A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37700323", "kind": "science-record", "revision": 4, "content_hash": "sha256:7159757ffdadf8b5ee0e56b764a77f983ab5355c1edafaa736a8366a533f7f52", "created_at": "2026-11-01T19:02:30Z", "updated_at": "2026-11-01T22:52:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications.", "doi": "10.1186/s12967-023-04499-4", "pmid": "37700323", "authors": ["Yongxin Zheng", "Jinping Wang", "Zhaoyi Ling", "Jiamei Zhang", "Yuan Zeng", "Ke Wang", "Yu Zhang", "Lingbo Nong", "Ling Sang", "Yonghao Xu", "Xiaoqing Liu", "Yimin Li", "Yongbo Huang"], "journal": "Journal of translational medicine", "year": 2023, "abstract": "BACKGROUND: A significant proportion of septic patients with acute lung injury (ALI) are recognized late due to the absence of an efficient diagnostic test, leading to the postponed treatments and consequently higher mortality. Identifying diagnostic biomarkers may improve screening to identify septic patients at high risk of ALI earlier and provide the potential effective therapeutic drugs. Machine learning represents a powerful approach for making sense of complex gene expression data to find robust ALI diagnostic biomarkers.\nMETHODS: The datasets were obtained from GEO and ArrayExpress databases. Following quality control and normalization, the datasets (GSE66890, GSE10474 and GSE32707) were merged as the training set, and four machine learning feature selection methods (Elastic net, SVM, random forest and XGBoost) were applied to construct the diagnostic model. The other datasets were considered as the validation sets. To further evaluate the performance and predictive value of diagnostic model, nomogram, Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC) were constructed. Finally, the potential small molecular compounds interacting with selected features were explored from the CTD database.\nRESULTS: The results of GSEA showed that immune response and metabolism might play an important role in the pathogenesis of sepsis-induced ALI. Then, 52 genes were identified as putative biomarkers by consensus feature selection from all four methods. Among them, 5 genes (ARHGDIB, ALDH1A1, TACR3, TREM1 and PI3) were selected by all methods and used to predict ALI diagnosis with high accuracy. The external datasets (E-MTAB-5273 and E-MTAB-5274) demonstrated that the diagnostic model had great accuracy with AUC value of 0.725 and 0.833, respectively. In addition, the nomogram, DCA and CIC showed that the diagnostic model had great performance and predictive value. Finally, the small molecular compounds (Curcumin, Tretinoin, Acetaminophen, Estradiol and Dexamethasone) were screened as the potential therapeutic agents for sepsis-induced ALI.\nCONCLUSION: This consensus of multiple machine learning algorithms identified 5 genes that were able to distinguish ALI from septic patients. The diagnostic model could identify septic patients at high risk of ALI, and provide potential therapeutic targets for sepsis-induced ALI.", "claims": [{"id": "claim-method", "statement": "The ROC curve used to predict binary categorical variables was implemented via pROC package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37700323", "locator": "sections[11] \"Statistical analysis\" paragraph 2", "quote": "The ROC curve used to predict binary categorical variables was implemented via pROC package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used to support the findings of this study are included within the article. The datasets used and analyzed during the current study are available from GEO (http://www.ncbi.nlm.nih.gov/geo). The analyzed datasets generated during the study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37700323", "locator": "sections[26] \"Availability of data and materials\" paragraph 2", "quote": "All data used to support the findings of this study are included within the article. The datasets used and analyzed during the current study are available from GEO (http://www.ncbi.nlm.nih.gov/geo). The analyzed datasets generated during the study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The five-gene diagnostic model achieved external-validation AUC values of 0.725 and 0.833 in E-MTAB-5273 and E-MTAB-5274, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37700323", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The external datasets (E-MTAB-5273 and E-MTAB-5274) demonstrated that the diagnostic model had great accuracy with AUC value of 0.725 and 0.833, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The five-gene diagnostic model achieved external-validation AUC values of 0.725 and 0.833 in E-MTAB-5273 and E-MTAB-5274, respectively.", "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:37700323"], "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:GSE66890"], "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:GSE66890"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37700323", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-023-04499-4", "sha256": "sha256:9053068e6c5bf2c733109eba884256b9c29b2197384dee54c30d461dbbd3853d", "bytes": 69565, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12967-023-04499-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10498641", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE66890", "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=GSE66890", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE66890", "version": null, "note": "All data used to support the findings of this study are included within the article. The datasets used and analyzed during the current study are available from GEO (http://www.ncbi.nlm.nih.gov/geo). The analyzed datasets generated during the study are available from the corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data used to support the findings of this study are included within the article. The datasets used and analyzed during the current study are available from GEO (http://www.ncbi.nlm.nih.gov/geo). 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