Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35350787", "kind": "science-record", "revision": 5, "content_hash": "sha256:8769ee61f78826e2c85c5a5271113bfcab3cb96b4db5073c17a856a2865bb6ac", "created_at": "2026-11-01T18:14:09Z", "updated_at": "2026-11-01T22:48:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease.", "doi": "10.3389/fimmu.2022.740513", "pmid": "35350787", "authors": ["Yuepeng Zhang", "Rongyao Xia", "Meiyu Lv", "Zhiheng Li", "Lingling Jin", "Xueda Chen", "Yaqian Han", "Chunpeng Shi", "Yanan Jiang", "Shoude Jin"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Objective: This study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis.\nMethods: The GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the in vivo and in vitro model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results.\nResults: 80 genes were identified via DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both in vivo and in vitro. Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells.\nConclusion: STUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD.", "claims": [{"id": "claim-method", "statement": "Similarly, 134 COPD and 49 normal samples were analyzed for DEGs using the R package “limma” with DEGs selected based on thresholds of P < 0.05 and |log2 FC| > 1. We utilized the LASSO logistic regression algorithm to identify and COPD related 5 feature variables from DEGs (Figure 3A). The SVM-RFE algorithm was used to identify a subset of 42 features in the determined DEGs (Figure 3B). CIBERSORT is a deconvolution algorithm that quantifies immune cell infiltration (22 various cell types) in COPD gene expression profiles (20). Pearson correlation analysis allowed for in-depth scrutiny of relationships between diagnostic biological markers and infiltrating immune cells. The degree of efficacy of each diagnostic biomarker was assessed using receiver operating characteristic (ROC) curves.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35350787", "locator": "sections[4] \"Data Preprocessing and Differentially Expressed Genes (DEGs) Screening\" paragraph 2", "quote": "Similarly, 134 COPD and 49 normal samples were analyzed for DEGs using the R package “limma” with DEGs selected based on thresholds of P < 0.05 and |log2 FC| > 1."}, {"asset": "source:pmid:35350787", "locator": "sections[19] \"Identification and Verification of Diagnostic Biomarkers\" paragraph 2", "quote": "We utilized the LASSO logistic regression algorithm to identify and COPD related 5 feature variables from DEGs (Figure 3A)."}, {"asset": "source:pmid:35350787", "locator": "sections[19] \"Identification and Verification of Diagnostic Biomarkers\" paragraph 2", "quote": "The SVM-RFE algorithm was used to identify a subset of 42 features in the determined DEGs (Figure 3B)."}, {"asset": "source:pmid:35350787", "locator": "sections[7] \"Evaluation of Immune Cell Infiltration\" paragraph 2", "quote": "CIBERSORT is a deconvolution algorithm that quantifies immune cell infiltration (22 various cell types) in COPD gene expression profiles (20)."}, {"asset": "source:pmid:35350787", "locator": "sections[8] \"Correlation Analysis Between Diagnostic Biomarkers and Infiltrating Immune Cells\" paragraph 2", "quote": "Pearson correlation analysis allowed for in-depth scrutiny of relationships between diagnostic biological markers and infiltrating immune cells."}, {"asset": "source:pmid:35350787", "locator": "sections[15] \"Statistical Analysis\" paragraph 2", "quote": "The degree of efficacy of each diagnostic biomarker was assessed using receiver operating characteristic (ROC) curves."}], "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:35350787", "locator": "sections[27] \"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": "Using GEO lung-tissue expression data, LASSO logistic regression and SVM-RFE identified SLC27A3 and STAU1 as diagnostic biomarkers for COPD, with AUCs of 0.734 and 0.745 in the metadata cohort and 0.900 and 0.971, respectively, in the GSE106986 validation cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35350787", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1."}, {"asset": "source:pmid:35350787", "locator": "sections[19] \"Identification and Verification of Diagnostic Biomarkers\" paragraph 2", "quote": "The AUCs of SLC27A3 and STAU1 in the metadata cohort were 0.734 and 0.745, respectively (Figures 5A, B). Furthermore, the AUCs of SLC27A3 and STAU1 in the GSE106986 dataset were 0.900 and 0.971, respectively (Figures 5C, D), indicating that both SLC27A3 and STAU1 have high diagnostic values."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using GEO lung-tissue expression data, LASSO logistic regression and SVM-RFE identified SLC27A3 and STAU1 as diagnostic biomarkers for COPD, with AUCs of 0.734 and 0.745 in the metadata cohort and 0.900 and 0.971, respectively, in the GSE106986 validation cohort.", "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:35350787"], "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. 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Furthermore, the in vivo and in vitro model of COPD was established u", "d STAU1 were upregulated in COPD models both in vivo and in vitro. Immune infiltration activation"], "order_basis": "paper_text_order:1093", "id": "assay:1"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["xpression of SLC27A3 and STAU1. 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CIBERSORT is a deconvolution algorithm that quantifies immune cell infiltration (22 various cell types) in COPD gene expression profiles (20). Pearson correlation analysis allowed for in-depth scrutiny of relationships between diagnostic biological markers and infiltrating immune cells. The degree of efficacy of each diagnostic biomarker was assessed using receiver operating characteristic (ROC) curves."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}