Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37228596", "kind": "science-record", "revision": 3, "content_hash": "sha256:8e619f176389720b24b1bd2a44cf4a402bc19ae146f91c1c4c2748e5f974d0ac", "created_at": "2026-11-01T19:01:56Z", "updated_at": "2026-11-01T22:09:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis.", "doi": "10.3389/fimmu.2023.1184126", "pmid": "37228596", "authors": ["Qi Shu", "Han She", "Xi Chen", "Like Zhong", "Junfeng Zhu", "Luo Fang"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background: Sepsis remains a complex condition with incomplete understanding of its pathogenesis. Further research is needed to identify prognostic factors, risk stratification tools, and effective diagnostic and therapeutic targets.\nMethods: Three GEO datasets (GSE54514, GSE65682, and GSE95233) were used to explore the potential role of mitochondria-related genes (MiRGs) in sepsis. WGCNA and two machine learning algorithms (RF and LASSO) were used to identify the feature of MiRGs. Consensus clustering was subsequently carried out to determine the molecular subtypes for sepsis. CIBERSORT algorithm was conducted to assess the immune cell infiltration of samples. A nomogram was also established to evaluate the diagnostic ability of feature biomarkers via \"rms\" package.\nResults: Three different expressed MiRGs (DE-MiRGs) were identified as sepsis biomarkers. A significant difference in the immune microenvironment landscape was observed between healthy controls and sepsis patients. Among the DE-MiRGs, NDUFB3 was selected to be a potential therapeutic target and its significant elevated expression level was confirmed in sepsis using in vitro experiments and confocal microscopy, indicating its significant contribution to the mitochondrial quality imbalance in the LPS-simulated sepsis model.\nConclusion: By digging the role of these pivotal genes in immune cell infiltration, we gained a better understanding of the molecular immune mechanism in sepsis and identified potential intervention and treatment strategies.", "claims": [{"id": "claim-method", "statement": "Weighted gene co-expression network analysis (WGCNA) was employed in this study to identify the pivotal gene module associated with sepsis and healthy controls. The least absolute shrinkage and selection operator (LASSO) logistic regression and random forests (RF) were employed for feature selection to screen diagnostic markers for sepsis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37228596", "locator": "sections[4] \"WGCNA and machine learning algorithm\" paragraph 2", "quote": "Weighted gene co-expression network analysis (WGCNA) was employed in this study to identify the pivotal gene module associated with sepsis and healthy controls."}, {"asset": "source:pmid:37228596", "locator": "sections[4] \"WGCNA and machine learning algorithm\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) logistic regression and random forests (RF) were employed for feature selection to screen diagnostic markers for sepsis."}], "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:37228596", "locator": "sections[23] \"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": "The ROC analysis in the training cohort suggested that the AUC of three feature biomarkers (BCKDHB, LETMD1, NDUFB3) and nomogram score was 0.971, 0.977, 0.985, and 0.997, respectively ( Figure 6D ).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37228596", "locator": "sections[18] \"Validation of feature biomarkers and effectiveness evaluation\" paragraph 2", "quote": "The ROC analysis in the training cohort suggested that the AUC of three feature biomarkers (BCKDHB, LETMD1, NDUFB3) and nomogram score was 0.971, 0.977, 0.985, and 0.997, respectively ( Figure 6D )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The ROC analysis in the training cohort suggested that the AUC of three feature biomarkers (BCKDHB, LETMD1, NDUFB3) and nomogram score was 0.971, 0.977, 0.985, and 0.997, respectively ( Figure 6D ).", "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:37228596"], "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:GSE54514"], "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:GSE54514"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37228596", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1184126", "sha256": "sha256:47a453e5ce3ec6f2643c49fb6a9629b729d21dedfb5413dfad206a664014117f", "bytes": 38372, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1184126", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10203506", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE54514", "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=GSE54514", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE54514", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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