An oxidative stress-related signature for predicting the prognosis of liver cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36685933", "kind": "science-record", "revision": 3, "content_hash": "sha256:28d544214d72324eadecee89a90d4baf6c179f49420b2faffc778ebe2d442a64", "created_at": "2026-11-01T19:01:14Z", "updated_at": "2026-11-01T22:08:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An oxidative stress-related signature for predicting the prognosis of liver cancer.", "doi": "10.3389/fgene.2022.975211", "pmid": "36685933", "authors": ["Luling Wang", "Xing Liu"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Introduction: This study aimed to screen for oxidative stress-related genes (OSRGs) and build an oxidative stress-related signature to predict the prognosis of liver cancer. Methods: OSRGs with a protein domain correlation score ≥ 6 were downloaded from the GeneCards database and intersected with The Cancer Genome Atlas (TCGA) data for subsequent analyses. Differential immune cells (DICs) and immune and stromal scores between the normal and tumor samples were determined, followed by unsupervised hierarchical cluster analysis. Immune-related OSRGs were identified using weighted gene co-expression network analysis. An OSRG-related risk signature was then built, and the GSE14520 dataset was used for validation. A nomogram evaluation model was used to predict prognosis. Results: Nine DICs were determined between the normal and tumor groups, and three subtypes were obtained: clusters 1, 2, and 3. Cluster 1 had the best prognosis among the clusters. One hundred thirty-eight immune-related OSRGs were identified, and seven prognosis-related OSRGs were used to build the OSRG score prognostic model. Patients in the high OSRG score group had a poorer prognosis than those in the low OSRG score group. Six immune cell infiltration and enrichment scores of the 16 immune gene sets showed significant differences between the high and low OSRG score groups. Moreover, a nomogram was constructed based on the prognostic signature and clinicopathological features and had a robust predictive performance and high accuracy. Conclusion: The OSRG-related risk signature and the prognostic nomogram accurately predicted patient survival.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was performed on immune-related OSRGs using the ‘survival’ package to screen prognosis-related OSRGs with a cutoff value of p < 0.05. The key OSRGs were obtained using least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation and a p-value <0.05. Stepwise Cox regression analysis was used to build the OSRG-related risk model using the ‘survminer’ package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36685933", "locator": "sections[8] \"2.6 Construction and validation of a risk model\" paragraph 2", "quote": "Univariate Cox regression analysis was performed on immune-related OSRGs using the ‘survival’ package to screen prognosis-related OSRGs with a cutoff value of p < 0.05. The key OSRGs were obtained using least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation and a p-value <0.05. Stepwise Cox regression analysis was used to build the OSRG-related risk model using the ‘survminer’ package."}], "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:36685933", "locator": "sections[22] \"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": "The OSRG-related risk signature and the prognostic nomogram accurately predicted patient survival.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36685933", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The OSRG-related risk signature and the prognostic nomogram accurately predicted patient survival."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The OSRG-related risk signature and the prognostic nomogram accurately predicted patient survival.", "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:36685933"], "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:GSE14520"], "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:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36685933", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.975211", "sha256": 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