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pmid:34135913

Prognosis and Characterization of Immune Microenvironment in Acute Myeloid Leukemia Through Identification of an Autophagy-Related Signature.

Denggang Fu · Biyu Zhang · Shiyong Wu · Yinghua Zhang · Jingwu Xie · Wangbin Ning · Hua Jiang

Frontiers in immunology · 2021 doi:10.3389/fimmu.2021.695865

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:34135913
kind
science-record
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sha256:5c6704016a74b72fd6a0e31163151538aa0c5bff53ab2ae612f2862f6a8f97d3
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T15:41:39Z
updated_at
2026-11-01T22:03:59Z
generator object · 2 fields
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paper object · 10 fields
state
complete
title
Prognosis and Characterization of Immune Microenvironment in Acute Myeloid Leukemia Through Identification of an Autophagy-Related Signature.
doi
10.3389/fimmu.2021.695865
pmid
34135913
authors array · 7 items
[0]
Denggang Fu
[1]
Biyu Zhang
[2]
Shiyong Wu
[3]
Yinghua Zhang
[4]
Jingwu Xie
[5]
Wangbin Ning
[6]
Hua Jiang
journal
Frontiers in immunology
year
2021
abstract
Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies that has an unfavorable outcome and a high rate of relapse. Autophagy plays a vital role in the development of and therapeutic responses to leukemia. This study identifies a potential autophagy-related signature to monitor the prognoses of patients of AML. Transcriptomic profiles of AML patients (GSE37642) with the relevant clinical information were downloaded from Gene Expression Omnibus (GEO) as the training set while TCGA-AML and GSE12417 were used as validation cohorts. Univariate regression analyses and multivariate stepwise Cox regression analysis were respectively applied to identify the autophagy-related signature. The univariate Cox regression analysis identified 32 autophagy-related genes (ARGs) that were significantly associated with the overall survival (OS) of the patients, and were mainly rich in signaling pathways for autophagy, p53, AMPK, and TNF. A prognostic signature that comprised eight ARGs (BAG3, CALCOCO2, CAMKK2, CANX, DAPK1, P4HB, TSC2, and ULK1) and had good predictive capacity was established by LASSO-Cox stepwise regression analysis. High-risk patients were found to have significantly shorter OS than patients in low-risk group. The signature can be used as an independent prognostic predictor after adjusting for clinicopathological parameters, and was validated on two external AML sets. Differentially expressed genes analyzed in two groups were involved in inflammatory and immune signaling pathways. An analysis of tumor-infiltrating immune cells confirmed that high-risk patients had a strong immunosuppressive microenvironment. Potential druggable OS-related ARGs were then investigated through protein-drug interactions. This study provides a systematic analysis of ARGs and develops an OS-related prognostic predictor for AML patients. Further work is needed to verify its clinical utility and identify the underlying molecular mechanisms in AML.
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OS-related ARGs with P < 0.05 were identified using univariate Cox hazard regression analysis.
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sections[5] "Identification of Overall Survival (OS)-Related ARGs" paragraph 2
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OS-related ARGs with P < 0.05 were identified using univariate Cox hazard regression analysis.
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The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding authors.
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sections[23] "Data Availability Statement" paragraph 2
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The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding authors.
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the published estimate and its evidence
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Overall survival-related ARGs in the AML patients (P < 0.05).
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Identification and Functional Enrichment Analysis of OS-Related ARGs in AML
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Table 1
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Overall survival-related ARGs in the AML patients (P < 0.05).
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HR (95% CI)
[2]
P value
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[0]
TSC2
[1]
0.4072(0.2832 - 0.5856)
[2]
1.25E-06
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[0]
CALCOCO2
[1]
0.6758(0.5680 - 0.8039)
[2]
9.69E-06
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[0]
DAPK1
[1]
1.3835(1.1847 - 1.6157)
[2]
4.10E-05
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[0]
BAG3
[1]
1.1503(1.0753 - 1.2305)
[2]
4.72E-05
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[0]
CAMKK2
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0.5397(0.3882 - 0.7504)
[2]
0.000244719
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[0]
CANX
[1]
0.7301(0.6099 - 0.8739)
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0.000606334
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ULK1
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0.7257(0.6025 - 0.8742)
[2]
0.000735293
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P4HB
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[2]
0.000790605
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0.002205099
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[2]
0.002507313
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0.003127288
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0.004684677
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GAPDH
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0.007994922
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0.009954145
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0.015041995
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0.016217681
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0.017994775
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0.023876677
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ERN1
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0.6722(0.4728 - 0.9558)
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0.026995358
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0.028824089
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0.03365361
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0.03374212
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0.034038805
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0.046832376
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Eight OS-related ARGs targeted by the drugs available from DrugBank.
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Multiple Survival-Related ARGs Are Potential Druggable Targets
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Table 2
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Eight OS-related ARGs targeted by the drugs available from DrugBank.
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CASP3
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0.8846(0.7838-0.9983)
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11
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DB08497, DB08229, DB08213, DB07696, DB01017, DB06862, DB05408, DB03124, DB08498, DB08251, DB08499
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EMRICASAN, MINOCYCLINE, METHYL (3S)-3-[(TERT-BUTOXYCARBONYL]-4-OXOPENTANOATE, 2-HYDROXY-5-(2-MERCAPTO-ETHYLSUFAMOYL)-BENZOIC ACID
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Diphthamide, Glycinamide, Guanosine-5’-Diphosphate, Adenosine-5-Diphosphoribose
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GAPDH
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0.00799
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4
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DB03893,DB00157,DB07347
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Thionicotinamide-Adenine-Dinucleotide,NADH,4-(2-Aminoethyl)Benzenesulfonyl Fluoride
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0.8331(0.7073-0.9813)
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0.02882
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3
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DB07627,DB04276,DB04653
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CBZ-LEU-LEU-TYR-CH2F
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0.00004
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3
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DB04069,DB04395,DB07444
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5,6-Dihydro-Benzo[H]Cinnolin-3-Ylamine
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0.9062(0.8370-0.9811)
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0.01504
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3
[4]
DB01998,DB05481,DB03345
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Recombinant alpha 1-antitrypsin,Mercaptoethanol
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0.7301(0.6099-0.8739)
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0.00061
[3]
2
[4]
DB00031,DB00025
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Tenecteplase, Antihemophilic factor
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1.1482(1.0510-1.2544)
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0.00221
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2
[4]
DB01055,DB01406
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Mimosine, Danazol
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OS-related ARGs with P < 0.05 were identified using univariate Cox hazard regression analysis.
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gnaling pathway, AMPK signaling pathway, and apoptosis ( Figure 1B ). Overall survival-related ARGs
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required_operations array · 1 item
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cox_ph
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dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
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order_basis
consumes_earlier_output
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34135913", "kind": "science-record", "revision": 7, "content_hash": "sha256:5c6704016a74b72fd6a0e31163151538aa0c5bff53ab2ae612f2862f6a8f97d3", "created_at": "2026-11-01T15:41:39Z", "updated_at": "2026-11-01T22:03:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognosis and Characterization of Immune Microenvironment in Acute Myeloid Leukemia Through Identification of an Autophagy-Related Signature.", "doi": "10.3389/fimmu.2021.695865", "pmid": "34135913", "authors": ["Denggang Fu", "Biyu Zhang", "Shiyong Wu", "Yinghua Zhang", "Jingwu Xie", "Wangbin Ning", "Hua Jiang"], "journal": "Frontiers in immunology", "year": 2021, "abstract": "Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies that has an unfavorable outcome and a high rate of relapse. Autophagy plays a vital role in the development of and therapeutic responses to leukemia. This study identifies a potential autophagy-related signature to monitor the prognoses of patients of AML. Transcriptomic profiles of AML patients (GSE37642) with the relevant clinical information were downloaded from Gene Expression Omnibus (GEO) as the training set while TCGA-AML and GSE12417 were used as validation cohorts. Univariate regression analyses and multivariate stepwise Cox regression analysis were respectively applied to identify the autophagy-related signature. The univariate Cox regression analysis identified 32 autophagy-related genes (ARGs) that were significantly associated with the overall survival (OS) of the patients, and were mainly rich in signaling pathways for autophagy, p53, AMPK, and TNF. A prognostic signature that comprised eight ARGs (BAG3, CALCOCO2, CAMKK2, CANX, DAPK1, P4HB, TSC2, and ULK1) and had good predictive capacity was established by LASSO-Cox stepwise regression analysis. High-risk patients were found to have significantly shorter OS than patients in low-risk group. The signature can be used as an independent prognostic predictor after adjusting for clinicopathological parameters, and was validated on two external AML sets. Differentially expressed genes analyzed in two groups were involved in inflammatory and immune signaling pathways. An analysis of tumor-infiltrating immune cells confirmed that high-risk patients had a strong immunosuppressive microenvironment. Potential druggable OS-related ARGs were then investigated through protein-drug interactions. This study provides a systematic analysis of ARGs and develops an OS-related prognostic predictor for AML patients. Further work is needed to verify its clinical utility and identify the underlying molecular mechanisms in AML.", "claims": [{"id": "claim-method", "statement": "OS-related ARGs with P < 0.05 were identified using univariate Cox hazard regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34135913", "locator": "sections[5] \"Identification of Overall Survival (OS)-Related ARGs\" paragraph 2", "quote": "OS-related ARGs with P < 0.05 were identified using univariate Cox hazard regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34135913", "locator": "sections[23] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding authors."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34135913"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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