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pmid:39135956

Prognostic role of chemokine-related genes in acute myeloid leukemia.

Yanfei Hou · Yu Chen · Yaofang Zhang · Mengyao Li · Jianfang Chen

PeerJ · 2024 doi:10.7717/peerj.17862

Paper

complete

Experiment

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Data

partial

Analysis

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pmid:39135956
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title
Prognostic role of chemokine-related genes in acute myeloid leukemia.
doi
10.7717/peerj.17862
pmid
39135956
authors array · 5 items
[0]
Yanfei Hou
[1]
Yu Chen
[2]
Yaofang Zhang
[3]
Mengyao Li
[4]
Jianfang Chen
journal
PeerJ
year
2024
abstract
Background: Chemotactic cytokines play a crucial role in the development of acute myeloid leukemia (AML). Thus, investigating the mechanisms of chemotactic cytokine-related genes (CCRGs) in AML is of paramount importance. Methods: Using the TCGA-AML, GSE114868, and GSE12417 datasets, differential expression analysis identified differentially expressed CCRGs (DE-CCRGs). These genes were screened by overlapping differentially expressed genes (DEGs) between AML and control groups with CCRGs. Subsequently, functional enrichment analysis and the construction of a protein-protein interaction (PPI) network were conducted to explore the functions of the DE-CCRGs. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses identified relevant prognostic genes and developed a prognostic model. Survival analysis of the prognostic gene was performed, followed by functional similarity analysis, immune analysis, enrichment analysis, and drug prediction analysis. Results: Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study. Functional enrichment analysis indicated that DE-CCRGs were primarily involved in chemotactic cytokine-related functions and pathways. Six prognostic genes (CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2) were identified and incorporated into the risk model. The model's performance was validated using the GSE12417 dataset. Survival analysis showed significant differences in AML overall survival (OS) between prognostic gene high and low expression groups, indicating that prognostic gene might be significantly associated with patient survival. Additionally, nine different immune cells were identified between the two risk groups. Correlation analysis revealed that CCR2 had the most significant positive correlation with monocytes and the most significant negative correlation with resting mast cells. The tumor immune dysfunction and exclusion score was lower in the high-risk group. Conclusion: CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2 were identified as prognostic genes correlated to AML and the tumor immune microenvironment. These findings offerred novel insights into the prevention and treatment of AML.
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sections[4] "Identification and functional enrichment analysis of DEGs and DE-CCRGs" paragraph 2
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The limma package was used for differential analysis with criteria of —log2fold change (FC)— > 0.5 and adj.P.Val < 0.05 (Shen et al., 2023).
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sections[21] "Additional Information and Declarations" paragraph 6
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The raw measurements are available in the Supplementary Files.
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Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study.
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Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study.
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sample_count
405
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null
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null
platform
570;96;97
taxon
Homo sapiens
title
Prognostic gene signature for normal karyotype AML
resolution_checked_at
2026-11-01T15:39:54Z
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identifier_resolution_only_no_download_no_licence_read
downloaded
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[2]
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[3]
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data_shapes array · 1 item
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count_matrix_with_condition
evidence object · 3 fields
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quote
The limma package was used for differential analysis with criteria of —log2fold change (FC)— > 0.5 and adj.P.Val < 0.05 (Shen et al., 2023).
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evidence object · 3 fields
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[0]
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data_shapes array · 1 item
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evidence object · 3 fields
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id
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correlation_analysis
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[0]
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data_shapes array · 1 item
[0]
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evidence object · 3 fields
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evidence object · 3 fields
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gaps array · 1 item
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capability object · 3 fields
state
complete
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[0] object · 6 fields
capability_id
capability:computational_analysis
label
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[0]
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candidate
Automated Assays Laravel application and simulated adapters
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state
complete
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R1
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nothing:replicable_now
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basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 2 items
[0]
GSE114868
[1]
GSE12417
resolved_accessions array · 2 items
[0]
GSE114868
[1]
GSE12417
statements array · 1 item
[0]
The raw measurements are available in the Supplementary Files.
basis
The paper names GSE114868, GSE12417 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE114868
gaps array · 0 items
review object · 5 fields
state
partial
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kind
extraction_review
reader object · 5 fields
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MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T19:28:04Z
independence
single_reader
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method
verbatim
availability
verbatim
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5
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10103
completion_tokens
1548
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256
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[0] object · 4 fields
id
extraction
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complete
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MiniMax-M3.1-Flash
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not_run
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pmid:39135956
type
paper
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pmid:39135956
[1] object · 3 fields
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source:pmid:39135956
type
manuscript
label
PMID 39135956 full text
[2] object · 3 fields
id
data:geo:GSE114868
type
dataset
label
GEO GSE114868
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[0] object · 4 fields
id
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type
classification
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type
paper
label
pmid:39135956
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id
source:pmid:39135956
type
manuscript
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PMID 39135956 full text
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id
data:geo:GSE114868
type
dataset
label
GEO GSE114868
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agent:corpus-assay-engine
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extraction
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agent:minimax-reader
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accession_resolution
used array · 1 item
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agent:corpus-assay-engine
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The limma package was used for differential analysis with criteria of —log2fold change (FC)— > 0.5 and adj.P.Val < 0.05 (Shen et al., 2023).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39135956", "kind": "science-record", "revision": 4, "content_hash": "sha256:0243141ded037e5e9a03335a47d96f487f3782f8c6f437f8d1cc91051d8cee9d", "created_at": "2026-11-01T20:29:14Z", "updated_at": "2026-11-01T22:54:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic role of chemokine-related genes in acute myeloid leukemia.", "doi": "10.7717/peerj.17862", "pmid": "39135956", "authors": ["Yanfei Hou", "Yu Chen", "Yaofang Zhang", "Mengyao Li", "Jianfang Chen"], "journal": "PeerJ", "year": 2024, "abstract": "Background: Chemotactic cytokines play a crucial role in the development of acute myeloid leukemia (AML). Thus, investigating the mechanisms of chemotactic cytokine-related genes (CCRGs) in AML is of paramount importance.\nMethods: Using the TCGA-AML, GSE114868, and GSE12417 datasets, differential expression analysis identified differentially expressed CCRGs (DE-CCRGs). These genes were screened by overlapping differentially expressed genes (DEGs) between AML and control groups with CCRGs. Subsequently, functional enrichment analysis and the construction of a protein-protein interaction (PPI) network were conducted to explore the functions of the DE-CCRGs. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses identified relevant prognostic genes and developed a prognostic model. Survival analysis of the prognostic gene was performed, followed by functional similarity analysis, immune analysis, enrichment analysis, and drug prediction analysis.\nResults: Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study. Functional enrichment analysis indicated that DE-CCRGs were primarily involved in chemotactic cytokine-related functions and pathways. Six prognostic genes (CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2) were identified and incorporated into the risk model. The model's performance was validated using the GSE12417 dataset. Survival analysis showed significant differences in AML overall survival (OS) between prognostic gene high and low expression groups, indicating that prognostic gene might be significantly associated with patient survival. Additionally, nine different immune cells were identified between the two risk groups. Correlation analysis revealed that CCR2 had the most significant positive correlation with monocytes and the most significant negative correlation with resting mast cells. The tumor immune dysfunction and exclusion score was lower in the high-risk group.\nConclusion: CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2 were identified as prognostic genes correlated to AML and the tumor immune microenvironment. These findings offerred novel insights into the prevention and treatment of AML.", "claims": [{"id": "claim-method", "statement": "The limma package was used for differential analysis with criteria of —log2fold change (FC)— > 0.5 and adj.P.Val < 0.05 (Shen et al., 2023).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39135956", "locator": "sections[4] \"Identification and functional enrichment analysis of DEGs and DE-CCRGs\" paragraph 2", "quote": "The limma package was used for differential analysis with criteria of —log2fold change (FC)— > 0.5 and adj.P.Val < 0.05 (Shen et al., 2023)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw measurements are available in the Supplementary Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39135956", "locator": "sections[21] \"Additional Information and Declarations\" paragraph 6", "quote": "The raw measurements are available in the Supplementary Files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39135956", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study.", "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:39135956"], "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": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE114868"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"fold_change": "ia of —log2fold change (FC)— > 0"}, "inputs": ["data:geo:GSE114868"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39135956", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.17862", "sha256": "sha256:73d9917ba1df2977cb7f452546d5d58eae54999084690758de8c1cafb2b7336a", "bytes": 35311, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.17862", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11318587", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE114868", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114868", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE114868", "version": null, "note": "The raw measurements are available in the Supplementary Files.", "structure": {"repository": "GEO", "access_route": 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