Pan-cancer analysis of the intervertebral-disc-degeneration-related innate immunity gene NAIP.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37267294", "kind": "science-record", "revision": 2, "content_hash": "sha256:c59df1889ae2eeb24b8ec68a053d976757c183148c7bb8ba3c4106d540e7f6ea", "created_at": "2026-11-01T19:01:59Z", "updated_at": "2026-11-01T21:30:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Pan-cancer analysis of the intervertebral-disc-degeneration-related innate immunity gene NAIP.", "doi": "10.1371/journal.pone.0286647", "pmid": "37267294", "authors": ["Wen-Bin Xu", "Vit Kotheeranurak", "Ding-Qiang Chen", "Nai-Kun Sun", "Di-Xin Cai", "Chien-Min Chen", "Guang-Xun Lin", "Gang Rui"], "journal": "PloS one", "year": 2023, "abstract": "BACKGROUND: Intervertebral disc degeneration (IDD) is a progressive chronic condition that commonly causes low back pain. Cancer is among the primary reasons for deaths worldwide. Our purpose was to identify the characteristic genes of IDD and explore the potential association between IDD and cancer.\nMETHODS: Immune cell infiltration and differentially expressed analysis were conducted utilizing data from the GSE124272 database. Enrichment analysis of differentially expressed genes (DEGs) was performed to explore the possible mechanisms underlying IDD development. Moreover, weighted gene correlation network analysis (WGCNA) was applied to select IDD-related hub genes. The immune-related key genes were determined by intersecting DEGs, IDD-related hub genes, and immune genes. Subsequently, machine learning models based on these genes were built to identify and verify the characteristic genes. RNA sequencing and clinical data of 33 carcinoma categories were obtained from the Cancer Genome Atlas (TCGA). The association between NAIP expression and prognosis was calculated using the Kaplan-Meier analysis. To gain a deeper understanding of the impact of NAIP in tumor immunotherapy, the association between NAIP and immune infiltration and two immunotherapeutic biomarkers were explored. Ultimately, the association between NAIP and immunotherapeutic response was investigated utilizing two independent cohorts.\nRESULTS: NAIP was identified as an immune-related characteristic gene between IDD and normal intervertebral disc tissue. In certain carcinoma categories, NAIP expression levels were elevated (4/33) and significantly correlated to the respective tumor stage (4/21). Survival analysis revealed that the expression levels of NAIP have prognostic significance in different cancer types. Generally, NAIP presented a strong association with immune cell infiltration and modulators. NAIP may influence immunotherapy effects through tumor mutational burden and microsatellite instability. No remarkable association between NAIP and immunotherapy response was found in either cohort.\nCONCLUSION: Our study is the first to identify NAIP as an immune-related characteristic gene. Pan-cancer analysis revealed that NAIP could serve as a novel clinical prognostic marker and therapeutic target for a variety of carcinoma categories, reducing the risk of IDD in tumor patients.", "claims": [{"id": "claim-method", "statement": "The Kaplan-Meier survival analysis was utilized to forecast the correlation between the overall survival (OS) of patients and NAIP expression levels in 33 diverse cancer types.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37267294", "locator": "sections[11] \"Survival analysis\" paragraph 2", "quote": "The Kaplan-Meier survival analysis was utilized to forecast the correlation between the overall survival (OS) of patients and NAIP expression levels in 33 diverse cancer types."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are available in the GEO database through GEO accession numbers GSE124272, GSE78220, and GSE67501.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37267294", "locator": "sections[30] \"Data Availability\" paragraph 2", "quote": "The datasets analyzed during the current study are available in the GEO database through GEO accession numbers GSE124272, GSE78220, and GSE67501."}], "state": "complete"}, {"id": "claim-target-result", "statement": "NAIP was identified as an immune-related characteristic gene between IDD and normal intervertebral disc tissue.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37267294", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "NAIP was identified as an immune-related characteristic gene between IDD and normal intervertebral disc tissue."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "NAIP was identified as an immune-related characteristic gene between IDD and normal intervertebral disc tissue.", "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:37267294"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37267294", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0286647", "sha256": "sha256:438fbca7d1915d72d5d6b7ece736514c3298bd2a1b82213688e4fbfb3d7addf5", "bytes": 37166, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1371/journal.pone.0286647", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10237447", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The Kaplan-Meier survival analysis was utilized to forecast the correlation between the overall survival (OS) of patients and NAIP expression levels in 33 diverse cancer types."}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"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": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The datasets analyzed during the current study are available in the GEO database through GEO accession numbers GSE124272, GSE78220, and GSE67501."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["survival_km"], "assets": ["source:pmid:37267294"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:42:38Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11149, "completion_tokens": 1423, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:37267294", "type": "paper", "label": "pmid:37267294"}, {"id": "source:pmid:37267294", "type": "manuscript", "label": "PMID 37267294 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37267294", "type": "paper", "label": "pmid:37267294"}, {"id": "source:pmid:37267294", "type": "manuscript", "label": "PMID 37267294 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37267294", "type": "extraction", "used": ["source:pmid:37267294"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"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": {"parent_id": "pmid:37267294", "parent_hash": "sha256:a857695d381cad78dc6790e297f02d23682bacc2dc2ec3b15a9ffb1ef5a658e5", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["dentify and verify the characteristic genes. RNA sequencing and clinical data of 33 carcinoma categories"], "order_basis": "paper_text_order:954", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["survival_km"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The Kaplan-Meier survival analysis was utilized to forecast the correlation between the overall survival (OS) of patients and NAIP expression levels in 33 diverse cancer types."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}