Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39103477", "kind": "science-record", "revision": 3, "content_hash": "sha256:8e9142d1279b4ccedb7cf92a35b1dc1af3731916ab5c775f34a3585b26c43dfd", "created_at": "2026-11-01T20:29:10Z", "updated_at": "2026-11-01T22:12:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis.", "doi": "10.1038/s41598-024-69185-x", "pmid": "39103477", "authors": ["Linfeng Tao", "Yanyou Zhou", "Lifang Wu", "Jun Liu"], "journal": "Scientific reports", "year": 2024, "abstract": "Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package \"ConsensusClusterPlus\" on the bulk RNA-seq data. Hub genes were discerned through the application of the R package \"limma\" and univariate regression analysis, with the calculation of risk scores carried out using the R package \"survminer\". To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan-Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages \"ESTIMATE\" and \"GSVA\" were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.", "claims": [{"id": "claim-method", "statement": "Finally, the RSF, which showed the highest C-index, was used to identify key genes with importance scores higher than 0.01 to develop a prognostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39103477", "locator": "sections[6] \"Establishment and validation of a prognostic model based on the prognostic genes in sepsis\" paragraph 2", "quote": "Finally, the RSF, which showed the highest C-index, was used to identify key genes with importance scores higher than 0.01 to develop a prognostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets (GSE66099 and GSE65682) for healthy and sepsis samples were acquired from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) in this study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39103477", "locator": "sections[23] \"notes\" paragraph 1", "quote": "The datasets (GSE66099 and GSE65682) for healthy and sepsis samples were acquired from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) in this study."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The RSF-based model identified nine hub genes with an importance value greater than 0.01: SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39103477", "locator": "sections[13] \"Construction of a prognostic model for sepsis\" paragraph 2", "quote": "We ranked genes based on their importance (Fig. 9B) and identified nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with an importance value greater than 0.01."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The RSF-based model identified nine hub genes with an importance value greater than 0.01: SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA.", "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:39103477"], "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:GSE65682"], "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:GSE65682"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39103477", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-024-69185-x", "sha256": 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