Identification of a metabolic reprogramming-related signature associated with prognosis and immune microenvironment of head and neck squamous cell carcinoma by in silico analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35301800", "kind": "science-record", "revision": 3, "content_hash": "sha256:5c7359521161b63553cd917649836428076aee775906a5299ffea46c6a51844f", "created_at": "2026-11-01T18:14:05Z", "updated_at": "2026-11-01T21:27:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a metabolic reprogramming-related signature associated with prognosis and immune microenvironment of head and neck squamous cell carcinoma by in silico analysis.", "doi": "10.1002/cam4.4670", "pmid": "35301800", "authors": ["Weijie Qiang", "Yifei Dai", "Xiaoyan Xing", "Xiaobo Sun"], "journal": "Cancer medicine", "year": 2022, "abstract": "BACKGROUND: Metabolic reprogramming is one of the essential features of tumorigenesis. Herein, this study aimed to develop a novel metabolism-related gene signature for head and neck squamous cell carcinoma (HNSCC) patients.\nMETHODS: The transcriptomic and clinical data of HNSCC samples were collected from The Cancer Genome Atlas (TCGA) and GSE65858 datasets. The metabolism-related gene-based prognostic signature (MRGPS) was constructed by the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (K-M) survival curves were plotted for evaluating its predicting performance. At the same time, univariate along with multivariate analysis was carried out to explore its correlation with clinicopathologic factors. Furthermore, GSEA analysis was performed to explore the signaling pathways affected by MRGPS. We also analyzed the associations of MRGPS with the tumor immune microenvironment (TIME), as well as identified potential compounds via Connectivity Map (CMap) and molecular docking.\nRESULTS: A total of 12 differentially expressed metabolism-related genes were identified and selected to construct the MRGPS. Notably, this signature performed well in predicting HNSCC patients' survival and could serve as an independent prognostic factor in multiple datasets. In addition to the metabolism-related pathway, this signature could also affect some immune-related pathways. The results indicated that MRGPS is correlated with immune cells infiltration and anti-cancer immune response. Furthermore, we identified cephaeline as a potential therapeutic compound for HNSCC.\nCONCLUSION: Taken together, we established an MRGs-based signature that has the potential to predict the clinical outcome and immune microenvironment, which help to search for potential combination immunotherapy compounds and provide a promising therapeutic strategy for treating HNSCC patients.", "claims": [{"id": "claim-method", "statement": "Thereafter, the most optimal gene set was selected using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized Cox proportional hazards regression through R package “glmnet.”", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35301800", "locator": "sections[5] \"Construction of MRGs‐based prognostic signature (MRGPS) and functional enrichment analysis\" paragraph 2", "quote": "Thereafter, the most optimal gene set was selected using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized Cox proportional hazards regression through R package “glmnet.”"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the TCGA (https://portal.gdc.cancer.gov/) and GEO data sets (https://www.ncbi.nlm.nih.gov/gds/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35301800", "locator": "sections[27] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The data used to support the findings of this study are available from the TCGA (https://portal.gdc.cancer.gov/) and GEO data sets (https://www.ncbi.nlm.nih.gov/gds/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "MRGPS performed well in predicting HNSCC patients’ survival, with 1-, 3-, and 5-year survival area under the curve values of 0.697, 0.727, and 0.679, respectively, in the TCGA training set.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35301800", "locator": "sections[15] \"Evaluation of the MRGPS predicting ability in different data sets\" paragraph 2", "quote": "It was calculated that the area under the curve values for 1‐, 3‐, and 5‐year survival were 0.697, 0.727, and 0.679, separately, in the TCGA training set, indicating that MRGPS performed well in monitoring survival."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "MRGPS performed well in predicting HNSCC patients’ survival, with 1-, 3-, and 5-year survival area under the curve values of 0.697, 0.727, and 0.679, respectively, in the TCGA training set.", "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:35301800"], "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:GSE65858"], "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:GSE65858"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35301800", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/cam4.4670", "sha256": 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