Dataset Information

An IDO1-related immune gene signature predicts overall survival in acute myeloid leukemia.

ABSTRACT: 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.

SUBMITTER: Ragaini S

PROVIDER: S-EPMC8753212 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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An IDO1-related immune gene signature predicts overall survival in acute myeloid leukemia.

Ragaini Simone S Wagner Sarah S Marconi Giovanni G Parisi Sarah S Sartor Chiara C Nanni Jacopo J Cristiano Gianluca G Talami Annalisa A Olivi Matteo M Ocadlikova Darina D Ciciarello Marilena M Corradi Giulia G Ottaviani Emanuela E Papayannidis Cristina C Paolini Stefania S Vadakekolathu Jayakumar J Cavo Michele M Rutella Sergio S Curti Antonio A

Blood advances 20220101 1

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 retriev ...[more]

PMID: 34535017

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Project description:BackgroundThe immune response in the bone marrow microenvironment has implications for progression and prognosis in acute myeloid leukemia (AML). However, few immune-related biomarkers for AML prognosis and immunotherapy response have been identified. We aimed to establish a predictive gene signature and to explore the determinants of prognosis in AML.MethodsImmune-related genes with clinical significance were screened by a weighted gene co-expression network analysis. Seven immune-related genes were used to establish a gene signature by a multivariate Cox regression analysis. Based on the signature, low- and high-risk groups were compared with respect to the immune microenvironment, immune checkpoints, pathway activities, and mutation frequencies. The tumor immune dysfunction and exclusion (TIDE) method was used to predict the response to immune checkpoint blockade (ICB) therapy. The Connectivity Map database was used to explore small-molecule drugs expected to treat high-risk populations.ResultsA seven-gene prognostic signature was used to classify patients into high- and low-risk groups. Prognosis was poorer for patients in the former than in the latter. The high-risk group displayed higher levels of immune checkpoint molecules (LAG3, PD-1, CTLA4, PD-L2, and PD-L1), immune cell infiltration (dendritic cells, T helper 1, and gamma delta T), and somatic mutations (NPM1 and RUNX1). Moreover, hematopoietic stem cell/leukemia stem cell pathways were enriched in the high-risk phenotype. Compared with that in the low-risk group, the lower TIDE score for the high-risk group implied that this group is more likely to benefit from ICB therapy. Finally, some drugs (FLT3 inhibitors and BCL inhibitors) targeting the expression profiles associated with the high-risk group were generated using Connectivity Map.ConclusionThe newly developed immune-related gene signature is an effective biomarker for predicting prognosis in AML and provides a basis, from an immunological perspective, for the development of comprehensive therapeutic strategies.

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.

Project description:Background: Acute myeloid leukemia (AML) is a heterogeneous malignant disease. SLC25A1, the gene encoding mitochondrial carrier subfamily of solute carrier proteins, was reported to be overexpressed in certain solid tumors. However, its expression and value as prognostic marker has not been assessed in AML. Methods: We retrieved RNA profile and corresponding clinical data of AML patients from the Beat AML, TCGA, and TARGET databases (TARGET_AML). Patients in the TCGA cohort were well-grouped into two group based on SLC25A1 and differentially expressed genes were determined between the SLC25A1 high and low group. The expression of SLC25A1 was validated with clinical samples. The survival and apoptosis of two AML cell lines were analyzed with SLC25A1 inhibitor (CTPI-2) treatment. Cox and the least absolute shrinkage and selection operator (LASSO) regression analyses were applied to Beat AML database to identify SLC25A1-associated genes for the construction of a prognostic risk-scoring model. Survival analysis was performed by Kaplan-Meier and receiver operator characteristic curves. Results: Our analysis revealed that high expressed level of SLC25A1 in AML patients correlates with unfavorable prognosis. Moreover, SLC25A1 expression was positively associated with metabolism activity. We further demonstrated that the inhibition of SLC25A1 could inhibit the proliferation and increase the apoptosis of AML cells. In addition, a panel of SLC25A1-associated genes, was identified to construct a prognostic risk-scoring model. This SLC25A1-associated prognostic signature (SPS) is an independent risk factor with high area under curve (AUC) values of receiver operating characteristic (ROC) curves. A high SPS in leukemia patients is associated with poor survival. A Prognostic nomogram including the SPS and other clinical parameters, was constructed and its predictive efficiency was confirmed. Conclusion: We have successfully established a SPS prognostic model that predict outcome and risk stratification in AML. This risk model can be used as an independent biomarker to assess prognosis of AML.

Project description:BackgroundGastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC.MethodsRNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.ResultsA total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment.ConclusionsOur study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

Dataset Information

An IDO1-related immune gene signature predicts overall survival in acute myeloid leukemia.

Publications

An IDO1-related immune gene signature predicts overall survival in acute myeloid leukemia.

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