Transcriptomic signature associated with RNA-binding proteins for survival stratification of laryngeal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35985767", "kind": "science-record", "revision": 3, "content_hash": "sha256:14d83f006ad6f4678549568722f4bef71faffac96a4af40bf86456099d158353", "created_at": "2026-11-01T18:15:07Z", "updated_at": "2026-11-01T21:28:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcriptomic signature associated with RNA-binding proteins for survival stratification of laryngeal cancer.", "doi": "10.18632/aging.204234", "pmid": "35985767", "authors": ["Yujie Shen", "Huijun Chen", "Qiang Huang", "Huaidong Du", "Liang Zhou"], "journal": "Aging", "year": 2022, "abstract": "RNA-binding proteins (RBPs) have been suggested as important prognostic indicators in different human cancers. This study was designed to search the prognostic value of RBPs of laryngeal squamous cell carcinoma (LSCC). Differentially expressed RBPs (DERBPs) were screened via The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify prognostic DERBPs. Expression profiling of training cohort were calculated to develop a transcriptomic signature, which was validated by three independent cohorts (TCGA cohort, GSE65858 cohort and GSE27020 cohort). We identified DERBPs and a set of signatures (GTPBP3, KHDRBS3 and RBM38) were confirmed as prognosis-related hub DERBPs in LSCC, which was also tested and verified by bioinformatics method and molecular biology experiment. The role of immune cell infiltration and drug resistance between subgroups was explored. Furthermore, the risk score based on transcriptomic signature was turned out to be an independent prognostic indicator for LSCC. Finally, a nomogram for further clinical application was established. Our study demonstrated that the transcriptomic signature we constructed could serve as a novel therapeutic target and biomarker for LSCC from the perspective of RBPs.", "claims": [{"id": "claim-method", "statement": "The univariate Cox regression analysis was served to select the prognostic DERBPs in training cohort, and LASSO analysis method was performed to avoid overfitting. Multivariate Cox regression analysis was applied to determine the prognostic signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35985767", "locator": "sections[7] \"Identification and validation of the transcriptomic signature\" paragraph 2", "quote": "The univariate Cox regression analysis was served to select the prognostic DERBPs in training cohort, and LASSO analysis method was performed to avoid overfitting. Multivariate Cox regression analysis was applied to determine the prognostic signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Transcript data were obtained from TCGA (https://portal.gdc.cancer.gov), which containing 111 LSCC samples and 12 paired samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35985767", "locator": "sections[3] \"Detection of DERBPs\" paragraph 2", "quote": "Transcript data were obtained from TCGA (https://portal.gdc.cancer.gov), which containing 111 LSCC samples and 12 paired samples."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Results from different independent cohorts showed that this prognostic signature had decent discriminative ability in predicting the prognosis of LSCC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35985767", "locator": "sections[24] \"CONCLUSIONS\" paragraph 2", "quote": "Results from different independent cohorts showed that this prognostic signature had decent discriminative ability in predicting the prognosis of LSCC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Results from different independent cohorts showed that this prognostic signature had decent discriminative ability in predicting the prognosis of LSCC patients.", "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:35985767"], "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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