Correlation of senescence-related gene FEN1 on neuroblastoma progression and cisplatin chemotherapy sensitivity.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40612863", "kind": "science-record", "revision": 2, "content_hash": "sha256:8afff661bf3f75c336375d86c2f66c4846d2aee67327168bd11523b709f80893", "created_at": "2026-11-01T20:30:42Z", "updated_at": "2026-11-01T21:35:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Correlation of senescence-related gene FEN1 on neuroblastoma progression and cisplatin chemotherapy sensitivity.", "doi": "10.32604/or.2025.060021", "pmid": "40612863", "authors": ["Youyang Hu", "Yishu Luo", "Tianyue Xie", "Yuehua Chen", "Jun Zhao", "Weichao Ji", "Zhiwei Yan", "Sitong Qiu", "Kexin Gao", "Haixia Zhu", "Limin Ma", "Qiyou Yin"], "journal": "Oncology research", "year": 2025, "abstract": "Objective: Neuroblastoma (NB) is frequently associated with high-risk pediatric cases that demonstrate limited response to cisplatin, contributing to a poor prognosis. Recent studies have explored the role of tumor cell senescence in increasing sensitivity to this chemotherapy agent. This study aims to identify genes related to cell senescence in children diagnosed with NB, evaluate their influence on cisplatin sensitivity, and investigate potential strategies to enhance the efficacy of chemotherapy.\nMethods: Gene expression profiles and clinical data were obtained for 498 NB patients from the GEO database (GSE49710). The study focused on identifying genes that were differentially expressed between stage IV and other stages, particularly those linked to cell senescence and cisplatin resistance. To analyze the prognostic significance of these differentially expressed genes, we employed LASSO regression and multivariate Cox proportional hazards models. Transcriptomic and proteomic analyses of 15 NB specimens revealed a significant correlation between Flap endonuclease-1 (FEN1) expression levels and both cellular senescence and sensitivity to cisplatin. We quantified FEN1 expression and cisplatin IC50 values in four different NB cell lines. The influence of FEN1 knockdown and overexpression on NB cell proliferation, invasion, and migration was evaluated using cloning assays, transwell assays, and scratch assays. Furthermore, we utilized Western blotting to analyze senescence-associated proteins p21 and proliferating cell nuclear antigen (PCNA), thereby evaluating the role of FEN1 in cellular senescence. The impact of FEN1 on cisplatin sensitivity was investigated via the CCK-8 cell counting assay. Additionally, we investigated how FEN1 inhibitors might impact NB cell proliferation and enhance the therapeutic efficacy of cisplatin treatment.\nResults: FEN1 was found to be highly expressed in stage IV NB and showed a strong association with cisplatin sensitivity, establishing it as a critical molecular marker linked to poor patient prognosis. Notably, elevated FEN1 expression correlated with reduced sensitivity to cisplatin, as evidenced by higher IC50 values. In the SH-SY5Y cell line, FEN1 knockdown led to significant reductions in cell proliferation, invasion, and migration, along with an increase in β-galactosidase staining-indicative of senescence. This knockdown also resulted in elevated levels of the p21 protein and decreased expression of PCNA, concurrently lowering cisplatin IC50 values. Conversely, FEN1 overexpression in the SK-N-SH cell line resulted in enhanced cell proliferation, invasion, and migration. This overexpression was associated with reduced β-galactosidase staining, decreased levels of p21, and increased expression of PCNA, ultimately resulting in higher cisplatin IC50 values. Importantly, FEN1 inhibitors alone significantly impeded NB cell proliferation, and their combination with cisplatin further amplified this inhibitory effect compared to cisplatin treatment alone.\nConclusions: Bioinformatics and sequencing analyses indicate that the senescence-related gene FEN1 is significantly associated with cisplatin sensitivity and adverse prognosis in pediatric NB. FEN1 plays a pivotal role in regulating NB cell proliferation, invasion, and migration, thereby facilitating cancer progression. Furthermore, it influences cisplatin sensitivity through its effects on cellular senescence. FEN1 inhibitors demonstrate potential both as monotherapies and in conjunction with cisplatin, suggesting that targeting FEN1 may be represent a valuable strategy for improving outcomes in high-risk NB patients.", "claims": [{"id": "claim-method", "statement": "The analysis employed LASSO regression and multivariate Cox models to assess the impact of gene expression on survival outcomes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40612863", "locator": "sections[5] \"Survival analysis and prognostic modeling\" paragraph 2", "quote": "The analysis employed LASSO regression and multivariate Cox models to assess the impact of gene expression on survival outcomes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated for this manuscript have been included in this article. The publicly available data are provided in GEO databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40612863", "locator": "sections[33] \"Availability of Data and Materials\" paragraph 2", "quote": "All data generated for this manuscript have been included in this article. The publicly available data are provided in GEO databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The optimal prognostic risk model, derived from the LASSO algorithm with a λ value of 0.11, resulted in the following risk score formula: Risk Score = (−0.0504) * CNR1 + (0.0083) * RET + (0.00298) * FEN1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40612863", "locator": "sections[19] \"Construction of a prognostic model using LASSO regression\" paragraph 4", "quote": "The optimal prognostic risk model, derived from the LASSO algorithm with a λ value of 0.11, resulted in the following risk score formula: Risk Score = (−0.0504) * CNR1 + (0.0083) * RET + (0.00298) * FEN1."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The optimal prognostic risk model, derived from the LASSO algorithm with a λ value of 0.11, resulted in the following risk score formula: Risk Score = (−0.0504) * CNR1 + (0.0083) * RET + (0.00298) * FEN1.", "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:40612863"], "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:GSE49710"], "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:GSE49710"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40612863", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.32604/or.2025.060021", "sha256": "sha256:07a850ea558aa8e4e8fadd6e56b6797b64bf7fb3e9518928ba05ab918d0ecf74", "bytes": 63243, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.32604/or.2025.060021", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12215590", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE49710", "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=GSE49710", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE49710", "version": null, "note": "All data generated for this manuscript have been included in this article. The publicly available data are provided in GEO databases.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data generated for this manuscript have been included in this article. 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have been included in this article. The publicly available data are provided in GEO databases.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data generated for this manuscript have been included in this article. 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