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pmid:35223859

The Roles of Blood Lipid-Metabolism Genes in Immune Infiltration Could Promote the Development of IDD.

Weihang Li · Ziyi Ding · Huan Zhang · Quan Shi · Dong Wang · Shilei Zhang · Songjie Xu · Bo Gao · Ming Yan

Frontiers in cell and developmental biology · 2022 doi:10.3389/fcell.2022.844395

Paper

complete

Experiment

complete

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:35223859
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science-record
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T17:36:59Z
updated_at
2026-11-01T22:06:00Z
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paper object · 10 fields
state
complete
title
The Roles of Blood Lipid-Metabolism Genes in Immune Infiltration Could Promote the Development of IDD.
doi
10.3389/fcell.2022.844395
pmid
35223859
authors array · 9 items
[0]
Weihang Li
[1]
Ziyi Ding
[2]
Huan Zhang
[3]
Quan Shi
[4]
Dong Wang
[5]
Shilei Zhang
[6]
Songjie Xu
[7]
Bo Gao
[8]
Ming Yan
journal
Frontiers in cell and developmental biology
year
2022
abstract
Objectives: Intervertebral disc degeneration is a progressive and chronic disease, usually manifesting as low back pain. This study aimed to screen effective biomarkers for medical practice as well as figuring out immune infiltration situations between circulation and intervertebral discs. Methods: Gene expression profiles of GSE124272 was included for differentially analysis, WGCNA and immune infiltration analysis from GEO database, and other GSE series were used as validation datasets. A series of validation methods were conducted to verify the robustness of hub genes, such as principal component analysis, machine learning models, and expression verification. Lastly, nomogram was established for medical practice. Results: 10 genes were commonly screened via combination of DEGs, WGCNA analysis and lipid metabolism related genes. Furthermore, 3 hub gens CYP27A1, FAR2, CYP1B1 were chosen for subsequent analysis based on validation of different methods. GSEA analysis discovered that neutrophil extracellular traps formation and NOD-like receptor signaling pathway was activated during IDD. Immune infiltration analysis demonstrated that the imbalance of neutrophils and γδT cells were significantly correlated with IDD progression. Nomogram was established based on CYP27A1, FAR2, CYP1B1 and age, the calibration plot confirmed the stability of our model. Conclusion: CYP27A1, FAR2, CYP1B1 were considered as hub lipid metabolism related genes (LMRGs) in the development of IDD, which were regarded as candidate diagnostic biomarkers especially in circulation. The effects are worth expected in the early diagnosis of IDD through detecting these genes in blood.
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All genes of whole blood from GSE124272 were included for WGCNA (“WGCNA” and related packages in R) analysis, to avoid the inaccurate results that may generate from the top of 5,000 genes (according to variance).
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sections[8] "Weight Gene Co-Expression Network Analysis" paragraph 2
quote
All genes of whole blood from GSE124272 were included for WGCNA (“WGCNA” and related packages in R) analysis, to avoid the inaccurate results that may generate from the top of 5,000 genes (according to variance).
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sections[26] "Data Availability Statement" paragraph 2
quote
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
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Furthermore, 3 hub gens CYP27A1, FAR2, CYP1B1 were chosen for subsequent analysis based on validation of different methods.
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sections[0] "Abstract" paragraph 3
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Furthermore, 3 hub gens CYP27A1, FAR2, CYP1B1 were chosen for subsequent analysis based on validation of different methods.
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Furthermore, 3 hub gens CYP27A1, FAR2, CYP1B1 were chosen for subsequent analysis based on validation of different methods.
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https://doi.org/10.3389/fcell.2022.844395
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title
Transcriptome signatures reveal candidate key genes in the whole blood of patients with lumbar disc prolapse
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Gene expression data for human intervertebral discs
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
structure object · 15 fields
repository
GEO
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GEO accession URL
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
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1352
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Homo sapiens
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Gene Array Data from Human Annulus Disc Tissue
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
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GSM841722
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6
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96
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Homo sapiens
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Expression data between human degenerative and non-degenerative intervertebral discs
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2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70362
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null
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
structure object · 15 fields
repository
GEO
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
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GSM1725780
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GSM1725809
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GSM1725810
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GSM1725811
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GSM1725812
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GSM1725814
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GSM1725820
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48
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null
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null
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17810
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Homo sapiens
title
Gene Expression Profiling Identifies Interferon Signalling Molecules and IGFBP3 in Human Degenerative Annulus Fibrosus
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2026-11-01T17:30:42Z
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All genes of whole blood from GSE124272 were included for WGCNA (“WGCNA” and related packages in R) analysis, to avoid the inaccurate results that may generate from the top of 5,000 genes (according to variance).
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[0]
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id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:35223859
parent_hash
sha256:8a9402e7b58f2fbe61ef20a48579e598f4ca4fca9a1b92eccc73180a19790e86
fork_of
null
gaps array · 15 items
[0]
asset:data:geo:GSE124272 licence not read
[1]
asset:data:geo:GSE124272 not downloaded
[2]
asset:data:geo:GSE147383 licence not read
[3]
asset:data:geo:GSE147383 not downloaded
[4]
asset:data:geo:GSE23130 licence not read
[5]
asset:data:geo:GSE23130 not downloaded
[6]
asset:data:geo:GSE34095 licence not read
[7]
asset:data:geo:GSE34095 not downloaded
[8]
asset:data:geo:GSE70362 licence not read
[9]
asset:data:geo:GSE70362 not downloaded
[10]
execution.not_run
[11]
method.reader_named_unmapped
[12]
review.human_critique
[13]
review.independent_reader
[14]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
microarray
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.93
evidence array · 1 item
[0]
tween IDD and normal patients, blood tissues microarray data from GSE124272 series was conducted for
order_basis
paper_text_order:6758
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
group_comparison
definition_version
f2132e96ae6c7a4e
required_operations array · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
ANOVA
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:35223859", "kind": "science-record", "revision": 5, "content_hash": "sha256:032621d8261e3ef3fe924de50278cda5656a4e2fda1ec891658a510527bd2acd", "created_at": "2026-11-01T17:36:59Z", "updated_at": "2026-11-01T22:06:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The Roles of Blood Lipid-Metabolism Genes in Immune Infiltration Could Promote the Development of IDD.", "doi": "10.3389/fcell.2022.844395", "pmid": "35223859", "authors": ["Weihang Li", "Ziyi Ding", "Huan Zhang", "Quan Shi", "Dong Wang", "Shilei Zhang", "Songjie Xu", "Bo Gao", "Ming Yan"], "journal": "Frontiers in cell and developmental biology", "year": 2022, "abstract": "Objectives: Intervertebral disc degeneration is a progressive and chronic disease, usually manifesting as low back pain. This study aimed to screen effective biomarkers for medical practice as well as figuring out immune infiltration situations between circulation and intervertebral discs. Methods: Gene expression profiles of GSE124272 was included for differentially analysis, WGCNA and immune infiltration analysis from GEO database, and other GSE series were used as validation datasets. A series of validation methods were conducted to verify the robustness of hub genes, such as principal component analysis, machine learning models, and expression verification. Lastly, nomogram was established for medical practice. Results: 10 genes were commonly screened via combination of DEGs, WGCNA analysis and lipid metabolism related genes. Furthermore, 3 hub gens CYP27A1, FAR2, CYP1B1 were chosen for subsequent analysis based on validation of different methods. GSEA analysis discovered that neutrophil extracellular traps formation and NOD-like receptor signaling pathway was activated during IDD. Immune infiltration analysis demonstrated that the imbalance of neutrophils and γδT cells were significantly correlated with IDD progression. Nomogram was established based on CYP27A1, FAR2, CYP1B1 and age, the calibration plot confirmed the stability of our model. Conclusion: CYP27A1, FAR2, CYP1B1 were considered as hub lipid metabolism related genes (LMRGs) in the development of IDD, which were regarded as candidate diagnostic biomarkers especially in circulation. The effects are worth expected in the early diagnosis of IDD through detecting these genes in blood.", "claims": [{"id": "claim-method", "statement": "All genes of whole blood from GSE124272 were included for WGCNA (“WGCNA” and related packages in R) analysis, to avoid the inaccurate results that may generate from the top of 5,000 genes (according to variance).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35223859", "locator": "sections[8] \"Weight Gene Co-Expression Network Analysis\" paragraph 2", "quote": "All genes of whole blood from GSE124272 were included for WGCNA (“WGCNA” and related packages in R) analysis, to avoid the inaccurate results that may generate from the top of 5,000 genes (according to variance)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35223859", "locator": "sections[26] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Furthermore, 3 hub gens CYP27A1, FAR2, CYP1B1 were chosen for subsequent analysis based on validation of different methods.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35223859", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Furthermore, 3 hub gens CYP27A1, FAR2, CYP1B1 were chosen for subsequent analysis based on validation of different methods."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Furthermore, 3 hub gens CYP27A1, FAR2, CYP1B1 were chosen for subsequent analysis based on validation of different methods.", "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:35223859"], "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:GSE124272"], "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:GSE124272"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35223859", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2022.844395", "sha256": "sha256:002e3bfb729ec71e478ad9c5e5bb95157471e1bf18c90ab2eb89e5a8b06dd54e", "bytes": 59528, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcell.2022.844395", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8864150", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE124272", "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=GSE124272", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE124272", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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