An Oxidative Stress-Related Prognostic Signature Predicts Treatment Response and Outcomes for Hepatocellular Carcinoma After Transarterial Chemoembolization.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39156675", "kind": "science-record", "revision": 2, "content_hash": "sha256:c887bdc499dc11dee609a197c349fa0225f99fedd008c8ca83d5891bde177e36", "created_at": "2026-11-01T20:29:16Z", "updated_at": "2026-11-01T21:33:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An Oxidative Stress-Related Prognostic Signature Predicts Treatment Response and Outcomes for Hepatocellular Carcinoma After Transarterial Chemoembolization.", "doi": "10.2147/JHC.S465592", "pmid": "39156675", "authors": ["Hui Ma", "Ting Yu", "Zhong-Chen Li", "Lan Zhang", "Rong-Xin Chen", "Zheng-Gang Ren"], "journal": "Journal of hepatocellular carcinoma", "year": 2024, "abstract": "Purpose: Oxidative stress plays a critical role in promoting tumor resistance to hypoxia and chemotherapeutic drugs. However, the prognostic role of oxidative stress-related genes (OSRGs) in hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) has not been fully explored.\nMethods: We used transcriptome data from the GSE104580 cohort containing patients marked as responders or nonresponders to TACE therapy to identify differentially expressed OSRGs associated with TACE response (TR-OSRGs). We created a TR-OSRG prognostic signature based on TR-OSRGs using least absolute shrinkage and selection operator Cox and stepwise Cox regression analyses in a training cohort of patients with HCC (TCGA-LIHC). We verified this prognostic signature in two external cohorts of patients who received TACE for HCC (GSE14520-TACE and ZS-TACE-37). Finally, we constructed a prognostic nomogram model for predicting survival probability of patients with HCC based on Cox regression analysis.\nResults: The TR-OSRG prognostic signature was created and shown to be a robust independent prognostic factor for treatment response and outcomes for HCC after TACE therapy. Risk scores based on this signature correlated with tumor stage and grade. Tumor samples from patients with higher risk scores exhibited more infiltration of immune cells and significantly increased expression of immune checkpoint genes. We also developed a nomogram for patients with HCC based on the TR-OSRG prognostic signature and clinical parameters; this nomogram was a useful quantitative analysis tool for predicting patient survival.\nConclusion: The TR-OSRGs signature exhibited good performance in predicting treatment response and outcomes in patients with HCC treated with TACE.", "claims": [{"id": "claim-method", "statement": "Least absolute shrinkage and selection operator (LASSO) Cox and stepwise Cox regression analyses were performed to evaluate key prognosis-related qualities and to establish prognostic characteristics using the R package glmnet (version 1.2, https://cran.r-project.org/web/packages/glmnet/index.html) and survminer (version 0.4.9, https://cran.rstudio.com/web/packages/survminer/index.html).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39156675", "locator": "sections[8] \"Construction and Validation of the TR-OSRG Prognostic Signature\" paragraph 2", "quote": "Least absolute shrinkage and selection operator (LASSO) Cox and stepwise Cox regression analyses were performed to evaluate key prognosis-related qualities and to establish prognostic characteristics using the R package glmnet (version 1.2, https://cran.r-project.org/web/packages/glmnet/index.html) and survminer (version 0.4.9, https://cran.rstudio.com/web/packages/survminer/index.html)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study, and the names of the repositories can be found in the article. Additional data supporting the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39156675", "locator": "sections[22] \"Data Sharing Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study, and the names of the repositories can be found in the article. Additional data supporting the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Kaplan–Meier survival analysis revealed that patients with a high RS had a worse prognosis, and ROC analysis showed that the TR-OSRG signature had good prognostic performance, with area under the curve (AUC) values of 0.744, 0.684, and 0.605 at 1-, 3-, and 5-year, respectively (Figure 2H).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39156675", "locator": "sections[14] \"Construction of the TR-OSRG Prognostic Signature\" paragraph 2", "quote": "Kaplan–Meier survival analysis revealed that patients with a high RS had a worse prognosis, and ROC analysis showed that the TR-OSRG signature had good prognostic performance, with area under the curve (AUC) values of 0.744, 0.684, and 0.605 at 1-, 3-, and 5-year, respectively (Figure 2H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Kaplan–Meier survival analysis revealed that patients with a high RS had a worse prognosis, and ROC analysis showed that the TR-OSRG signature had good prognostic performance, with area under the curve (AUC) values of 0.744, 0.684, and 0.605 at 1-, 3-, and 5-year, respectively (Figure 2H).", "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:39156675"], "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:GSE104580"], "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:GSE104580"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39156675", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JHC.S465592", "sha256": "sha256:cce19571cd83ac6c137215821081fe06728acea6ffdd4114efd205ac81d3216c", "bytes": 38894, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/JHC.S465592", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11330244", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE104580", "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=GSE104580", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE104580", "version": null, "note": "Publicly available datasets were analyzed in this study, and the names of the repositories can be found in the article. 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Methods: We used transcriptome data from the GSE104580 cohort containing pa"], "order_basis": "paper_text_order:433", "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": ["Least absolute shrinkage and selection operator (LASSO) Cox and stepwise Cox regression analyses were performed to evaluate key prognosis-related qualities and to establish prognostic characteristics using the R package glmnet (version 1.2, https://cran.r-project.org/web/packages/glmnet/index.html) and survminer (version 0.4.9, https://cran.rstudio.com/web/packages/survminer/index.html)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}