 ]. These investigations have revealed promising applications for anti-angiogenic therapies in hematological malignancies. Moreover, evidence has demonstrated the impact of angiogenesis in AML patients [ 16 ], and vascular endothelial growth factor (VEGF)-targeted therapy in combination with conventional chemotherapy has shown improved outcomes in AML patients [ 17 ]. However, to date, there has been no comprehensive analysis investigating the prognostic significance of angiogenesis-associated genes in LAML. To address this gap in knowledge, we conducted a thorough examination of the expression patterns and prognostic significance of angiogenesis-associated genes in LAML. We constructed a prognostic signature for LAML that serves as an independent prognostic indicator and reveals distinct immune profiles. Furthermore, we explored the immune microenvironment of diverse LAML patients and categorized them based on the characteristics of angiogenesis-associated genes. Finally, we predicted LAML drug sensitivity and evaluated differences in drug activity based on the prognostic signature. This comprehensive analysis provides valuable insights into the role of angiogenesis-associated genes in LAML prognosis and may inform the development of novel therapeutic strategies for this challenging disease. Materials and methods Data preparation The leukemia gene expression profile GSE37642 was obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ), while the TCGA_LAML dataset was accessed through the Cancer Genome Atlas (TCGA) database ( https://www.cancer.gov/ccg/research/genome-sequencing/tcga ). For prognostic analysis, we selected patients aged 60 years and older, resulting in a cohort of 343 acute myeloid leukemia (AML) samples. The GSE37642 dataset served as the training set, whereas the TCGA_LAML dataset was utilized as the validation set for subsequent analyses. This study employed anonymized, de-identified, publicly available data, thus exempting it from ethics committee approval. Identification of angiogenesis-associated gens with prognostic significance in LAML Angiogenesis-associated genes were extracted from the GeneCards database [ 18 ]. The survminer R package's surv_cutpoint function was employed to determine the optimal cutoff value for each gene across all samples, enabling the classification of samples into high- and low-expression groups. Subsequently, univariate Cox regression analysis was conducted to identify genes significantly associated with survival in the GSE37642 dataset, with validation performed using the TCGA_LAML dataset. Genes demonstrating overlap between survival association and angiogenesis involvement were designated as candidate molecules. The prognostic significance of these selected candidates was evaluated using Kaplan-Meier curves, followed by log-rank tests implemented through the survminer package. To identify the most robust genes for predicting survival in LAML patients, Least Absolute Shrinkage and Selection Operator (LASSO) analysis was performed using the glmnet package. This approach helped mitigate overfitting, and ten-fold cross-validation was utilized to determine the optimal value for the penalty parameter. Consensus clustering of survival-related angiogenesis-associated genes To further elucidate the functional significance and prognostic value of the selected genes in LAML, we employed consensus clustering analysis. This approach utilized the following parameters: 1,000 iterations, k = 10, and agglomerative hierarchical clustering with Ward's method (Ward.D2) for inner linkage and complete linkage for outer grouping. We then validated the optimal number of clusters through principal component analysis (PCA) using the "ggplot2" R package. Subsequently, we generated Kaplan-Meier survival curves to assess the prognostic implications of the resulting cluster classifications. Construction and validation of an angiogenesis-associated prognostic signature Multivariate Cox proportional hazards regression analysis was employed to derive the coefficients of prognostic-related angiogenesis-associated genes. Subsequently, a risk score for each patient was calculated using these coefficients to construct a prognostic signature. The detailed formula is as follows: Risk score = β1 * Exp1 + β2 * Exp2 + β, where β and Exp represent coefficients and expression levels of selected genes, respectively. Patients were stratified into high- and low-risk groups based on the median risk score. Kaplan-Meier survival analysis, coupled with log-rank tests, was performed to assess the prognostic value of this classification using the "survminer" R package. Functional and pathway enrichment analysis Angiogenesis-associated genes exhibiting a correlation coefficient magnitude (|R|) greater than 0.3 were deemed to have a strong relationship with the risk score. These genes were subsequently utilized to investigate the biological pathways associated with the signature. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted on the identified genes using the clusterProfiler package. To elucidate potential functional mechanisms, Gene Set Enrichment Analysis (GSEA) was performed using the c2.cp.v7.2.symbols.gtm file. The threshold for significance was set at a false discovery rate (FDR) < 0.2 and a normalized P-value < 0.05. Assessment of the immune microenvironment composition and drug sensitivity In our analysis, we employed the CIBERSORT algorithm implemented in R to evaluate the infiltration patterns of 22 immune cell types in LAML tissues from the TCGA_LAML dataset. We considered only results with a statistical significance of P < 0.05 for determining the relative abundance of infiltrating immune cells. To assess differential immune cell infiltration between high- and low-risk LAML tissues, we applied the Wilcoxon rank-sum test. Furthermore, we investigated the correlations between the selected prognostic genes and infiltrating immune cells using Spearman's rank correlation analysis. We also compared tumor purity across samples using the estimate package in R and visualized the results using the ggplot2 and pheatmap packages. Lastly, we predicted LAML drug sensitivity and evaluated differences in drug activity across risk score groups using the oncoPredict package in R. Statistical analyses All statistical analysis and data visualization were performed using R program. Differences between two groups were compared using t-tests or Wilcoxon tests, and more than two groups were compared using Kruskal Wallis test. Kaplan-Meier curves followed by log-rank tests were employed for prognostic analysis. Univariate and multivariate Cox regression models was utilized to evaluated prognostic value of angiogenesis-associated genes. Pearson’s chi-square test was applied to determine variations in the distribution of clinical variables in LAML. A P-value less than 0.05 was considered statistically significant. Results Identification of angiogenesis-associated gens with prognostic significance in LAML The flow chart of this work was shown in Fig. 1 A. A total of 233 genes were found to be significantly associated with prognosis of LAML (P < 0.05) from GSE37642 dataset, and 1137 angiogenesis-associated genes were obtained from the GeneCards database, containing 8 genes significantly associated with prognosis of LAML. Thus, we selected these 8 genes as candidate targets, which were further screened using LASSO analysis and verified using TCGA_LAML dataset. We ultimately identified 5 angiogenesis-related genes that associated with prognosis (Figures S1 and S2), namely ECM1, EGLN1, FKBP5, FOXP1, and SIRT2. Further Kaplan-Meier curves delineated that high expression of ECM1, EGLN1, FKBP5, and SIRT2, whereas low expression of FOXP1 were related to worse prognosis (Fig. 1 B and 1 C). Cluster classification was implicated with the malignancy of LAML To investigate the overall prognostic value of these 5 genes, we conducted a consensus clustering analysis to classify LAML patients, showing that k = 2 appeared to be a more stable value than that of k = 3–6 (Figs. 2 A and 2 B). PCA was further employed to validate the reliability of the cluster, indicating that, when k = 3–5, the samples showed high similarity and clustered together (Figure S3). Thus, we stratified the LAML patients into 2 clusters. The Kaplan-Meier curves demonstrated that patients in cluster 1 had a poor prognosis than those in cluster 2 (Fig. 2 C). Construction and validation of an angiogenesis-associated prognostic signature Risk scores for prognostic signature of LAML patients were constructed based on the coefficients and expression levels of 5 selected genes. The detailed formula was shown below: Risk score = 0.5285351*ECM1 + 0.2861940*EGLN1 + 0.3392907*FKBP5-0.5082985*FOXP1 + 1.0852212*SIRT2. The patients were classified into low- and high-risk groups based on the median risk score, and Kaplan-Meier curves showed that patients with high-risk had worse prognosis compared with those with low-risk (Fig. 2 D). The prognostic characteristics were then validated in TCGA_LAML dataset, showing the similar result of GSE37642 dataset (Figure S4). These results demonstrate the reliability and stability of the prognostic characteristics. Further analysis identified risk score as an independent risk factor for prognosis of LAML patients (Figs. 2 E and 2 F). Identification of the prognostic signature-related biological pathways We selected genes closely related to the risk score to explore potential biological pathways associated with prognostic signature, including 915 positively correlated genes and 187 negatively correlated genes. The enrichment analysis results of GO and KEGG pathways are shown in Figs. 3 A, 3 C, and 3 E. The GO analysis primarily enriched in actin filament organization, while the KEGG analysis primarily enriched in salmonella infection. The results of GSEA analysis show that high-risk samples are mainly enriched in immune-related pathways (Figs. 3 B, 3 D, and 3 F). The associates with the tumor immune microenvironment and drug sensitivity As depicted in Fig. 4 A, remarkable disparities in the infiltration of immune cells can be observed between high- and low-risk patients, with intricate variations in the proportions of various immune cells. Subsequent analysis unveiled that, in comparison to high-risk patients, low-risk patients exhibited reduced immune scores and heightened tumor purity (Fig. 4 B). Correlation analysis demonstrated a robust association between 5 selected angiogenesis-associated genes with and immune cells, potentially implicated in the regulation of patient prognosis (Figs. 4 C and 4 D). Evaluation of drug sensitivity in LAML patients using 198 drugs revealed significant discrepancies in IC50 values between high- and low-risk groups for 6 drugs, namely AZD7762, BI.2536, Daporinad, AZD5582, ULK1_4989, and ABT737 (Fig. 5 A). Notably, the gene FOXP1 displayed a negative correlation with Daporinad, ABT737, and BI.2536, while SIRT2 exhibited a positive correlation with ABT737, BI.2536, and ULK1_4989 (Fig. 5 B and Figure S5). Discussion The aberrant activation of tumor angiogenesis is a critical factor in tumor metastasis and growth [ 10 ]. Our study identified five angiogenesis-related genes associated with LAML prognosis using univariate Cox regression and LASSO analyses: ECM1, EGLN1, FKBP5, FOXP1, and SIRT2. Kaplan-Meier analyses further confirmed that elevated levels of ECM1, EGLN1, FKBP5, and SIRT2, coupled with diminished expression of FOXP1, were associated with unfavorable prognoses. ECM1, a secreted protein, is involved in cellular proliferation, angiogenesis, and differentiation [ 19 ]. Elevated ECM1 levels have been observed in various human carcinomas, including breast ductal carcinoma, colorectal cancer, esophageal squamous carcinoma, and gastric cancer [ 19 ]. These elevated levels are often associated with poor prognosis and increased metastatic potential [ 19 – 21 ]. EGLN1, also known as PHD2, has emerged as a promising therapeutic target for breast cancer due to its mitochondrial accumulation under hypoxic conditions, which contributes to metabolic homeostasis and tumor growth [ 22 ]. Bonezzi et al. demonstrated SIRT2's role in facilitating cell motility and its regulatory effects on pathological processes such as tumor invasion, metastasis, and angiogenesis [ 23 ]. These findings align with our current observations regarding ECM1, EGLN1, and SIRT2. Interestingly, upregulated FKBP5 has been linked to improved survival in luminal B subtype breast cancer [ 24 ]. Similarly, while FOXP1 overexpression is associated with shorter overall survival in cutaneous melanoma [ 25 ], it correlates with favorable prognosis in squamous cell/adenosquamous carcinomas [ 26 ]. These variations may be attributed to the complex and tumor-specific molecular regulation. We constructed a prognostic signature for LAML patients based on these five angiogenesis-associated genes, revealing that higher risk scores correlated with poorer prognoses. Subsequently, we identified this risk score as an independent prognostic factor for LAML patients. To our knowledge, this study presents the first prognostic marker based on angiogenesis-associated genes in LAML patients. Current risk stratification in AML primarily relies on cytogenetics and common genetic abnormalities (e.g., NPM1, FLT3-ITD, CEBPA, RUNX1, ASXL1, and TP53 mutations) [ 27 ], validated in both young and older patients. However, as the global population ages, there is an increasing need for precise prognostic indices tailored for older adults with AML [ 28 ]. Our prognostic signature could serve as a valuable complement to existing prognostic assessments in LAML. Further analysis of genes closely related to the risk score revealed that high-risk samples are primarily enriched in immune-related pathways. Moreover, we found significant correlations between the five angiogenesis-associated genes and the tumor immune microenvironment. With recent advancements in immunotherapy, including checkpoint blockade, immune cellular therapies, and vaccines, AML patient prognoses have significantly improved [ 29 ]. However, some patients fail to respond to immunotherapy, underscoring the importance of identifying biomarkers that can accurately predict treatment response and guide patient selection. Our prognostic signature has the potential to offer valuable insights into forecasting patient outcomes and identifying suitable candidates for immunotherapy. This study has several limitations. First, it primarily relied on public databases and followed a retrospective approach. The limited availability of prognostic information datasets for LAML patients may have restricted our analysis of clinical parameters. Second, our focus on angiogenesis-associated molecules may not fully capture the spatial heterogeneity of the tumor microenvironment, potentially limiting the effectiveness of the prognostic features. Future prospective studies focusing on LAML patients undergoing immunotherapy are crucial to validate the clinical significance of our signature. Conclusion In conclusion, this study firstly comprehensive analyze the prognostic significance of angiogenesis-associated genes in LAML and identified 5 genes, namely ECM1, EGLN1, FKBP5, FOXP1, and SIRT2. We further constructed and validated a novel prognostic signature of LAML based on the 5 angiogenesis-associated genes, that classifies patients into high- and low-risk subgroups. Patients with high risk displayed poor prognosis and elevated levels of immune cell infiltration. Collectively, this work enriches the prognostic assessment system and provides a novel therapeutic direction for LAML. Declarations Funding This work was supported by Project of Yancheng Medical Science and Technology Development Plan (No. YK2020004). Author contribution statement Yuqing Miao designed this work. Can Chen and Yongfen Huang collected the data, performed data analysis, and drafted and revised the manuscript. Lingling Wang, Linlin Zhang, Jinbo Lu, and Yuexin Cheng performed data analysis. All authors approved the final manuscript. Data availability Gene expression datasets and Cancer Genome Atlas database are publicly available (GEO, https://www.ncbi.nlm.nih.gov/geo and TCGA, https://www.cancer.gov/ccg/research/genome-sequencing/tcga). Further inquiries can be directed to the corresponding author. Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Declaration Ethics, Consent to Participate, and Consent to Publish declarations: not applicable References Almatani MF, Ali A, Onyemaechi S, Zhao Y, Gutierrez L, Vaikari VP, Alachkar H (2021) Strategies targeting FLT3 beyond the kinase inhibitors. 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Supplementary Files FigureS1.tif FigureS2.tif FigureS3.tif FigureS4.png FigureS5.tif Cite Share Download PDF Status: Published Journal Publication published 17 Jul, 2025 Read the published version in Annals of Hematology → Version 1 posted Editorial decision: Revision requested 25 Apr, 2025 Reviews received at journal 05 Oct, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviewers invited by journal 15 Sep, 2024 Editor assigned by journal 04 Sep, 2024 Submission checks completed at journal 04 Sep, 2024 First submitted to journal 02 Sep, 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-5015900","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":362648892,"identity":"44607c44-b544-4bc6-8caa-158b3c2cf7bc","order_by":0,"name":"Can Chen","email":"","orcid":"","institution":"Yancheng Clinical College, Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Can","middleName":"","lastName":"Chen","suffix":""},{"id":362648893,"identity":"36731ce3-0f21-45fb-87ad-977f122a9123","order_by":1,"name":"Yongfen Huang","email":"","orcid":"","institution":"Yancheng Clinical College, Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongfen","middleName":"","lastName":"Huang","suffix":""},{"id":362648894,"identity":"a12fa467-109c-4f80-805a-d4dee5e4e58a","order_by":2,"name":"Lingling Wang","email":"","orcid":"","institution":"Yancheng Clinical College, Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingling","middleName":"","lastName":"Wang","suffix":""},{"id":362648895,"identity":"d0da27dd-d97c-438c-abc1-0782cf6247e3","order_by":3,"name":"Linlin Zhang","email":"","orcid":"","institution":"Yancheng Clinical College, Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Zhang","suffix":""},{"id":362648896,"identity":"8b970c7c-637d-4843-9cc2-12c3cf5e8105","order_by":4,"name":"Jinbo Lu","email":"","orcid":"","institution":"Yancheng Clinical College, Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinbo","middleName":"","lastName":"Lu","suffix":""},{"id":362648897,"identity":"0b633808-1c38-4521-bf8c-f7dac2814a05","order_by":5,"name":"Yuexin Cheng","email":"","orcid":"","institution":"Yancheng Clinical College, Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuexin","middleName":"","lastName":"Cheng","suffix":""},{"id":362648898,"identity":"ca4f2493-c334-47bf-b3e9-27051162f639","order_by":6,"name":"Yuqing Miao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDACZhBhAGYyPkioqCFNC7PBgzPHSLOQTfJhCzNhZbrtzA8f3Siwk9OdkbytIrGBjYG/vTsBrxazw2zGxjkGycZmN9LKbiTukGGQOHN2AwEtDGbSOQbMidtu5JjdSDzDxmAgkUtIC/s3oJZ6sJaCxDZmYrTwgGw5DNbCQKyWYqBfjhubnXlWLJFw5hgPYb+cP77xcc6fajmz48kbP/6oqJHjb+/FrwUBBBLAEcpDpHIQ4D9gQILqUTAKRsEoGEkAAHpWSIz2SB/cAAAAAElFTkSuQmCC","orcid":"","institution":"Yancheng Clinical College, Xuzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Miao","suffix":""}],"badges":[],"createdAt":"2024-09-02 06:42:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5015900/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5015900/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00277-025-06454-3","type":"published","date":"2025-07-17T16:05:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67112826,"identity":"87d0eb5c-bea4-4dc3-951b-12c20d238b3a","added_by":"auto","created_at":"2024-10-21 09:55:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2185634,"visible":true,"origin":"","legend":"\u003cp\u003eThe intricate diagram depicting the framework of the current research. (A) Gene expression prolife was downloaded from the Gene Expression Omnibus (GEO) database (GSE37642, Training set) and Cancer Genome Atlas (TCGA) database (TCGA_LAML, Validation set). Angiogenesis-associated genes were obtained from the GeneCards database. Elderly patients with Acute myeloid leukemia (LAML) survival related angiogenesis-associated genes were identified and their correlations with tumor immune microenvironment and drug sensitivity were evaluated. The Kaplan-Meier curves for the 5 angiogenesis-associated genes in LAML from (B) GSE37642 dataset and (C) TCGA_LAML dataset, containing ECM1, EGLN1, FKBP5, FOXP1, and SIRT2.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/7361486f59d91eb9f7fc48f8.png"},{"id":67112821,"identity":"833b50f9-d40e-487f-ac77-55d38b46cfb5","added_by":"auto","created_at":"2024-10-21 09:55:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1971085,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus clustering based on 5 angiogenesis-associated genes. (A) Consensus clustering cumulative distribution function for k = 2-6. (B) The relative change in area under the cumulative distribution function curve for k = 2-6. (C) Kaplan–Meier curve for two clusters of LAML. (D) The Kaplan-Meier curve for the risk score of LAML from GSE37642 dataset. (E) The multivariate Cox regression analyses of prognosis for the prognostic signature and clinic characteristics. (F) The violin plot of risk score in different clinical subgroups.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/de6f53db11d7b113e1b87ce5.png"},{"id":67113402,"identity":"099305b8-4941-4250-bcb4-2b938b8fcbbd","added_by":"auto","created_at":"2024-10-21 10:03:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2812375,"visible":true,"origin":"","legend":"\u003cp\u003eThe 5 angiogenesis-associated genes-based prognostic signature-related biological pathways. (A) Gene Ontology (GO) functional enrichment analysis of angiogenesis-associated genes based on signature. (B) Gene Set Enrichment Analysis (GSEA) based on GO terms. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of angiogenesis-associated genes based on signature. (D) GSEA based on KEGG pathway. (E) Network diagram of biological processes of angiogenesis-associated genes based on signature. (F) Reactomeresult analysis based on KEGG pathway.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/1ed0aca872fee1aba0769ae1.png"},{"id":67113401,"identity":"060a1a8f-d658-411d-a55d-fa3c006be507","added_by":"auto","created_at":"2024-10-21 10:03:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1306144,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between 5 angiogenesis-associated genes and the tumor immune microenvironment. (A) The proportion of 22 types of immune cells in LAML tissues. (B) Scatter plot between tumor purity, stroma, and immune score, and the immune score and tumor purity in high- and low-risk scores. (C) Heatmap of correlations between 5 angiogenesis-associated genes and immune cells. (D) Heatmap of correlations between 5 angiogenesis-associated genes and immune score indicators. The shape of the dot signifies the strength of relevance. A circle represents positive correlation, while a square represents negative correlation. The deeper the color, the stronger the connection implies.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/a4310ba75ef9523801c71fed.png"},{"id":67112830,"identity":"129479bd-f9c8-416b-93b0-6fdb1353cf01","added_by":"auto","created_at":"2024-10-21 09:55:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1753464,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis. (A) Estimated IC50 for different drugs in high and low risk groups. (B) The correlations between angiogenesis-associated genes and drugs.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/c6beaaa4ab752c4cfe562794.png"},{"id":88506112,"identity":"b35d1ed0-e424-4c93-b32c-e8fec181a678","added_by":"auto","created_at":"2025-08-07 07:31:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10346269,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/03d0af47-2b91-4837-ad19-039d4bbbd053.pdf"},{"id":67112822,"identity":"9a6f2569-3e66-404f-8e4b-29ac11aa3a34","added_by":"auto","created_at":"2024-10-21 09:55:17","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1192720,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/e280825483fb4cacfc4e3982.tif"},{"id":67112827,"identity":"9f9d2765-78de-4ba2-8539-6666cda65ddb","added_by":"auto","created_at":"2024-10-21 09:55:17","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":794428,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/c55d2fc28ba2874b79b48afc.tif"},{"id":67113400,"identity":"886a260d-6d53-46cf-846d-a49e5a23cba2","added_by":"auto","created_at":"2024-10-21 10:03:17","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1287028,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/b8307b4f0b3573562348f765.tif"},{"id":67113399,"identity":"991071e3-32e9-46df-84ac-b045f4389586","added_by":"auto","created_at":"2024-10-21 10:03:17","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":103848,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.png","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/a6f69fd1a02e47a7de6813e3.png"},{"id":67112824,"identity":"9d3cdfc2-1798-43d4-90cd-8ee2cfa66de7","added_by":"auto","created_at":"2024-10-21 09:55:17","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":883032,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-5015900/v1/75d77a4dfdc572abc78fdf6f.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic significance of angiogenesis-associated molecules and immunologic characteristic in elderly patients with acute myeloid leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a hematological malignancy characterized by the dysdifferentiation and clonal expansion of myeloid progenitor cells, leading to their accumulation in peripheral blood and bone marrow [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This disease primarily affects the elderly, with a median age of 68 years at diagnosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Significant advances in AML treatment have been achieved through the development of hypomethylating agents (HMAs) such as decitabine and azacitidine [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], as well as combination therapies involving HMAs with venetoclax [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] or ivosidenib in IDH1-mutated AML [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, outcomes for elderly patients with AML (LAML) remain unsatisfactory due to the toxicity associated with intensive chemotherapy and frequent comorbidities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Consequently, there is an urgent need to discover novel therapeutic targets and interventions to improve LAML outcomes.\u003c/p\u003e \u003cp\u003eIn tumors, neovascularization mechanisms are hyperactivated, resulting in vascular dysfunction characterized by high permeability and poor pericellular attachment, which contribute to tumor metastasis and growth [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As such, anti-angiogenic or vessel-targeted therapies have emerged as attractive approaches in combinatorial tumor treatment regimens, including chemotherapy and immunotherapy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While numerous studies have elucidated the role of angiogenesis in solid tumors, recent decades have seen increasing research focused on clarifying its effects in hematological malignancies, including B-cell non-Hodgkin lymphoma [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], acute promyelocytic leukemia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and multiple myeloma [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These investigations have revealed promising applications for anti-angiogenic therapies in hematological malignancies. Moreover, evidence has demonstrated the impact of angiogenesis in AML patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and vascular endothelial growth factor (VEGF)-targeted therapy in combination with conventional chemotherapy has shown improved outcomes in AML patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, to date, there has been no comprehensive analysis investigating the prognostic significance of angiogenesis-associated genes in LAML.\u003c/p\u003e \u003cp\u003eTo address this gap in knowledge, we conducted a thorough examination of the expression patterns and prognostic significance of angiogenesis-associated genes in LAML. We constructed a prognostic signature for LAML that serves as an independent prognostic indicator and reveals distinct immune profiles. Furthermore, we explored the immune microenvironment of diverse LAML patients and categorized them based on the characteristics of angiogenesis-associated genes. Finally, we predicted LAML drug sensitivity and evaluated differences in drug activity based on the prognostic signature. This comprehensive analysis provides valuable insights into the role of angiogenesis-associated genes in LAML prognosis and may inform the development of novel therapeutic strategies for this challenging disease.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eData preparation\u003c/p\u003e \u003cp\u003eThe leukemia gene expression profile GSE37642 was obtained from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while the TCGA_LAML dataset was accessed through the Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/ccg/research/genome-sequencing/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/ccg/research/genome-sequencing/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For prognostic analysis, we selected patients aged 60 years and older, resulting in a cohort of 343 acute myeloid leukemia (AML) samples. The GSE37642 dataset served as the training set, whereas the TCGA_LAML dataset was utilized as the validation set for subsequent analyses. This study employed anonymized, de-identified, publicly available data, thus exempting it from ethics committee approval.\u003c/p\u003e \u003cp\u003eIdentification of angiogenesis-associated gens with prognostic significance in LAML\u003c/p\u003e \u003cp\u003eAngiogenesis-associated genes were extracted from the GeneCards database [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The survminer R package's surv_cutpoint function was employed to determine the optimal cutoff value for each gene across all samples, enabling the classification of samples into high- and low-expression groups. Subsequently, univariate Cox regression analysis was conducted to identify genes significantly associated with survival in the GSE37642 dataset, with validation performed using the TCGA_LAML dataset. Genes demonstrating overlap between survival association and angiogenesis involvement were designated as candidate molecules. The prognostic significance of these selected candidates was evaluated using Kaplan-Meier curves, followed by log-rank tests implemented through the survminer package. To identify the most robust genes for predicting survival in LAML patients, Least Absolute Shrinkage and Selection Operator (LASSO) analysis was performed using the glmnet package. This approach helped mitigate overfitting, and ten-fold cross-validation was utilized to determine the optimal value for the penalty parameter.\u003c/p\u003e \u003cp\u003eConsensus clustering of survival-related angiogenesis-associated genes\u003c/p\u003e \u003cp\u003eTo further elucidate the functional significance and prognostic value of the selected genes in LAML, we employed consensus clustering analysis. This approach utilized the following parameters: 1,000 iterations, k\u0026thinsp;=\u0026thinsp;10, and agglomerative hierarchical clustering with Ward's method (Ward.D2) for inner linkage and complete linkage for outer grouping. We then validated the optimal number of clusters through principal component analysis (PCA) using the \"ggplot2\" R package. Subsequently, we generated Kaplan-Meier survival curves to assess the prognostic implications of the resulting cluster classifications.\u003c/p\u003e \u003cp\u003eConstruction and validation of an angiogenesis-associated prognostic signature\u003c/p\u003e \u003cp\u003eMultivariate Cox proportional hazards regression analysis was employed to derive the coefficients of prognostic-related angiogenesis-associated genes. Subsequently, a risk score for each patient was calculated using these coefficients to construct a prognostic signature. The detailed formula is as follows: Risk score\u0026thinsp;=\u0026thinsp;β1 * Exp1\u0026thinsp;+\u0026thinsp;β2 * Exp2\u0026thinsp;+\u0026thinsp;β, where β and Exp represent coefficients and expression levels of selected genes, respectively. Patients were stratified into high- and low-risk groups based on the median risk score. Kaplan-Meier survival analysis, coupled with log-rank tests, was performed to assess the prognostic value of this classification using the \"survminer\" R package.\u003c/p\u003e \u003cp\u003eFunctional and pathway enrichment analysis\u003c/p\u003e \u003cp\u003eAngiogenesis-associated genes exhibiting a correlation coefficient magnitude (|R|) greater than 0.3 were deemed to have a strong relationship with the risk score. These genes were subsequently utilized to investigate the biological pathways associated with the signature. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted on the identified genes using the clusterProfiler package. To elucidate potential functional mechanisms, Gene Set Enrichment Analysis (GSEA) was performed using the c2.cp.v7.2.symbols.gtm file. The threshold for significance was set at a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.2 and a normalized P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eAssessment of the immune microenvironment composition and drug sensitivity\u003c/p\u003e \u003cp\u003eIn our analysis, we employed the CIBERSORT algorithm implemented in R to evaluate the infiltration patterns of 22 immune cell types in LAML tissues from the TCGA_LAML dataset. We considered only results with a statistical significance of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for determining the relative abundance of infiltrating immune cells. To assess differential immune cell infiltration between high- and low-risk LAML tissues, we applied the Wilcoxon rank-sum test. Furthermore, we investigated the correlations between the selected prognostic genes and infiltrating immune cells using Spearman's rank correlation analysis. We also compared tumor purity across samples using the estimate package in R and visualized the results using the ggplot2 and pheatmap packages. Lastly, we predicted LAML drug sensitivity and evaluated differences in drug activity across risk score groups using the oncoPredict package in R.\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eAll statistical analysis and data visualization were performed using R program. Differences between two groups were compared using t-tests or Wilcoxon tests, and more than two groups were compared using Kruskal Wallis test. Kaplan-Meier curves followed by log-rank tests were employed for prognostic analysis. Univariate and multivariate Cox regression models was utilized to evaluated prognostic value of angiogenesis-associated genes. Pearson\u0026rsquo;s chi-square test was applied to determine variations in the distribution of clinical variables in LAML. A P-value less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIdentification of angiogenesis-associated gens with prognostic significance in LAML\u003c/p\u003e \u003cp\u003eThe flow chart of this work was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. A total of 233 genes were found to be significantly associated with prognosis of LAML (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from GSE37642 dataset, and 1137 angiogenesis-associated genes were obtained from the GeneCards database, containing 8 genes significantly associated with prognosis of LAML. Thus, we selected these 8 genes as candidate targets, which were further screened using LASSO analysis and verified using TCGA_LAML dataset. We ultimately identified 5 angiogenesis-related genes that associated with prognosis (Figures S1 and S2), namely ECM1, EGLN1, FKBP5, FOXP1, and SIRT2. Further Kaplan-Meier curves delineated that high expression of ECM1, EGLN1, FKBP5, and SIRT2, whereas low expression of FOXP1 were related to worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCluster classification was implicated with the malignancy of LAML\u003c/p\u003e \u003cp\u003eTo investigate the overall prognostic value of these 5 genes, we conducted a consensus clustering analysis to classify LAML patients, showing that k\u0026thinsp;=\u0026thinsp;2 appeared to be a more stable value than that of k\u0026thinsp;=\u0026thinsp;3\u0026ndash;6 (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). PCA was further employed to validate the reliability of the cluster, indicating that, when k\u0026thinsp;=\u0026thinsp;3\u0026ndash;5, the samples showed high similarity and clustered together (Figure S3). Thus, we stratified the LAML patients into 2 clusters. The Kaplan-Meier curves demonstrated that patients in cluster 1 had a poor prognosis than those in cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConstruction and validation of an angiogenesis-associated prognostic signature\u003c/p\u003e \u003cp\u003eRisk scores for prognostic signature of LAML patients were constructed based on the coefficients and expression levels of 5 selected genes. The detailed formula was shown below: Risk score\u0026thinsp;=\u0026thinsp;0.5285351*ECM1\u0026thinsp;+\u0026thinsp;0.2861940*EGLN1\u0026thinsp;+\u0026thinsp;0.3392907*FKBP5-0.5082985*FOXP1\u0026thinsp;+\u0026thinsp;1.0852212*SIRT2. The patients were classified into low- and high-risk groups based on the median risk score, and Kaplan-Meier curves showed that patients with high-risk had worse prognosis compared with those with low-risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The prognostic characteristics were then validated in TCGA_LAML dataset, showing the similar result of GSE37642 dataset (Figure S4). These results demonstrate the reliability and stability of the prognostic characteristics. Further analysis identified risk score as an independent risk factor for prognosis of LAML patients (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIdentification of the prognostic signature-related biological pathways\u003c/p\u003e \u003cp\u003eWe selected genes closely related to the risk score to explore potential biological pathways associated with prognostic signature, including 915 positively correlated genes and 187 negatively correlated genes. The enrichment analysis results of GO and KEGG pathways are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eE. The GO analysis primarily enriched in actin filament organization, while the KEGG analysis primarily enriched in salmonella infection. The results of GSEA analysis show that high-risk samples are mainly enriched in immune-related pathways (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe associates with the tumor immune microenvironment and drug sensitivity\u003c/p\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, remarkable disparities in the infiltration of immune cells can be observed between high- and low-risk patients, with intricate variations in the proportions of various immune cells. Subsequent analysis unveiled that, in comparison to high-risk patients, low-risk patients exhibited reduced immune scores and heightened tumor purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Correlation analysis demonstrated a robust association between 5 selected angiogenesis-associated genes with and immune cells, potentially implicated in the regulation of patient prognosis (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Evaluation of drug sensitivity in LAML patients using 198 drugs revealed significant discrepancies in IC50 values between high- and low-risk groups for 6 drugs, namely AZD7762, BI.2536, Daporinad, AZD5582, ULK1_4989, and ABT737 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Notably, the gene FOXP1 displayed a negative correlation with Daporinad, ABT737, and BI.2536, while SIRT2 exhibited a positive correlation with ABT737, BI.2536, and ULK1_4989 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Figure S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aberrant activation of tumor angiogenesis is a critical factor in tumor metastasis and growth [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our study identified five angiogenesis-related genes associated with LAML prognosis using univariate Cox regression and LASSO analyses: ECM1, EGLN1, FKBP5, FOXP1, and SIRT2. Kaplan-Meier analyses further confirmed that elevated levels of ECM1, EGLN1, FKBP5, and SIRT2, coupled with diminished expression of FOXP1, were associated with unfavorable prognoses. ECM1, a secreted protein, is involved in cellular proliferation, angiogenesis, and differentiation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Elevated ECM1 levels have been observed in various human carcinomas, including breast ductal carcinoma, colorectal cancer, esophageal squamous carcinoma, and gastric cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These elevated levels are often associated with poor prognosis and increased metastatic potential [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. EGLN1, also known as PHD2, has emerged as a promising therapeutic target for breast cancer due to its mitochondrial accumulation under hypoxic conditions, which contributes to metabolic homeostasis and tumor growth [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Bonezzi et al. demonstrated SIRT2's role in facilitating cell motility and its regulatory effects on pathological processes such as tumor invasion, metastasis, and angiogenesis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings align with our current observations regarding ECM1, EGLN1, and SIRT2. Interestingly, upregulated FKBP5 has been linked to improved survival in luminal B subtype breast cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, while FOXP1 overexpression is associated with shorter overall survival in cutaneous melanoma [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], it correlates with favorable prognosis in squamous cell/adenosquamous carcinomas [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These variations may be attributed to the complex and tumor-specific molecular regulation.\u003c/p\u003e \u003cp\u003eWe constructed a prognostic signature for LAML patients based on these five angiogenesis-associated genes, revealing that higher risk scores correlated with poorer prognoses. Subsequently, we identified this risk score as an independent prognostic factor for LAML patients. To our knowledge, this study presents the first prognostic marker based on angiogenesis-associated genes in LAML patients. Current risk stratification in AML primarily relies on cytogenetics and common genetic abnormalities (e.g., NPM1, FLT3-ITD, CEBPA, RUNX1, ASXL1, and TP53 mutations) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], validated in both young and older patients. However, as the global population ages, there is an increasing need for precise prognostic indices tailored for older adults with AML [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our prognostic signature could serve as a valuable complement to existing prognostic assessments in LAML.\u003c/p\u003e \u003cp\u003eFurther analysis of genes closely related to the risk score revealed that high-risk samples are primarily enriched in immune-related pathways. Moreover, we found significant correlations between the five angiogenesis-associated genes and the tumor immune microenvironment. With recent advancements in immunotherapy, including checkpoint blockade, immune cellular therapies, and vaccines, AML patient prognoses have significantly improved [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, some patients fail to respond to immunotherapy, underscoring the importance of identifying biomarkers that can accurately predict treatment response and guide patient selection. Our prognostic signature has the potential to offer valuable insights into forecasting patient outcomes and identifying suitable candidates for immunotherapy.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it primarily relied on public databases and followed a retrospective approach. The limited availability of prognostic information datasets for LAML patients may have restricted our analysis of clinical parameters. Second, our focus on angiogenesis-associated molecules may not fully capture the spatial heterogeneity of the tumor microenvironment, potentially limiting the effectiveness of the prognostic features. Future prospective studies focusing on LAML patients undergoing immunotherapy are crucial to validate the clinical significance of our signature.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study firstly comprehensive analyze the prognostic significance of angiogenesis-associated genes in LAML and identified 5 genes, namely ECM1, EGLN1, FKBP5, FOXP1, and SIRT2. We further constructed and validated a novel prognostic signature of LAML based on the 5 angiogenesis-associated genes, that classifies patients into high- and low-risk subgroups. Patients with high risk displayed poor prognosis and elevated levels of immune cell infiltration. Collectively, this work enriches the prognostic assessment system and provides a novel therapeutic direction for LAML.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Project of Yancheng Medical Science and Technology Development Plan (No. YK2020004).\u003c/p\u003e\n\u003cp\u003eAuthor contribution statement\u003c/p\u003e\n\u003cp\u003eYuqing Miao designed this work. Can Chen and Yongfen Huang collected the data, performed data analysis, and drafted and revised the manuscript. Lingling Wang, Linlin Zhang, Jinbo Lu, and Yuexin Cheng performed data analysis. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eData availability \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGene expression datasets and Cancer Genome Atlas database are publicly available (GEO, https://www.ncbi.nlm.nih.gov/geo and TCGA, https://www.cancer.gov/ccg/research/genome-sequencing/tcga). Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eDeclaration\u003c/p\u003e\n\u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations: not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmatani MF, Ali A, Onyemaechi S, Zhao Y, Gutierrez L, Vaikari VP, Alachkar H (2021) Strategies targeting FLT3 beyond the kinase inhibitors. 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J Geriatr Oncol 14(7):101582\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVago L, Gojo I (2020) Immune escape and immunotherapy of acute myeloid leukemia. J Clin Invest 130(4):1552\u0026ndash;1564\u003c/span\u003e\u003c/li\u003e\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":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Elderly patients with acute myeloid leukemia, Angiogenesis-related genes, Prognostic signature, Immune microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-5015900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5015900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNeovascularization mechanisms are hyperactivated in tumors, leading to vascular dysfunction and contributing to tumor metastasis and growth. This study aims to comprehensively analyze angiogenesis-associated genes in relation to the prognosis of elderly patients with acute myeloid leukemia (AML). Methods: Leukemia gene expression data were obtained from the GSE37642 (training set) and TCGA_LAML (validation set) datasets. Angiogenesis-associated genes were identified using the GeneCards database. Univariate Cox regression and LASSO analyses were employed to identify angiogenesis-associated genes linked to AML prognosis. A prognostic signature was constructed based on the selected genes, and its biological functions were analyzed. Finally, we predicted AML drug sensitivity and evaluated differences in drug activity based on the prognostic signature. Results: Five angiogenesis-related genes associated with AML prognosis were identified: ECM1, EGLN1, FKBP5, FOXP1, and SIRT2. Kaplan-Meier analyses confirmed their prognostic value. A prognostic signature based on these genes demonstrated commendable efficacy in predicting patient outcomes. This signature was found to be an independent risk factor for AML and revealed distinct immune profiles. Furthermore, the signature was implicated in the tumor immune microenvironment, with high-risk patients exhibiting elevated levels of immune cell infiltration. Drug sensitivity analysis revealed negative correlations between FOXP1 and Daporinad, ABT737, and BI.2536, while SIRT2 showed positive correlations with ABT737, BI.2536, and ULK1_4989. Conclusion: We have constructed an angiogenesis-related gene prognostic signature that enriches the prognostic assessment system for AML and provides novel therapeutic directions for this disease.\u003c/p\u003e","manuscriptTitle":"Prognostic significance of angiogenesis-associated molecules and immunologic characteristic in elderly patients with acute myeloid leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-21 09:55:12","doi":"10.21203/rs.3.rs-5015900/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-26T00:27:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-05T20:22:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261060903927863202787797821821305359573","date":"2024-09-17T00:01:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-16T01:27:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-04T11:30:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-04T11:29:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2024-09-02T06:40:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"615e184a-66ed-4232-876f-9966de93d737","owner":[],"postedDate":"October 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T07:11:26+00:00","versionOfRecord":{"articleIdentity":"rs-5015900","link":"https://doi.org/10.1007/s00277-025-06454-3","journal":{"identity":"annals-of-hematology","isVorOnly":false,"title":"Annals of Hematology"},"publishedOn":"2025-07-17 16:05:37","publishedOnDateReadable":"July 17th, 2025"},"versionCreatedAt":"2024-10-21 09:55:12","video":"","vorDoi":"10.1007/s00277-025-06454-3","vorDoiUrl":"https://doi.org/10.1007/s00277-025-06454-3","workflowStages":[]},"version":"v1","identity":"rs-5015900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5015900","identity":"rs-5015900","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}