Identification of an immune-related genes signature to predict risk of recurrence for patients with laryngeal squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37073715", "kind": "science-record", "revision": 4, "content_hash": "sha256:6eef606c2ba4c34e2cb5e12b65d4c0099f9f9c76c8191f68d41386c9d60f4cec", "created_at": "2026-11-01T19:01:44Z", "updated_at": "2026-11-01T22:51:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of an immune-related genes signature to predict risk of recurrence for patients with laryngeal squamous cell carcinoma.", "doi": "10.1177/03946320231172075", "pmid": "37073715", "authors": ["Xiu Hong", "Yang Li", "Qian Lv", "Jun An", "Ying Liu", "Xiuli Wang", "Rui Li", "Yuqiang Hu", "Bing Liu"], "journal": "International journal of immunopathology and pharmacology", "year": 2023, "abstract": "OBJECTIVE: Numerous studies indicate that immune-related genes (IRGs) are closely related to tumorigenesis and tumor progression. We aimed to establish a robust IRGs-based signature to predict risk of recurrence for patients with laryngeal squamous cell carcinoma (LSCC).\nMETHODS: Gene expression profiles were acquired to select differentially expressed IRGs (DEIRGs) between tumor and adjacent normal tissues. Functional enrichment analysis was performed to explore the biological roles of DEIRGs in LSCC. Univariate Cox analyses and LASSO regression model were used to construct a IRGs-based signature with the ability to predict recurrence for LSCC patients.\nRESULTS: A total of 272 DEIRGs were identified, of which 20 DEIRGs were significantly associated with recurrence-free survival (RFS). Subsequently, we constructed an eleven-IRGs signature that could classify patients into high-risk or low-risk groups in TCGA-LSCC training cohort. Patients in high-risk groups suffered from shorter RFS (log-rank p = 9.69E-06). Besides, the recurrence rate of high-risk group was significantly higher than that of low-risk group (41.1% vs. 13.7%; Fisher's exact test p < 0.001). The predictive performance was validated in an independent cohort (GSE27020, log-rank p = 1.43E-03). Person correlation analysis showed that the risk scores calculated by eleven-IRGs signature were significantly associated with filtrating immune cells. Furthermore, three immune checkpoint molecules were significantly over-expressed in the high-risk group.\nCONCLUSION: Our findings for the first time constructed a robust IRGs-based signature to precisely predict risk of recurrence and further provided a deeper understanding of IRGs regulatory mechanism in LSCC pathogenesis.", "claims": [{"id": "claim-method", "statement": "DEIRGs, which were significantly associated with relapse-free survival (RFS) using univariate Cox regression analysis, were selected to establish the prognostic signature by the least absolute shrinkage and selection operator (LASSO) method in TCGA-LSCC training cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37073715", "locator": "sections[6] \"Establishment of the IRGs-based signature for LSCC\" paragraph 2", "quote": "DEIRGs, which were significantly associated with relapse-free survival (RFS) using univariate Cox regression analysis, were selected to establish the prognostic signature by the least absolute shrinkage and selection operator (LASSO) method in TCGA-LSCC training cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data availability: The source data of this study were derived from the public repositories, as indicated in Materials and Methods. All data that support the findings of this study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37073715", "locator": "sections[18] \"fn-group\" paragraph 1", "quote": "Data availability: The source data of this study were derived from the public repositories, as indicated in Materials and Methods. All data that support the findings of this study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed an eleven-IRGs signature that classified LSCC patients into high-risk and low-risk groups and predicted recurrence risk in both the TCGA-LSCC training cohort and the GSE27020 validation cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37073715", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Subsequently, we constructed an eleven-IRGs signature that could classify patients into high-risk or low-risk groups in TCGA-LSCC training cohort."}, {"asset": "source:pmid:37073715", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Patients in high-risk groups suffered from shorter RFS (log-rank p = 9.69E−06)."}, {"asset": "source:pmid:37073715", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The predictive performance was validated in an independent cohort (GSE27020, log-rank p = 1.43E-03)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed an eleven-IRGs signature that classified LSCC patients into high-risk and low-risk groups and predicted recurrence risk in both the TCGA-LSCC training cohort and the GSE27020 validation cohort.", "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:37073715"], "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:GSE127165"], "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:GSE127165"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37073715", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1177/03946320231172075", "sha256": "sha256:e3fb1744ff5e95f4d5539694a5d5c4780cf13cf3cf4b71f233b99450d4bc6d25", "bytes": 29308, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1177/03946320231172075", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10127216", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE127165", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE127165", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE127165", "version": null, "note": "Data availability: The source data of this study were derived from the public repositories, as indicated in Materials and Methods. 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