Methylation related genes are associated with prognosis of patients with head and neck squamous cell carcinoma via altering tumor immune microenvironment.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36643267", "kind": "science-record", "revision": 3, "content_hash": "sha256:22a23c1d4261a9ee1fbcdf0749e5ccf8bd94b2321279cfd31cf185377efcfc33", "created_at": "2026-11-01T19:01:11Z", "updated_at": "2026-11-01T22:51:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Methylation related genes are associated with prognosis of patients with head and neck squamous cell carcinoma via altering tumor immune microenvironment.", "doi": "10.1016/j.jds.2022.07.010", "pmid": "36643267", "authors": ["Xudong Tian", "Congyu Shi", "Shan Liu", "Chengzhi Zhao", "Xiaoyi Wang", "Yubin Cao"], "journal": "Journal of dental sciences", "year": 2023, "abstract": "Background/purpose: Analysis of methylomes may enable prognostic stratification in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to identify methylation-related differentially expressed genes (mrDEGs), and to assess their efficacy in predicting patients' survival, tumor immune microenvironment alterations and immune checkpoints in patients with HNSCC.\nMaterials and methods: The methylome and transcriptome data of 528 HNSCC and 50 normal samples from TCGA database were used as training cohort. We identified mrDEGs and constituted a risk score model using Kaplan-Meier analysis and multivariate Cox regression. The prognostic efficacy of the risk score was validated in GSE65858 and GSE41613. We determined the enrichment of previously defined biological processes of mrDEGs. We separated the HNSCC patients into low-risk and high-risk groups and compared their immune cell infiltration and immune checkpoints' expressions.\nResults: The risk score model was constituted by nine prognostic mrDEGs, including LIMD2, SYCP2, EPHX3, UCLH1, STC2, PRAME, SLC7A4, PLOD2, and ACADL. The risk score was a significant prognostic factor both in training (P < 0.001) and validation dataset (GSE65858: P = 0.008; GSE41613 = 0.015). The prognostic mrDEGs were enriched in multiple immune-associated pathways. Effector immune cells were increased in low-risk patients, including CD8+ T cells, activated CD4+ T cells, and plasma cells, whereas tumor associated M2 macrophages were recruited in the high-risk group. Expressions of immune checkpoints were generally higher in low-risk patients, including CTLA-4, PD-1 and LAG3.\nConclusion: The mrDEGs can stratify HNSCC patients' prognosis, which correlates with alterations in tumor immune infiltrations and immune checkpoints.", "claims": [{"id": "claim-method", "statement": "We identified mrDEGs and constituted a risk score model using Kaplan-Meier analysis and multivariate Cox regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36643267", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We identified mrDEGs and constituted a risk score model using Kaplan-Meier analysis and multivariate Cox regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded DNA methylation (528 HNSCC samples and 50 normal samples), RNA-sequencing (500 HNSCC samples and 40 normal samples) and clinical information data of The Cancer Genome Atlas Program (TCGA) HNSCC cohort from UCSC-Xena (https://xenabrowser.net/) on August 28th, 2020.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36643267", "locator": "sections[3] \"Data acquisition and preprocessing\" paragraph 2", "quote": "We downloaded DNA methylation (528 HNSCC samples and 50 normal samples), RNA-sequencing (500 HNSCC samples and 40 normal samples) and clinical information data of The Cancer Genome Atlas Program (TCGA) HNSCC cohort from UCSC-Xena (https://xenabrowser.net/) on August 28th, 2020."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The risk score was a significant prognostic factor both in training (P < 0.001) and validation dataset (GSE65858: P = 0.008; GSE41613 = 0.015).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36643267", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The risk score was a significant prognostic factor both in training (P < 0.001) and validation dataset (GSE65858: P = 0.008; GSE41613 = 0.015)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The risk score was a significant prognostic factor both in training (P < 0.001) and validation dataset (GSE65858: P = 0.008; GSE41613 = 0.015).", "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:36643267"], "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:GSE41613"], "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:GSE41613"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36643267", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.jds.2022.07.010", "sha256": 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