Lactylation-related gene signature for prognostic prediction and immune infiltration analysis in breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38318076", "kind": "science-record", "revision": 3, "content_hash": "sha256:456b75b2e7d78665051a53f19e6f4ba1602fe3cd92a2be9531f90a913d7bfbbd", "created_at": "2026-11-01T19:20:15Z", "updated_at": "2026-11-01T22:11:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Lactylation-related gene signature for prognostic prediction and immune infiltration analysis in breast cancer.", "doi": "10.1016/j.heliyon.2024.e24777", "pmid": "38318076", "authors": ["Yangchi Jiao", "Fuqing Ji", "Lan Hou", "Yonggang Lv", "Juliang Zhang"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Lactylation is implicated in various aspects of tumor biology, but its relation to breast cancer remains poorly understood. This study aimed to explore the roles of the lactylation-related genes in breast cancer and its association with the tumor microenvironment.\nMethods: The expression and mutation patterns of lactylation-related genes were analyzed using the breast cancer data from The Cancer Genome Atlas (TCGA) database and GSE20685 datasets. Unsupervised clustering was used to identify two lactylation clusters. A lactylation-related gene signature was developed and validated using the training and validation cohorts. Immune cell infiltration and drug response were assessed.\nResults: We analyzed the mRNA expression, copy number variations, somatic mutations, and correlation networks of 22 lactylation-related genes in breast cancer tissues. We identified two distinct lactylation clusters with different survival outcomes and immune microenvironments. We further classified the patients into two gene subtypes based on lactylation clusters and identified a 7-gene signature for breast cancer survival prognosis. The prognostic score based on this signature demonstrated prognostic value and predicted the therapeutic response.\nConclusion: Lactylation-related genes play a critical role in breast cancer by influencing tumor growth, immune microenvironment, and drug response. This lactylation-related gene signature may serve as a prognostic marker and a potential therapeutic target for breast cancer.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was performed on the training cohort to identify lactylation-related DEGs associated with OS (the length of time from the start of a clinical study or treatment until death from any cause) [18], followed by the LASSO algorithm using the R package \"glmnet\" to select the most promising prognostic DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38318076", "locator": "sections[9] \"Development of the DEG model and prognostic score\" paragraph 2", "quote": "Univariate Cox regression analysis was performed on the training cohort to identify lactylation-related DEGs associated with OS (the length of time from the start of a clinical study or treatment until death from any cause) [18], followed by the LASSO algorithm using the R package \"glmnet\" to select the most promising prognostic DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "For this study, in order to obtain training samples and validate the results across different data sets, gene expression profiles and clinical data were compiled from 327 breast cancer tissues available on the GEO data portal (https://www.ncbi.nlm.nih.gov/geo/) (GSE20685, which has a large number of samples in the data set and relatively comprehensive clinical follow-up data of patients)", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38318076", "locator": "sections[3] \"Data acquisition and preprocessing\" paragraph 2", "quote": "For this study, in order to obtain training samples and validate the results across different data sets, gene expression profiles and clinical data were compiled from 327 breast cancer tissues available on the GEO data portal (https://www.ncbi.nlm.nih.gov/geo/) (GSE20685, which has a large number of samples in the data set and relatively comprehensive clinical follow-up data of patients)"}], "state": "complete"}, {"id": "claim-target-result", "statement": "A seven-gene lactylation-related gene signature was developed and showed prognostic value for breast cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38318076", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We further classified the patients into two gene subtypes based on lactylation clusters and identified a 7-gene signature for breast cancer survival prognosis."}, {"asset": "source:pmid:38318076", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The prognostic score based on this signature demonstrated prognostic value and predicted the therapeutic response."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A seven-gene lactylation-related gene signature was developed and showed prognostic value for breast cancer.", "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:38318076"], "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:GSE20685"], "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:GSE20685"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38318076", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e24777", "sha256": 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