Identification of diagnostic gene biomarkers related to immune infiltration in patients with idiopathic pulmonary fibrosis based on bioinformatics strategies.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36507532", "kind": "science-record", "revision": 4, "content_hash": "sha256:ec268ff2d60d04713bc3187ec74151ddad21460e6228facfe575750431b288c9", "created_at": "2026-11-01T19:01:00Z", "updated_at": "2026-11-01T22:50:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of diagnostic gene biomarkers related to immune infiltration in patients with idiopathic pulmonary fibrosis based on bioinformatics strategies.", "doi": "10.3389/fmed.2022.959010", "pmid": "36507532", "authors": ["Xiangdong Dai", "Zhihua Yang", "Wenjing Zhang", "Shuai Liu", "Qianru Zhao", "Tao Liu", "Lu Chen", "Lin Li", "Yi Wang", "Rui Shao"], "journal": "Frontiers in medicine", "year": 2022, "abstract": "Objective: The study aims to identify potential diagnostic markers of idiopathic pulmonary fibrosis (IPF) and analyze the significance of immune cell infiltration in this pathology.\nMaterials and methods: Download two publicly available gene expression profiles (GSE10667 and GSE24206 datasets) from the GEO database including 48 Idiopathic pulmonary fibrosis (IPF) samples and 21 human control samples and select for distinctly expressed genes (DEG) from them. Lasso regression model and support vector machine recursive feature elimination S,V,R,F analysis were used to check candidate biomarkers. The area under the subject's work characteristic curve (AUC) value is used to evaluate its recognition ability. The GSE53845 dataset (40 IPF patients and 8 controls) continue to validate the expression level and diagnostic value of biomarkers in IPF. Comprehensive analysis of immune infiltrated cells of IPF was performed using R software and immune cell infiltration estimation analysis tool- deconvolution algorithm (CIBERSORT).\nResults: 43 DEGs were identified in total. The identified DEGs mostly involve pneumonia, lung disease, collagen disease, obstructive pulmonary disease and other diseases. The activation of IL-17 signaling pathways, amoebic disease, interaction of viral proteins with cytokines and cytokine receptors, protein digestion and absorption, and flaccid hormone signaling pathways in IPF were different from the control group. The expression degree of CRTAC1, COL10A1, COMP, RPS4Y1, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 in IPF tissue were prominently higher than the normal group. Immune cell infiltration analysis showed that CRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 were associated with monocytes, plasma cells, neutrophils, and regulatory (treg) T cells.\nConclusion: CRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 can be used as diagnostic markers for IPF, providing new ideas for the future study of IPF occurrence and molecular mechanisms.", "claims": [{"id": "claim-method", "statement": "The area under ROC curve (AUC) value was used to determine the diagnostic validity of IPF relative to the control sample, and was further validated in the GSE53845 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36507532", "locator": "sections[7] \"Diagnostic value of characteristic biomarkers for idiopathic pulmonary fibrosis\" paragraph 2", "quote": "The area under ROC curve (AUC) value was used to determine the diagnostic validity of IPF relative to the control sample, and was further validated in the GSE53845 dataset."}], "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:36507532", "locator": "sections[19] \"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": "COMP achieved an AUC of 0.984 in the training dataset, and the eight selected biomarkers showed strong diagnostic discrimination in the independent GSE53845 validation dataset.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "In addition, a strong discrimination ability was confirmed in the GSE53845 dataset with an AUC of 0.981 (95% CI 0.941–1.000) in CRTAC1, AUC of 0.975 (95% CI 0.912–1.000) in COL10A1, AUC of 0.953 (95% CI 0.881–1.000) in COMP, AUC of 0.736 (95% CI 0.494–0.927) in RPS4Y1, AUC of 0.931 (95% CI 0.812–1.000) in IGFL2, AUC of 0.991 (95% CI 0.963–1.000) in NECAB1, AUC of 0.984 (95% CI 0.944–1.000) in SCG5, AUC of 0.900 (95% CI 0.784–0.981) in SLC6A4, AUC of 0.925 (95% CI 0.809–1.000) in SPP1 indicating that the feature biomarkers had a high diagnostic ability, as shown in Figure 11."}, "evidence": [{"asset": "source:pmid:36507532", "locator": "sections[14] \"Diagnostic validity of traits biomarkers in idiopathic pulmonary fibrosis\" paragraph 2", "quote": "In addition, a strong discrimination ability was confirmed in the GSE53845 dataset with an AUC of 0.981 (95% CI 0.941–1.000) in CRTAC1, AUC of 0.975 (95% CI 0.912–1.000) in COL10A1, AUC of 0.953 (95% CI 0.881–1.000) in COMP, AUC of 0.736 (95% CI 0.494–0.927) in RPS4Y1, AUC of 0.931 (95% CI 0.812–1.000) in IGFL2, AUC of 0.991 (95% CI 0.963–1.000) in NECAB1, AUC of 0.984 (95% CI 0.944–1.000) in SCG5, AUC of 0.900 (95% CI 0.784–0.981) in SLC6A4, AUC of 0.925 (95% CI 0.809–1.000) in SPP1 indicating that the feature biomarkers had a high diagnostic ability, as shown in Figure 11."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "COMP achieved an AUC of 0.984 in the training dataset, and the eight selected biomarkers showed strong diagnostic discrimination in the independent GSE53845 validation dataset.", "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:36507532"], "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:GSE10667"], "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:GSE10667"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36507532", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2022.959010", "sha256": "sha256:ad2b3208c46d4b33752affbf7615006046ace1eff8a26a48f0b4d1bfc737e59d", "bytes": 46263, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmed.2022.959010", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9729277", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE10667", "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=GSE10667", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE10667", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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