Holliday junction recognition protein (HJURP) could reflect the clinical outcomes of lung adenocarcinoma patients, and impact the choice of precision therapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39649097", "kind": "science-record", "revision": 2, "content_hash": "sha256:ba64dda09e6627482eb111f6e5902d7e92df3fe2f7246dade6cd699d91a8ac80", "created_at": "2026-11-01T20:29:55Z", "updated_at": "2026-11-01T21:34:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Holliday junction recognition protein (HJURP) could reflect the clinical outcomes of lung adenocarcinoma patients, and impact the choice of precision therapy.", "doi": "10.3389/fgene.2024.1475511", "pmid": "39649097", "authors": ["Xixi Gao", "Yingqing Zhang", "Ming Zhang", "Yuejiao Sun"], "journal": "Frontiers in genetics", "year": 2024, "abstract": "Background: Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer (NSCLC), characterized by poor prognosis and a high mortality rate. Identifying reliable prognostic biomarkers and potential therapeutic targets is crucial for improving patient outcomes.\nMethods: We conducted a comprehensive analysis of HJURP expression in LUAD using data from four cohorts: TCGA-LUAD (n = 453), GSE31210 (n = 226), GSE68465 (n = 442), and GSE72094 (n = 386). Univariate Cox regression analysis was employed to identify prognostic genes, with Kaplan-Meier survival analysis used to assess the predictive power of HJURP. Functional enrichment analyses were performed using MetaScape and FGSEA, and spatial transcriptomics and single-cell sequencing data were analyzed to explore HJURP's distribution and potential functions. Additionally, correlations between HJURP expression and genetic alterations, immune cell infiltration, and potential therapeutic responses were evaluated.\nResults: HJURP was identified as a significant prognostic biomarker in all four cohorts, with high expression associated with increased risk of overall survival (OS) death (TCGA-LUAD: HR = 1.93, 95% CI: 1.321-2.815, P < 0.001; GSE31210: HR = 2.75, 95% CI: 1.319-5.735, P = 0.007; GSE68465: HR = 1.57, 95% CI: 1.215-2.038, P < 0.001; GSE72094: HR = 2.2, 95% CI: 1.485-3.27, P < 0.001). Functional analyses indicated that HJURP is involved in DNA metabolic processes, cell cycle regulation, and mitotic processes, with significant activation of pathways related to MYC targets, G2M checkpoint, and DNA repair. High HJURP expression was associated with higher mutation frequencies in TP53, CSMD3, TTN, and MUC16, and positively correlated with pro-inflammatory immune cell infiltration and several immune checkpoints, including PD-L1 and PD-L2. Chemotherapeutic agents such as gefitinib and sorafenib were predicted to be effective against high HJURP-expressing tumors.\nConclusion: HJURP is a pivotal biomarker for LUAD, consistently associated with poor prognosis and advanced disease stages. Its high expression correlates with specific genetic alterations and immune profiles, highlighting its potential as a therapeutic target. Future studies should validate these findings in larger cohorts.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was employed to identify prognostic genes, with Kaplan-Meier survival analysis used to assess the predictive power of HJURP.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39649097", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate Cox regression analysis was employed to identify prognostic genes, with Kaplan-Meier survival analysis used to assess the predictive power of HJURP."}], "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 author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39649097", "locator": "sections[20] \"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 author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "HJURP was identified as a significant prognostic biomarker in all four cohorts, with high expression associated with increased risk of overall survival (OS) death (TCGA-LUAD: HR = 1.93, 95% CI: 1.321–2.815, P < 0.001; GSE31210: HR = 2.75, 95% CI: 1.319–5.735, P = 0.007; GSE68465: HR = 1.57, 95% CI: 1.215–2.038, P < 0.001; GSE72094: HR = 2.2, 95% CI: 1.485–3.27, P < 0.001).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "HJURP was identified as a significant prognostic biomarker in all four cohorts, with high expression associated with increased risk of overall survival (OS) death (TCGA-LUAD: HR = 1.93, 95% CI: 1.321–2.815, P < 0.001; GSE31210: HR = 2.75, 95% CI: 1.319–5.735, P = 0.007; GSE68465: HR = 1.57, 95% CI: 1.215–2.038, P < 0.001; GSE72094: HR = 2.2, 95% CI: 1.485–3.27, P < 0.001)."}, "evidence": [{"asset": "source:pmid:39649097", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "HJURP was identified as a significant prognostic biomarker in all four cohorts, with high expression associated with increased risk of overall survival (OS) death (TCGA-LUAD: HR = 1.93, 95% CI: 1.321–2.815, P < 0.001; GSE31210: HR = 2.75, 95% CI: 1.319–5.735, P = 0.007; GSE68465: HR = 1.57, 95% CI: 1.215–2.038, P < 0.001; GSE72094: HR = 2.2, 95% CI: 1.485–3.27, P < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "HJURP was identified as a significant prognostic biomarker in all four cohorts, with high expression associated with increased risk of overall survival (OS) death (TCGA-LUAD: HR = 1.93, 95% CI: 1.321–2.815, P < 0.001; GSE31210: HR = 2.75, 95% CI: 1.319–5.735, P = 0.007; GSE68465: HR = 1.57, 95% CI: 1.215–2.038, P < 0.001; GSE72094: HR = 2.2, 95% CI: 1.485–3.27, P < 0.001).", "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:39649097"], "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:GSE31210"], "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:GSE31210"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39649097", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2024.1475511", "sha256": 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directed to the corresponding author.", "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, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM773540", "GSM773541", "GSM773542", "GSM773543", "GSM773544", "GSM773545", "GSM773546", "GSM773547", "GSM773548", "GSM773549", "GSM773550", "GSM773551", "GSM773552", "GSM773553", "GSM773554", "GSM773555", "GSM773556", "GSM773557", "GSM773558", "GSM773559", "GSM773560", "GSM773561", "GSM773562", "GSM773563", "GSM773564", "GSM773565", "GSM773566", "GSM773567", "GSM773568", "GSM773569", "GSM773570", "GSM773571", "GSM773572", "GSM773573", "GSM773574", "GSM773575", "GSM773576", "GSM773577", "GSM773578", "GSM773579", "GSM773580", "GSM773581", "GSM773582", "GSM773583", "GSM773584", "GSM773585", "GSM773586", 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