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pmid:37700277

GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data.

ZhaoLiang Zhang · JianZhong Huo · XingHua Ji · LinDong Wei · Jinfeng Zhang

BMC musculoskeletal disorders · 2023 doi:10.1186/s12891-023-06854-4

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:37700277
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science-record
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2026-11-01T19:02:30Z
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2026-11-01T21:31:31Z
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paper object · 10 fields
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title
GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data.
doi
10.1186/s12891-023-06854-4
pmid
37700277
authors array · 5 items
[0]
ZhaoLiang Zhang
[1]
JianZhong Huo
[2]
XingHua Ji
[3]
LinDong Wei
[4]
Jinfeng Zhang
journal
BMC musculoskeletal disorders
year
2023
abstract
BACKGROUND: Low back pain (LBP) has drawn much widespread attention and is a major global health concern. In this field, intervertebral disc degeneration (IVDD) is frequently the focus of classic studies. However, the mechanistic foundation of IVDD is unclear and has led to conflicting outcomes. METHODS: Gene expression profiles (GSE34095, GSE147383) of IVDD patients alongside control groups were analyzed to identify differentially expressed genes (DEGs) in the GEO database. GSE23130 and GSE70362 were applied to validate the yielded key genes from DEGs by means of a best subset selection regression. Four machine-learning models were established to assess their predictive ability. Single-sample gene set enrichment analysis (ssGSEA) was used to profile the correlation between overall immune infiltration levels with Thompson grades and key genes. The upstream targeting miRNAs of key genes (GSE63492) were also analyzed. A single-cell transcriptome sequencing data (GSE160756) was used to define several cell clusters of nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous endplate (CEP) of human intervertebral discs and the distribution of key genes in different cell clusters was yielded. RESULTS: By developing appropriate p-values and logFC values, a total of 6 DEGs was obtained. 3 key genes (LRPPRC, GREM1, and SLC39A4) were validated by an externally validated predictive modeling method. The ssGSEA results indicated that key genes were correlated with the infiltration abundance of multiple immune cells, such as dendritic cells and macrophages. Accordingly, these 4 key miRNAs (miR-103a-3p, miR-484, miR-665, miR-107) were identified as upstream regulators targeting key genes using the miRNet database and external GEO datasets. Finally, the spatial distribution of key genes in AF, CEP, and NP was plotted. Pseudo-time series and GSEA analysis indicated that the expression level of GREM1 and the differentiation trajectory of NP chondrocytes are generally consistent. GREM1 may mainly exacerbate the degeneration of NP cells in IVDD. CONCLUSIONS: Our study gives a novel perspective for identifying reliable and effective gene therapy targets in IVDD.
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The best subset selection for variable screening was employed due to the possibility of multicollinearity among the first screened DEGs, which could result in overfitting of the model if all of them were included in the modelingmodelling.
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sections[5] "Definition of key genes set using a best subset selection regression" paragraph 2
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The best subset selection for variable screening was employed due to the possibility of multicollinearity among the first screened DEGs, which could result in overfitting of the model if all of them were included in the modelingmodelling.
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The datasets analysed during the current study are available in Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/): GSE34095, GSE147383, GSE70632, GSE23130, GSE63492, GSE160756.
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The datasets analysed during the current study are available in Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/): GSE34095, GSE147383, GSE70632, GSE23130, GSE63492, GSE160756.
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The key genes showed predictive power for IVDD grade groups, with logistic regression achieving the highest validation AUC of 0.7857143.
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sections[14] "Results of key genes and corresponding external validation" paragraph 3
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The AUC value of the logistic regression classifier reached 0.7857143 while the same for SVM, RandomForest and XGboost were 0.6938776, 0.6071429 and 0.622449 respectively, which showed that key genes had some predictive power for the defined group divided by Thompson grades in either linear or non-linear models.
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The key genes showed predictive power for IVDD grade groups, with logistic regression achieving the highest validation AUC of 0.7857143.
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a bioinformatics analysis based on multiple microarray and single-cell sequencing data.
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Methods In vitro experimental validation (qPCR and Western Blot) “AF needle puncture” metho
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igure the reaction system and then performed qPCR. IVDD group and sham group were set up with
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s In vitro experimental validation (qPCR and Western Blot) “AF needle puncture” method was used to con
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of Shanxi Provincial People’s Hospital. The rats were randomly divided into IVDD group (n = 4) and
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DD group (n = 4) and sham group (n = 4). The rats were anaesthetised using an intraperitoneal injec
[2]
(3.5 mg / 100 g). After confirming that the rats were under anaesthesia, a longitudinal incision w
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n. Lumbar intervertebral discs in IVDD group rats were exposed via the posterior peritoneum and pso
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37700277", "kind": "science-record", "revision": 3, "content_hash": "sha256:ddd1326de3cf16da83a2a8364fcbb6fbde6f04acd6406224fa865e592208981d", "created_at": "2026-11-01T19:02:30Z", "updated_at": "2026-11-01T21:31:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data.", "doi": "10.1186/s12891-023-06854-4", "pmid": "37700277", "authors": ["ZhaoLiang Zhang", "JianZhong Huo", "XingHua Ji", "LinDong Wei", "Jinfeng Zhang"], "journal": "BMC musculoskeletal disorders", "year": 2023, "abstract": "BACKGROUND: Low back pain (LBP) has drawn much widespread attention and is a major global health concern. In this field, intervertebral disc degeneration (IVDD) is frequently the focus of classic studies. However, the mechanistic foundation of IVDD is unclear and has led to conflicting outcomes.\nMETHODS: Gene expression profiles (GSE34095, GSE147383) of IVDD patients alongside control groups were analyzed to identify differentially expressed genes (DEGs) in the GEO database. GSE23130 and GSE70362 were applied to validate the yielded key genes from DEGs by means of a best subset selection regression. Four machine-learning models were established to assess their predictive ability. Single-sample gene set enrichment analysis (ssGSEA) was used to profile the correlation between overall immune infiltration levels with Thompson grades and key genes. The upstream targeting miRNAs of key genes (GSE63492) were also analyzed. A single-cell transcriptome sequencing data (GSE160756) was used to define several cell clusters of nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous endplate (CEP) of human intervertebral discs and the distribution of key genes in different cell clusters was yielded.\nRESULTS: By developing appropriate p-values and logFC values, a total of 6 DEGs was obtained. 3 key genes (LRPPRC, GREM1, and SLC39A4) were validated by an externally validated predictive modeling method. The ssGSEA results indicated that key genes were correlated with the infiltration abundance of multiple immune cells, such as dendritic cells and macrophages. Accordingly, these 4 key miRNAs (miR-103a-3p, miR-484, miR-665, miR-107) were identified as upstream regulators targeting key genes using the miRNet database and external GEO datasets. Finally, the spatial distribution of key genes in AF, CEP, and NP was plotted. Pseudo-time series and GSEA analysis indicated that the expression level of GREM1 and the differentiation trajectory of NP chondrocytes are generally consistent. GREM1 may mainly exacerbate the degeneration of NP cells in IVDD.\nCONCLUSIONS: Our study gives a novel perspective for identifying reliable and effective gene therapy targets in IVDD.", "claims": [{"id": "claim-method", "statement": "The best subset selection for variable screening was employed due to the possibility of multicollinearity among the first screened DEGs, which could result in overfitting of the model if all of them were included in the modelingmodelling.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37700277", "locator": "sections[5] \"Definition of key genes set using a best subset selection regression\" paragraph 2", "quote": "The best subset selection for variable screening was employed due to the possibility of multicollinearity among the first screened DEGs, which could result in overfitting of the model if all of them were included in the modelingmodelling."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are available in Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/): GSE34095, GSE147383, GSE70632, GSE23130, GSE63492, GSE160756.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37700277", "locator": "sections[28] \"notes\" paragraph 1", "quote": "The datasets analysed during the current study are available in Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/): GSE34095, GSE147383, GSE70632, GSE23130, GSE63492, GSE160756."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The key genes showed predictive power for IVDD grade groups, with logistic regression achieving the highest validation AUC of 0.7857143.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37700277", "locator": "sections[14] \"Results of key genes and corresponding external validation\" paragraph 3", "quote": "The AUC value of the logistic regression classifier reached 0.7857143 while the same for SVM, RandomForest and XGboost were 0.6938776, 0.6071429 and 0.622449 respectively, which showed that key genes had some predictive power for the defined group divided by Thompson grades in either linear or non-linear models."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The key genes showed predictive power for IVDD grade groups, with logistic regression achieving the highest validation AUC of 0.7857143.", "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:37700277"], "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. 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