Development of a Novel Inflammatory-Associated Gene Signature and Immune Infiltration Patterns in Intervertebral Disc Degeneration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36193061", "kind": "science-record", "revision": 5, "content_hash": "sha256:6f7d4862a42536c93f7aa6110a0cbee13db26f7e6005c26189cc9ed4e8b9ac71", "created_at": "2026-11-01T18:15:29Z", "updated_at": "2026-11-01T22:50:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of a Novel Inflammatory-Associated Gene Signature and Immune Infiltration Patterns in Intervertebral Disc Degeneration.", "doi": "10.1155/2022/2481071", "pmid": "36193061", "authors": ["Tao Lan", "Zhihao Hu", "Weizhuang Guo", "Bin Yan", "Yuantao Zhang"], "journal": "Oxidative medicine and cellular longevity", "year": 2022, "abstract": "Background: Both inflammatory factors and immune response play important roles in the pathogenesis of intervertebral disc degeneration (IDD). However, a comprehensive analysis of interaction between inflammatory response-associated genes (IRGs) and immune microenvironment in patients with IDD remains lacking. Hence, the current research is aimed at investigating the correlations between IRG signatures and immune cells in the progression of IDD.\nMethods: The expression profiles (GSE27494 and GSE41883) and IRGs were downloaded from the Gene Expression Omnibus (GEO) database and Molecular Signature Database (MSigDB), respectively. Weighted gene coexpression network analysis (WGCNA) and differential expression analysis were used to identify the pivotal modules and common differentially expressed genes (DEGs) associated with IDD. Subsequently, we retrieved differentially expressed IRGs (DE-IRGs) by intersecting IRGs and DEGs for enrichment analysis. Next, LASSO regression analyses were performed to screen optimal marker genes for IDD prediction. Additionally, we validated differences DE-IRGs between IDD patients and controls in GSE150408. Finally, the infiltration alteration of immune cells was evaluated by the CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed.\nResults: A total of 10 upregulated differentially expressed inflammatory genes were identified that were obviously related to progression of IDD. Functional analysis results revealed that DE-IRGs were mainly enriched in signaling pathways TNF, IL-17, NOD-like receptor, and NF-kappa B pathway. A five-gene signature that consisted of IL-1β, LIF, LYN, NAMPT, and SLC7A2 was constructed by the LASSO Cox regression. IL1B, LYN, and NAMPT were further validated as optimal candidate genes in the pathophysiology of IDD. In addition, there was a remarkable immune cell infiltration difference between the healthy and IDD groups. The proportions for dendritic cells activated, mast cells activated, and neutrophils in the IDD group were significantly higher than those in the normal group, while the proportion of some cells was lower than that of the normal group, such as T cell CD4 memory resting, NK cells activated, and macrophage M0. Furthermore, correlation analysis indicated IL-1β, LYN, and NAMPT were closely implicated with immune cell infiltration in IDD development.\nConclusions: We explored an association between inflammatory response-associated signature and immune infiltration in IDD and validated that IL-1β, LYN, and NAMPT might serve as biomarkers and therapeutic targets for IDD in the future.", "claims": [{"id": "claim-method", "statement": "Differential analysis between IDD patients and healthy controls in GSE27494 or GSE41883 was performed after using the limma package in R software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36193061", "locator": "sections[4] \"2.2. Differential and WGCNA Expression Analyses\" paragraph 2", "quote": "Differential analysis between IDD patients and healthy controls in GSE27494 or GSE41883 was performed after using the limma package in R software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability: The raw data can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27494, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41883, and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150408.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36193061", "locator": "sections[20] \"Data Availability\" paragraph 2", "quote": "The following information was supplied regarding data availability: The raw data can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27494, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41883, and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150408."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Ten differentially expressed inflammatory response-associated genes were identified in IDD, and all were upregulated in the IDD group.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36193061", "locator": "sections[11] \"3.1. Identification of 10 Differentially Expressed IRGs Related to IDD\" paragraph 2", "quote": "Next, 200 inflammatory response-associated genes were downloaded from hallmark gene sets in MSigDB and then overlapped with previously obtained DEGs, and Venn diagrams revealed 10 differentially expressed IRGs (SLC7A2, LIF, NAMPT, IL-1β, NOD2, CCL20, CCL7, TNFRSF1B, LYN, and GCH1) (Figure 4(b) and Table 1). Finally, a boxplot revealed that all DE-IRGs were upregulated in IDD group (Figure 5(a))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Ten differentially expressed inflammatory response-associated genes were identified in IDD, and all were upregulated in the IDD group.", "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:36193061"], "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:GSE150408"], "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:GSE150408"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36193061", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/2481071", "sha256": 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availability: The raw data can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27494, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41883, and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150408.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The following information was supplied regarding data availability: The raw data can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27494, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41883, and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150408.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1026926", "GSM1026927", "GSM1026928", "GSM1026929", "GSM1026930", "GSM1026931", "GSM1026932", "GSM1026933"], "sample_count": 8, "group_sizes": null, "group_sizes_basis": null, "platform": "1352", "taxon": "Homo sapiens", "title": "Human annulus disc cells grown in a 3D collagen sponge and exposed to TNF-a", "resolution_checked_at": "2026-11-01T18:06:44Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE150408 not downloaded", "asset:data:geo:GSE150408 licence not read", "asset:data:geo:GSE27494 not downloaded", "asset:data:geo:GSE27494 licence not read", "asset:data:geo:GSE41883 not downloaded", "asset:data:geo:GSE41883 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Differential analysis between IDD patients and healthy controls in GSE27494 or GSE41883 was performed after using 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