Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37564869", "kind": "science-record", "revision": 4, "content_hash": "sha256:62df51a9ac36e44b0812e09b468508573aa4525abc833467e6e35e7e55331c2e", "created_at": "2026-11-01T19:02:21Z", "updated_at": "2026-11-01T22:52:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method.", "doi": "10.3389/fgene.2023.1223243", "pmid": "37564869", "authors": ["Huimin Jiang", "Yun Ren", "Jiale Yu", "Sheng Hu", "Jihui Zhang"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Background: Lactate, as an essential clinical evaluation index of septic shock, is crucial in the incidence and progression of septic shock. This study aims to investigate the differential expression, regulatory relationship, clinical diagnostic efficacy, and immune infiltration of lactate metabolism-related genes (LMGs) in septic shock. Methods: Two sepsis shock datasets (GSE26440 and GSE131761) were screened from the GEO database, and the common differentially expressed genes (DEGs) of the two datasets were screened out. LMGs were selected from the GeneCards database, and lactate metabolism-related DEGs (LMDEGs) were determined by integrating DEGs and LMGs. Protein-protein interaction networks, mRNA-miRNA, mRNA-RBP, and mRNA-TF interaction networks were constructed using STRING, miRDB, ENCORI, and CHIPBase databases, respectively. Receiver operating characteristic (ROC) curves were constructed for each of the LMDEGs to evaluate the diagnostic efficacy of the expression changes in relation to septic shock. Finally, immune infiltration analysis was performed using ssGSEA and CIBERSORT. Results: This study identified 10 LMDEGs, including LDHB, STAT3, LDHA, GSR, FOXM1, PDP1, GCDH, GCKR, ABCC1, and CDKN3. Enrichment analysis revealed that DEGs were significantly enriched in pathways such as pyruvate metabolism, hypoxia pathway, and immune-inflammatory pathways. PPI networks based on LMDEGs, as well as 148 pairs of mRNA-miRNA interactions, 243 pairs of mRNA-RBP interactions, and 119 pairs of mRNA-TF interactions were established. ROC curves of eight LMDEGs (LDHA, GSR, STAT3, CDKN3, FOXM1, GCKR, PDP1, and LDHB) with consistent expression patterns in two datasets had an area under the curve (AUC) ranging from 0.662 to 0.889. The results of ssGSEA and CIBERSORT both showed significant differences in the infiltration of various immune cells, including CD8 T cells, T regulatory cells, and natural killer cells, and LMDEGs such as STAT3, LDHB, LDHA, PDP1, GSR, FOXM1, and CDKN3 were significantly associated with various immune cells. Conclusion: The LMDEGs are significantly associated with the immune-inflammatory response in septic shock and have a certain diagnostic accuracy for septic shock.", "claims": [{"id": "claim-method", "statement": "To identify differentially expressed genes (DEGs) associated with septic shock, we employed the limma package to normalize the data sets GSE26440 and GSE131761. Subsequently, limma package was utilized to perform differential analysis on the datasets GSE26440 and GSE131761 to determine DEGs between various groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37564869", "locator": "sections[4] \"2.2 Identification of LMDEGs related to septic shock\" paragraph 2", "quote": "To identify differentially expressed genes (DEGs) associated with septic shock, we employed the limma package to normalize the data sets GSE26440 and GSE131761. Subsequently, limma package was utilized to perform differential analysis on the datasets GSE26440 and GSE131761 to determine DEGs between various groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26440 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131761.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37564869", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26440 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131761."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Limma-based differential-expression analysis of the GSE26440 and GSE131761 whole-blood datasets, followed by intersection with GeneCards lactate metabolism-related genes, identified 10 lactate metabolism-related differentially expressed genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37564869", "locator": "sections[13] \"3.1 Identification of LMDEGs\" paragraph 4", "quote": "By taking the intersection of all the DEGs with |logFC| > 0 and P.adj < 0.05 from both datasets, we obtained a list of 1,969 common DEGs (Figure 3C). We further intersected these common DEGs with LMGs, resulting in the identification of 10 LMDEGs (Figure 3D), including lactate dehydrogenase B (LDHB), signal transducer and activator of transcription 3 (STAT3), lactate dehydrogenase A (LDHA), glutathione-disulfide reductase (GSR), forkhead box protein M1 (FOXM1), pyruvate dehyrogenase phosphatase catalytic subunit 1 (PDP1), glutaryl-CoA dehydrogenase (GCDH), glucokinase regulatory protein (GCKR), ATP-binding cassette subfamily C member 1 (ABCC1), and cyclin-dependent kinase inhibitor 3 (CDKN3)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Limma-based differential-expression analysis of the GSE26440 and GSE131761 whole-blood datasets, followed by intersection with GeneCards lactate metabolism-related genes, identified 10 lactate metabolism-related differentially expressed genes.", "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:37564869"], "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:GSE131761"], "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:GSE131761"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37564869", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2023.1223243", "sha256": "sha256:89914c1485c7fa13c0ba767f8f8147c687b8d875d1847d7fa88d90d20e08768f", "bytes": 74242, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2023.1223243", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10410269", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE131761", "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=GSE131761", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE131761", "version": null, "note": "Publicly available datasets were analyzed in this study. 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