Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36761753", "kind": "science-record", "revision": 3, "content_hash": "sha256:a174c7691606de6a8d7985dbcb3e57b2dce71dff5e64ab8731e0ad425fb241d4", "created_at": "2026-11-01T19:01:20Z", "updated_at": "2026-11-01T22:51:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy.", "doi": "10.3389/fimmu.2023.1128390", "pmid": "36761753", "authors": ["Kai Hong", "Kenan Cen", "Qiaoqiao Chen", "Ying Dai", "Yifeng Mai", "Yangyang Guo"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Introduction: Cellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-related genes in thyroid cancer (THCA) and their relationship with the TME.\nMethods: Senescence-related genes were identified from the Molecular Signatures Database and used to conduct consensus clustering across TCGA-THCA. Differentially expressed genes (DEGs) were identified between the clusters used to perform multivariate Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a senescence-related signature. TCGA dataset was randomly divided into training and test datasets to verify the prognostic ability of the signature. Subsequently, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. Finally, the expression of signature genes was detected across TCGA-THCA and GSE33630 datasets, and further validated by RT-qPCR.\nResults: Three senescence clusters were identified based on the expression of 432 senescence-related genes. Then, 23 prognostic DEGs were identified in TCGA dataset. The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-risk THCA shows a better prognosis and higher immunotherapy response than high-risk THCA. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. The validation part demonstrated that ADAMTSL4, DOCK6, FAM111B, and SEMA6B were expressed at higher levels in the tumor tissue, whereas lower expression of MRPS10 and PSMB7 was observed.\nDiscussion: In conclusion, the senescence-related signature is a promising biomarker for predicting the outcome of THCA and has the potential to guide immunotherapy.", "claims": [{"id": "claim-method", "statement": "The expression of 432 senescence genes was used to conduct the consensus clustering with the “ConsensusClusterPlus” R package (13). Univariate Cox regression analysis was then conducted to calculate the prognostic DEGs, with HR<1 or >1 regarded as protective or risk genes (p < 0.05). To avoid overfitting, least absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify signature genes using the “glmnet” package (15). Kaplan–Meier survival analyses with log-rank tests were performed to assess the significant differences in OS between the two groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36761753", "locator": "sections[4] \"Identification of the senescence clustering\" paragraph 2", "quote": "The expression of 432 senescence genes was used to conduct the consensus clustering with the “ConsensusClusterPlus” R package (13)."}, {"asset": "source:pmid:36761753", "locator": "sections[5] \"Establishment of the senescence-related signature\" paragraph 2", "quote": "Univariate Cox regression analysis was then conducted to calculate the prognostic DEGs, with HR<1 or >1 regarded as protective or risk genes (p < 0.05)."}, {"asset": "source:pmid:36761753", "locator": "sections[5] \"Establishment of the senescence-related signature\" paragraph 2", "quote": "To avoid overfitting, least absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify signature genes using the “glmnet” package (15)."}, {"asset": "source:pmid:36761753", "locator": "sections[13] \"Statistical analysis\" paragraph 2", "quote": "Kaplan–Meier survival analyses with log-rank tests were performed to assess the significant differences in OS between the two groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36761753", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36761753", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity.", "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:36761753"], "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:GSE33630"], "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:GSE33630"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36761753", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1128390", "sha256": "sha256:448dc0182ab133e8a08c042c1b275beb6ee47f922419a0ce2b567e2a10fc0d64", "bytes": 37532, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1128390", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9902917", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE33630", "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=GSE33630", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE33630", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/ Supplementary Material . 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Results: Three senescence clusters were ide", "E33630 datasets, and further validated by RT-qPCR. Results: Three senescence clusters were ide", "Methods and materials Cell culture and RT-qPCR The normal thyroid cell line (Nthy ori-3-1)", "ry DNA (cDNA) using the Hifair® III One-Step RT-qPCR SYBR Green Kit (Yeasen, China). RT-qPCR was"], "order_basis": "paper_text_order:1226", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The expression of 432 senescence genes was used to conduct the consensus clustering with the “ConsensusClusterPlus” R package (13). Univariate Cox regression analysis was then conducted to calculate the prognostic DEGs, with HR<1 or >1 regarded as protective or risk genes (p < 0.05). To avoid overfitting, least absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify signature genes using the “glmnet” package (15). Kaplan–Meier survival analyses with log-rank tests were performed to assess the significant differences in OS between the two groups."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}