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pmid:39262492

Predictive model of gene expression regulating invasion and migration of M2 macrophages in breast cancer: clinical prognosis and therapeutic implications.

Chengjie Jiang · Jinlei Luo · Xiaoxue Jiang · Yujie Lv · Jianwei Dou

Translational cancer research · 2024 doi:10.21037/tcr-24-29

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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title
Predictive model of gene expression regulating invasion and migration of M2 macrophages in breast cancer: clinical prognosis and therapeutic implications.
doi
10.21037/tcr-24-29
pmid
39262492
authors array · 5 items
[0]
Chengjie Jiang
[1]
Jinlei Luo
[2]
Xiaoxue Jiang
[3]
Yujie Lv
[4]
Jianwei Dou
journal
Translational cancer research
year
2024
abstract
Background: Breast cancer (BRCA) has surpassed lung cancer to become the malignant tumor with the highest incidence in female population. It occurs in malignant cells in breast tissue and is common worldwide. An increasing body of research indicates that M2 macrophages are critical to the occurrence and progression of BRCA. The aim of this work is to build a predictive model of genes related to invasion and migration of M2 macrophages, forecast the prognosis of patients with BRCA, and then evaluate the efficacy of some targeted treatments. Methods: The Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database supplied the GSE20685 dataset, whereas the expression profile a clinical details of BRCA patients were obtained from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) database. The genes linked to M2 macrophages and the differentially elevated genes of invasion and migration were found in GSE20685. To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression. GSE58812 was utilized for external validation. After calculating each patient's risk score, the prognostic model was examined by analyses of immune infiltration, medication sensitivity, mutation, and enrichment of the risk score. Results: The risk score had a strong correlation with both several immune cells and popular anti-tumor medications. Additionally, it was discovered that the risk score was a separate prognostic factor for BRCA. Conclusions: Based on invasion and migration-related M2 macrophage genes, we investigated and validated predictive characteristics in our study that may offer helpful insights into the progression and prognosis of BRCA.
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To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression.
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To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression.
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The Series Matrix File of GSE20685 was retrieved from the National Center for Biotechnology Information (NCBI) GEO public database, and the annotation platform used was GPL570. For a total of 327 BRCA patients, an extensive expression profile and survival information were obtained.
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The risk score was a separate prognostic factor for BRCA.
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To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression. GSE58812 was utilized for external validation.
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The risk score was a separate prognostic factor for BRCA.
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Survival analysis
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cox_ph
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To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression.
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survival_analysis
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Survival analysis
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time_to_event_with_censoring
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survival_analysis
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Survival analysis
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capability_id
capability:computational_analysis
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candidate
Automated Assays Laravel application and simulated adapters
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cox_ph
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basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards.
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
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GSE20685
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GSE58812
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[0]
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statements array · 1 item
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The Series Matrix File of GSE20685 was retrieved from the National Center for Biotechnology Information (NCBI) GEO public database, and the annotation platform used was GPL570. For a total of 327 BRCA patients, an extensive expression profile and survival information were obtained.
basis
The paper names GSE20685, GSE58812 in National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) public database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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data:geo:GSE20685
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MiniMax-M3.1-Flash
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minimax
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minimax
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2026-11-01T19:31:08Z
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single_reader
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pmid:39262492
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PMID 39262492 full text
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data:geo:GSE20685
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GEO GSE20685
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classification
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PMID 39262492 full text
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data:geo:GSE20685
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GEO GSE20685
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agent:corpus-assay-engine
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asset:data:geo:GSE20685 licence not read
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f2132e96ae6c7a4e
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To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39262492", "kind": "science-record", "revision": 2, "content_hash": "sha256:d8b7faef6ec3fc84cf4355164d96ac0ea598a880319bbb74fcf74258bcfae0d7", "created_at": "2026-11-01T20:29:25Z", "updated_at": "2026-11-01T21:33:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Predictive model of gene expression regulating invasion and migration of M2 macrophages in breast cancer: clinical prognosis and therapeutic implications.", "doi": "10.21037/tcr-24-29", "pmid": "39262492", "authors": ["Chengjie Jiang", "Jinlei Luo", "Xiaoxue Jiang", "Yujie Lv", "Jianwei Dou"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Breast cancer (BRCA) has surpassed lung cancer to become the malignant tumor with the highest incidence in female population. It occurs in malignant cells in breast tissue and is common worldwide. An increasing body of research indicates that M2 macrophages are critical to the occurrence and progression of BRCA. The aim of this work is to build a predictive model of genes related to invasion and migration of M2 macrophages, forecast the prognosis of patients with BRCA, and then evaluate the efficacy of some targeted treatments.\nMethods: The Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database supplied the GSE20685 dataset, whereas the expression profile a clinical details of BRCA patients were obtained from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) database. The genes linked to M2 macrophages and the differentially elevated genes of invasion and migration were found in GSE20685. To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression. GSE58812 was utilized for external validation. After calculating each patient's risk score, the prognostic model was examined by analyses of immune infiltration, medication sensitivity, mutation, and enrichment of the risk score.\nResults: The risk score had a strong correlation with both several immune cells and popular anti-tumor medications. Additionally, it was discovered that the risk score was a separate prognostic factor for BRCA.\nConclusions: Based on invasion and migration-related M2 macrophage genes, we investigated and validated predictive characteristics in our study that may offer helpful insights into the progression and prognosis of BRCA.", "claims": [{"id": "claim-method", "statement": "To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39262492", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The Series Matrix File of GSE20685 was retrieved from the National Center for Biotechnology Information (NCBI) GEO public database, and the annotation platform used was GPL570. 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For a total of 327 BRCA patients, an extensive expression profile and survival information were obtained."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The risk score was a separate prognostic factor for BRCA.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39262492", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression. GSE58812 was utilized for external validation."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The risk score was a separate prognostic factor for BRCA.", "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:39262492"], "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:GSE20685"], "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": {}, "inputs": ["data:geo:GSE20685"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39262492", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-24-29", "sha256": "sha256:5fb3f47e43e50877ae2aec2022469ca35603e864608800fca3d44c7bf8eae383", "bytes": 53103, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-24-29", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11384920", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE20685", "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=GSE20685", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE20685", "version": null, "note": "The Series Matrix File of GSE20685 was retrieved from the National Center for Biotechnology Information (NCBI) GEO public database, and the annotation platform used was GPL570. For a total of 327 BRCA patients, an extensive expression profile and survival information were obtained.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The Series Matrix File of GSE20685 was retrieved from the National Center for Biotechnology Information (NCBI) GEO public database, and the annotation platform used was GPL570. 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