Identification and prognostic analysis of propionate metabolism-related genes in head and neck squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40575157", "kind": "science-record", "revision": 3, "content_hash": "sha256:5464803aa03a6f06f2a51a9babc4ad0666b99ad53918ce209e6d914e19ff0adb", "created_at": "2026-11-01T20:30:39Z", "updated_at": "2026-11-01T22:14:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and prognostic analysis of propionate metabolism-related genes in head and neck squamous cell carcinoma.", "doi": "10.3389/fonc.2025.1518587", "pmid": "40575157", "authors": ["Shitong Zhou", "Yu Jiang", "Panhui Xiong", "Zhongwan Li", "Lifeng Jia", "Wei Yuan", "Xiufu Liao", "Xiang An", "Jie Hu", "Rui Luo", "Hailan Mo", "Hongyan Fang", "Yucheng Yang"], "journal": "Frontiers in oncology", "year": 2025, "abstract": "Introduction: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor overall prognosis. Recent studies have suggested that propionate metabolism-related genes (PMRGs) may play key roles in tumor progression and immune regulation, yet their functions in HNSCC remain unclear.\nMethods: Transcriptomic data from 502 HNSCC tumor samples and 44 normal tissue samples were obtained from the UCSC Xena database as the training set. Two independent datasets (GSE41613 and GSE6631) from the GEO database were used for validation. Differentially expressed genes (DEGs), key module genes identified via weighted gene co-expression network analysis (WGCNA), and PMRGs were intersected to identify candidate genes. A prognostic model was constructed using Cox regression and LASSO analysis. Immune infiltration, somatic mutations, and drug sensitivity were compared between high- and low-risk groups. Gene expression was further validated by RT-qPCR using clinical samples.\nResults: A total of 42 intersecting genes were identified, and four feature genes (PRKAA2, SLC7A5, GRIP2, CHGB) were selected to build the prognostic model. The model effectively stratified patients into high- and low-risk groups with significant survival differences in both the training and validation cohorts. The high-risk group exhibited marked differences in immune cell infiltration, immune checkpoint expression, and cancer immune cycle activity. Mutation burden and drug sensitivity also varied significantly between risk groups. A nomogram combining risk score and pathological N stage showed strong predictive performance.\nDiscussion: This study highlights the potential role of PMRGs in immune regulation and tumor progression in HNSCC. The proposed four-gene signature provides a novel tool for prognosis prediction and offers new insights for risk stratification and individualized therapy. Further multicenter validation and mechanistic studies are warranted.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was performed on the intersecting genes in the training set to calculate the p-values, hazard ratios (HRs), and their 95% confidence intervals (CIs) for each gene (p< 0.05, HRs ≠ 1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40575157", "locator": "sections[5] \"Prognostic risk model\" paragraph 2", "quote": "Univariate Cox regression analysis was performed on the intersecting genes in the training set to calculate the p-values, hazard ratios (HRs), and their 95% confidence intervals (CIs) for each gene (p< 0.05, HRs ≠ 1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40575157", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A prognostic risk model based on PRKAA2, SLC7A5, GRIP2, and CHGB stratified HNSCC patients into high- and low-risk groups with significantly different survival in the training and validation cohorts, with a training-set log-rank p<0.0001.", "kind": "quantitative", "target": {"value": 0.0001, "unit": "p-value", "tolerance": null, "source": "As the risk score increased, mortality rates also increased ( Figure 2D ), with patients in the low-risk group exhibiting significantly longer survival (Log-rank test p< 0.0001) ( Figure 2E )."}, "evidence": [{"asset": "source:pmid:40575157", "locator": "sections[14] \"Prognostic risk models were constructed based on PRKAA2, SLC7A5, GRIP2, and CHGB\" paragraph 2", "quote": "Patients were stratified into high- (n = 250) and low-risk (n = 251) groups based on the median risk score."}, {"asset": "source:pmid:40575157", "locator": "sections[14] \"Prognostic risk models were constructed based on PRKAA2, SLC7A5, GRIP2, and CHGB\" paragraph 2", "quote": "As the risk score increased, mortality rates also increased ( Figure 2D ), with patients in the low-risk group exhibiting significantly longer survival (Log-rank test p< 0.0001) ( Figure 2E )."}, {"asset": "source:pmid:40575157", "locator": "sections[14] \"Prognostic risk models were constructed based on PRKAA2, SLC7A5, GRIP2, and CHGB\" paragraph 2", "quote": "External validation in the GSE41613 dataset yielded consistent results with the training set ( Figures 3A–D )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A prognostic risk model based on PRKAA2, SLC7A5, GRIP2, and CHGB stratified HNSCC patients into high- and low-risk groups with significantly different survival in the training and validation cohorts, with a training-set log-rank p<0.0001.", "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:40575157"], "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. 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