A 29-gene signature associated with NOX2 discriminates acute myeloid leukemia prognosis and survival.
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ABSTRACT:
SUBMITTER: Ijurko C
PROVIDER: S-EPMC9303675 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionGene expression profile of mitochondrial-related genes is not well deciphered in pediatric acute myeloid leukaemia (AML). We aimed to identify mitochondria-related differentially expressed genes (DEGs) in pediatric AML with their prognostic significance.MethodsChildren with de novo AML were included prospectively between July 2016-December 2019. Transcriptomic profiling was done for a subset of samples, stratified by mtDNA copy number. Top mitochondria-related DEGs were identified and validated by real-time PCR. A prognostic gene signature risk score was formulated using DEGs independently predictive of overall survival (OS) in multivariable analysis. Predictive ability of the risk score was estimated along with external validation in The Tumor Genome Atlas (TCGA) AML dataset.ResultsIn 143 children with AML, twenty mitochondria-related DEGs were selected for validation, of which 16 were found to be significantly dysregulated. Upregulation of SDHC (p<0.001), CLIC1 (p=0.013) and downregulation of SLC25A29 (p<0.001) were independently predictive of inferior OS, and included for developing prognostic risk score. The risk score model was independently predictive of survival over and above ELN risk categorization (Harrell's c-index: 0.675). High-risk patients (risk score above median) had significantly inferior OS (p<0.001) and event free survival (p<0.001); they were associated with poor-risk cytogenetics (p=0.021), ELN intermediate/poor risk group (p=0.016), absence of RUNX1-RUNX1T1 (p=0.027), and not attaining remission (p=0.016). On external validation, the risk score also predicted OS (p=0.019) in TCGA dataset.DiscussionWe identified and validated mitochondria-related DEGs with prognostic impact in pediatric AML and also developed a novel 3-gene based externally validated gene signature predictive of survival.
| S-EPMC9947241 | biostudies-literature
Project description:AimAcute myeloid leukemia (AML) is a heterogeneous disorder with complex genetic basis and adverse prognosis. Cytogenetics risk, somatic mutations and gene expression profiles are important prognostic factors for AML patients. However, accurate stratification of patient prognosis remains an unsolved problem in AML. This study was to to develop a novel gene profile to accurately classify AML patients into subgroups with different survival probabilities.MethodsSurvival-related genes were determined by Kaplan-Meier survival analysis and multivariate analysis using the expression and clinical data of 405 AML patients from Oregon Health & Science University (OHSU) dataset and validated in The Cancer Genome Atlas (TCGA) database. Feature selection was performed by using the Least Absolute Shrinkage and Selection Operator (LASSO) method. With the LASSO model, a prognostic 85-gene score was established and compared with 2 known gene-expression risk scores. The stratification of AML patients was performed by unsupervised hierarchical clustering of 85 gene expression levels to identify clusters of AML patients with different survival probabilities.ResultsThe LASSO model comprising 85 genes was considered as the optimal model based on relatively high area under curve value (0.83) and the minimum mean squared error. The 85-gene score was associated with increased mortality in AML patients. Hierarchical clustering analysis of the 85 genes revealed 3 subgroups of AML patients in the OHSU dataset. The cluster1 AML patients were associated with more female cases, higher percent of bone marrow blast cells, 85-gene score, cytogenetics risk, more frequent FLT3-ITD, DNMT3A, NP1 mutations, less frequent TP53, RUNX1 mutations, poorer overall survival than cluster2 tumors. The 85-gene score had higher AUC (0.75) than the 5-gene risk score and LSC17 score (0.74 and 0.65).ConclusionsThe 85-gene score is superior to the 2 established prognostic gene signatures in the prediction of prognosis of AML patients.
| S-EPMC8020099 | biostudies-literature
Project description:The contribution of the bone marrow (BM) immune microenvironment to acute myeloid leukemia (AML) development is well-known, but its prognostic significance is still elusive. Indoleamine 2,3-dioxygenase 1 (IDO1), which is negatively regulated by the BIN1 proto-oncogene, is an interferon-γ-inducible mediator of immune tolerance. With the aim to develop a prognostic IDO1-based immune gene signature, biological and clinical data of 982 patients with newly diagnosed, nonpromyelocytic AML were retrieved from public datasets and analyzed using established computational pipelines. Targeted transcriptomic profiles of 24 diagnostic BM samples were analyzed using the NanoString's nCounter platform. BIN1 and IDO1 were inversely correlated and individually predicted overall survival. PLXNC1, a semaphorin receptor involved in inflammation and immune response, was the IDO1-interacting gene retaining the strongest prognostic value. The incorporation of PLXNC1 into the 2-gene IDO1-BIN1 score gave rise to a powerful immune gene signature predicting survival, especially in patients receiving chemotherapy. The top differentially expressed genes between IDO1lowand IDO-1high and between PLXNC1lowand PLXNC1high cases further improved the prognostic value of IDO1 providing a 7- and 10-gene immune signature, highly predictive of survival and correlating with AML mutational status at diagnosis. Taken together, our data indicate that IDO1 is pivotal for the construction of an immune gene signature predictive of survival in AML patients. Given the emerging role of immunotherapies for AML, our findings support the incorporation of immune biomarkers into current AML classification and prognostication algorithms.
| S-EPMC8753212 | biostudies-literature
Project description:Pyroptosis is an inflammatory form of programmed necrotic cell death, but its potential prognostic value in acute myeloid leukemia (AML) remains unclear. On the basis of available AML data from TCGA and TARGET databases, a 10-gene signature model was constructed to effectively predict AML prognosis by performing LASSO Cox regression analysis, which showed that patients with a low-risk score had a significantly better prognosis than that of the high-risk group, and receiver operator characteristic (ROC) analysis achieved superior performance in the prognostic model. The model was further well-verified in an external GEO cohort. Multivariable Cox regression analysis showed that, in addition to age, the risk score was an independent poor survival factor for AML patients, and a nomogram model was constructed with high accuracy. Moreover, the high-risk group generally had higher cytolytic activity and increased levels of infiltrating immune cells, including tumor-infiltrating lymphocytes (TILs) and regulatory T cells (Tregs), which could be related to the expression of immune checkpoint genes. Additionally, low-risk AML patients may have a better response from traditional chemotherapeutic drugs. In conclusion, a pyroptosis-related gene signature can independently predict the prognosis of AML patients with sufficient predictive power, and pyroptosis plays an important role in the immune microenvironment of AML, which may be used to develop a new effective therapeutic method for AML in the future.
| S-EPMC9778227 | biostudies-literature
Project description:ObjectiveThe aim of this research was to create a new genetic signature of immune checkpoint-associated genes as a prognostic method for pediatric acute myeloid leukemia (AML).MethodsTranscriptome profiles and clinical follow-up details were obtained in Therapeutically Applicable Research to Generate Effective Treatments (TARGET), a database of pediatric tumors. Secondary data was collected from the Gene Expression Omnibus (GEO) to test the observations. In univariate Cox regression and multivariate Cox regression studies, the expression of immune checkpoint-related genes was studied. A three-mRNA signature was developed for predicting pediatric AML patient survival. Furthermore, the GEO cohort was used to confirm the reliability. A bioinformatics method was utilized to identify the diagnostic and prognostic value.ResultsA three-gene (STAT1, BATF, EML4) signature was developed to identify patients into two danger categories depending on their OS. A multivariate regression study showed that the immune checkpoint-related signature (STAT1, BATF, EML4) was an independent indicator of pediatric AML. By immune cell subtypes analyses, the signature was correlated with multiple subtypes of immune cells.ConclusionIn summary, our three-gene signature can be a useful tool to predict the OS in AML patients.
| S-EPMC8079183 | biostudies-literature
Project description:Treatment responses of patients with acute myeloid leukemia (AML) are known to be heterogeneous, posing challenges for risk scoring and treatment stratification. In this retrospective multi-cohort study, we investigated whether combining pyroptosis- and immune-related genes improves prognostic classification of AML patients. Using a robust gene pairing approach, which effectively eliminates batch effects across heterogeneous patient cohorts and transcriptomic data, we developed an immunity and pyroptosis-related prognostic (IPRP) signature that consists of 15 genes. Using 5 AML cohorts (n = 1327 patients total), we demonstrate that the IPRP score leads to more consistent and accurate survival prediction performance, compared with 10 existing signatures, and that IPRP scoring is widely applicable to various patient cohorts, treatment procedures and transcriptomic technologies. Compared to current standards for AML patient stratification, such as age or ELN2017 risk classification, we demonstrate an added prognostic value of the IPRP risk score for providing improved prediction of AML patients. Our web-tool implementation of the IPRP score and a simple 4-factor nomogram enables practical and robust risk scoring for AML patients. Even though developed for AML patients, our pan-cancer analyses demonstrate a wider application of the IPRP signature for prognostic prediction and analysis of tumor-immune interplay also in multiple solid tumors.
| S-EPMC9522598 | biostudies-literature
Project description:Patients with cytogenetically normal acute myeloid leukemia (CN-AML) show heterogeneous treatment outcomes. We used gene-expression profiling to develop a gene signature that predicts overall survival (OS) in CN-AML. Based on data from 163 patients treated in the German AMLCG 1999 trial and analyzed on oligonucleotide microarrays, we used supervised principal component analysis to identify 86 probe sets (representing 66 different genes), which correlated with OS, and defined a prognostic score based on this signature. When applied to an independent cohort of 79 CN-AML patients, this continuous score remained a significant predictor for OS (hazard ratio [HR], 1.85; P = .002), event-free survival (HR = 1.73; P = .001), and relapse-free survival (HR = 1.76; P = .025). It kept its prognostic value in multivariate analyses adjusting for age, FLT3 ITD, and NPM1 status. In a validation cohort of 64 CN-AML patients treated on CALGB study 9621, the score also predicted OS (HR = 4.11; P < .001), event-free survival (HR = 2.90; P < .001), and relapse-free survival (HR = 3.14, P < .001) and retained its significance in a multivariate model for OS. In summary, we present a novel gene-expression signature that offers additional prognostic information for patients with CN-AML.
| S-EPMC2954679 | biostudies-literature
Project description:(1) Background: Acute myeloid leukemia (AML) is a clonal malignancy with heterogeneity in genomics and clinical outcome. Metabolism reprogramming has been increasingly recognized to play an important role in the leukemogenesis and prognosis in AML. A comprehensive prognostic model based on metabolism signatures has not yet been developed. (2) Methods: We applied Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) normalization to establish a metabolism-related prognostic gene signature based on glycolysis, fatty acid metabolism, and the tricarboxylic acid cycle gene signatures. The Cancer Genome Atlas-Acute Myeloid Leukemia-like (TCGA-LAML) cohort was set as the training dataset for model construction. Three independent AML cohorts (GSE37642, GSE10358, and GSE12417) combined from Gene Expression Omnibus (GEO) datasets and the Beat-AML dataset were retrieved as two validation sets to test the robustness of the model. The transcriptome data and clinic information of the cohorts were enrolled for the analysis. (3) Results: Divided by the median value of the metabolism risk score, the five-year overall survival (OS) of the high-risk and low-risk groups in the training set were 8.2% and 41.3% (p < 0.001), respectively. The five-year OS of the high-risk and low-risk groups in the combined GEO cohort were 25.5% and 37.3% (p = 0.002), respectively. In the Beat-AML cohort, the three-year OS of the high-risk and low-risk groups were 16.2% and 40.2% (p = 0.0035), respectively. The metabolism risk score showed a significantly negative association with the long-term survival of AML. Furthermore, this metabolism risk score was an independent unfavorable factor for OS by univariate analysis and multivariate analysis. (4) Conclusions: Our study constructed a comprehensive metabolism-related signature with twelve metabolism-related genes for the risk stratification and outcome prediction of AML. This novel signature might contribute to a better use of metabolism reprogramming factors as prognostic markers and provide novel insights into potential metabolism targets for AML treatment.
| S-EPMC10815187 | biostudies-literature
Project description:PurposeTo identify and validate the immune-related gene signature in patients with acute myeloid leukemia (AML).MethodsDifferentially expressed genes (DEGs) profiles and survival data were obtained from The Cancer Genome Atlas (TCGA), following screened immune-associated genes from the InnateDB database. Subsequently, the weighted gene co-expression network analysis (WGCNA) was used to detect functional modules, and survival analysis was performed. The least absolute shrinkage and selection operator (LASSO) regression model combined with a partial likelihood-based Cox proportional hazard regression model was applied to select prognostic genes, and the ESTIMATE algorithm was used to construct an immune score-based risk assessment model. Finally, two independent datasets from the Gene Expression Omnibus (GEO) and our clinical data were used for external validation. Moreover, a subpopulation of the immune microenvironment cells was analyzed by the CIBERSORT algorithm, and its related serum indicator was identified by the enzyme-linked immunosorbent assay (ELISA) in clinical samples.ResultsFinally, CTSD, GNB2, CDK6, and WAS were identified as the immune-related gene signature, and the risk stratification model was validated in both the GSE12417 database and our clinical cohort. Furthermore, the fraction of activated mast cells was identified. CIBERSORT algorithm showed that these cells have a positive association with prognosis. In addition, mast cell stimulator IL-33 was markedly decreased in AML patients with poor prognoses.ConclusionA novel immune-related gene signature (CTSD, GNB2, CDK6 and WAS) and its associated plasma indicator (mast cells activator, IL-33) were found to have prognostic value in AML patients.
| S-EPMC10331686 | biostudies-literature
Project description:BackgroundAcute myeloid leukemia (AML) is a clonal malignant disease with poor prognosis and a low overall survival rate. Although many studies on the treatment and detection of AML have been conducted, the molecular mechanism of AML development and progression has not been fully elucidated. The present study was designed to pursuit the molecular mechanism of AML using a comprehensive bioinformatics analysis, and build an applicable model to predict the survival probability of AML patients in clinical use.MethodsTo simplify the complicated regulatory networks, we performed the gene co-expression and PPI network based on WGCNA and STRING database using modularization design. Two machine learning methods, A least absolute shrinkage and selector operation (LASSO) algorithm and support vector machine-recursive feature elimination (SVM-RFE), were used to filter the common hub genes by five-fold cross-validation. The candidate hub genes were used to build the predictive model of AML by the cox-proportional hazards analysis, and validated in The Cancer Genome Atlas (TCGA) cohort and ohsu cohort, which were reliable in the experimental verification by qRT-PCR and western blotting in mRNA and protein levels.ResultsThree hub genes, FLT3, CD177 and TTPAL were used to build a clinically applicable model to predict the survival probability of AML patients and divided them into high and low groups. To compare the survival ability of the model with the classical clinical features, we generated the nomogram. The model displayed the most risk points contrast to other clinical characteristics, which was compatible with the data of cox multivariate regression.ConclusionThis study reveal the novel molecular mechanism of AML, and construct a clinical model significantly related to AML patient prognosis. We showed the integrated roles of critical pathways, hub genes associated, which provide potential targets and new research ideas for the treatment and early detection of AML.
| S-EPMC7655922 | biostudies-literature