Construction and validation of a diagnostic model for dermatomyositis based on the LASSO algorithm.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41079379", "kind": "science-record", "revision": 3, "content_hash": "sha256:56fcd7937fc38f38aac44cb22f4914d2f934c53912693a6c612786265e1e58b4", "created_at": "2026-11-01T20:31:08Z", "updated_at": "2026-11-01T22:14:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a diagnostic model for dermatomyositis based on the LASSO algorithm.", "doi": "10.5114/ceji.2025.151230", "pmid": "41079379", "authors": ["Changyi Lin", "Peicheng Wu", "Xuelan You", "Minghui Song", "Youtian Liu", "Qiong Deng", "Xueyan Huang", "Zhongxiao Fan", "Damei Ye", "Ruimin Lin", "Chaoyan Xu"], "journal": "Central-European journal of immunology", "year": 2025, "abstract": "Introduction: Dermatomyositis (DM) is the most prevalent disease among myositis patients. The immune response is crucial in DM development. Bioinformatics research on immune-related genes in DM is limited. This study attempted to construct a diagnostic model and investigate immune characteristics of immune-related differentially expressed genes (DEGs), which could aid in DM diagnosis.\nMaterial and methods: GSE46239 and GSE39454 datasets were from the GEO database, and batch effects were eliminated for use as the DM training set. DEG were identified and enrichment analysis was conducted between DM and normal samples. Intersection of DEGs and immune-related genes yielded immune-related DEGs, which were utilized to generate a PPI network. The diagnostic model was built by the LASSO method. The diagnostic model and effectiveness of model genes were evaluated through GSE143323. The correlation between immune cell infiltration in DM and diagnostic genes was analyzed. Finally, expression levels of HLA genes in DM and their correlation with diagnostic genes were examined.\nResults: A total of 350 DEGs were identified. Seventy-one immune-related DEGs were screened. LASSO regression identified 5 immune-related DEGs (ACKR1, DHX58, IRF7, ISG15, and PSMB8) for constructing the DM diagnostic model. The model showed good effectiveness in training and validation sets (AUC of 0.99 and 0.958, respectively), and 5 immune-related DEGs also exhibited good effectiveness (AUC > 0.784). Diagnostic genes in DM were associated with M1 macrophages, M2 macrophages, resting dendritic cells, and certain HLA genes.\nConclusions: We constructed a DM diagnostic model using ACKR1, DHX58, IRF7, ISG15, and PSMB8, which were closely related to immune cells and HLA. This model could contribute to research in DM diagnosis.", "claims": [{"id": "claim-method", "statement": "The LASSO model was built using the glmnet R package according to gene expression data of immune-related DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41079379", "locator": "sections[7] \"Construction and validation of the diagnostic model based on immune-related DEGs\" paragraph 2", "quote": "The LASSO model was built using the glmnet R package according to gene expression data of immune-related DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GSE46239, GSE39454, and GSE143323 datasets were obtained from GEO (https://www.ncbi.nlm.nih.gov/). GSE46239 consisted of 5 normal samples and 8 DM samples, GSE39454 consisted of 4 normal samples and 48 DM samples, and GSE143323 consisted of 20 normal samples and 39 DM samples. Batch effects of datasets GSE46239 and GSE39454 were removed, and the datasets were merged into one training set, while GSE143323 served as the validation set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41079379", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "GSE46239, GSE39454, and GSE143323 datasets were obtained from GEO (https://www.ncbi.nlm.nih.gov/). GSE46239 consisted of 5 normal samples and 8 DM samples, GSE39454 consisted of 4 normal samples and 48 DM samples, and GSE143323 consisted of 20 normal samples and 39 DM samples. Batch effects of datasets GSE46239 and GSE39454 were removed, and the datasets were merged into one training set, while GSE143323 served as the validation set."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed a DM diagnostic model using ACKR1, DHX58, IRF7, ISG15, and PSMB8, with AUC values of 0.99 in the training set and 0.958 in the validation set.", "kind": "quantitative", "target": {"value": 0.99, "unit": "auc", "tolerance": null, "source": "The model showed good effectiveness in training and validation sets (AUC of 0.99 and 0.958, respectively), and 5 immune-related DEGs also exhibited good effectiveness (AUC > 0.784)."}, "evidence": [{"asset": "source:pmid:41079379", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We constructed a DM diagnostic model using ACKR1, DHX58, IRF7, ISG15, and PSMB8, which were closely related to immune cells and HLA."}, {"asset": "source:pmid:41079379", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The model showed good effectiveness in training and validation sets (AUC of 0.99 and 0.958, respectively), and 5 immune-related DEGs also exhibited good effectiveness (AUC > 0.784)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed a DM diagnostic model using ACKR1, DHX58, IRF7, ISG15, and PSMB8, with AUC values of 0.99 in the training set and 0.958 in the validation set.", "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:41079379"], "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:GSE143323"], "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:GSE143323"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41079379", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.5114/ceji.2025.151230", "sha256": "sha256:09c79dc3068f85d53e02d8ea6dbeb51dcf06771bcc6468fa1bfdfd0f4a02393a", "bytes": 21987, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.5114/ceji.2025.151230", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12512114", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE143323", "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=GSE143323", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE143323", "version": null, "note": "GSE46239, GSE39454, and GSE143323 datasets were obtained from GEO (https://www.ncbi.nlm.nih.gov/). GSE46239 consisted of 5 normal samples and 8 DM samples, GSE39454 consisted of 4 normal samples and 48 DM samples, and GSE143323 consisted of 20 normal samples and 39 DM samples. Batch effects of datasets GSE46239 and GSE39454 were removed, and the datasets were merged into one training set, while GSE143323 served as the validation set.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "GSE46239, GSE39454, and GSE143323 datasets were obtained from GEO (https://www.ncbi.nlm.nih.gov/). GSE46239 consisted of 5 normal samples and 8 DM samples, GSE39454 consisted of 4 normal samples and 48 DM samples, and GSE143323 consisted of 20 normal samples and 39 DM samples. 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