Identifying the Role of Oxidative Stress-Related Genes as Prognostic Biomarkers and Predicting the Response of Immunotherapy and Chemotherapy in Ovarian Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36561981", "kind": "science-record", "revision": 3, "content_hash": "sha256:e64dcb57e2b053622e6ee55678e6ff87cb0a9a4096148124053b19e1ee39b948", "created_at": "2026-11-01T19:01:06Z", "updated_at": "2026-11-01T22:08:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying the Role of Oxidative Stress-Related Genes as Prognostic Biomarkers and Predicting the Response of Immunotherapy and Chemotherapy in Ovarian Cancer.", "doi": "10.1155/2022/6575534", "pmid": "36561981", "authors": ["Qingyang Liu", "Xiaocheng Yang", "Yuhan Yin", "Hao Zhang", "Fanxing Yin", "Panpan Guo", "Xiaoxu Zhang", "Chenxi Sun", "Shining Li", "Yanshuo Han", "Zhuo Yang"], "journal": "Oxidative medicine and cellular longevity", "year": 2022, "abstract": "Background: Ovarian cancer (OC) is one of the most frequently seen and fatal gynecological malignancies, and oxidative stress (OS) plays a critical role in the development and chemoresistance of OC.\nMaterials and Methods: OS-related genes (OSRGs) were obtained from the Molecular Signatures Database. Besides, gene expression profiles and clinical information from The Cancer Genome Atlas (TCGA) were selected to identify the prognostic OSRGs. Moreover, univariate Cox regression, LASSO, and multivariate Cox regression analyses were conducted sequentially to establish a prognostic signature, which was later validated in three independent Gene Expression Omnibus (GEO) datasets. Next, gene set enrichment analysis (GSEA) and tumor mutation burden (TMB) analysis were performed. Afterwards, immune checkpoint genes (ICGs) and the tumor immune dysfunction and exclusion (TIDE) algorithm, together with IMvigor210 and GSE78220 cohorts, were applied to comprehensively explore the role of OSRG signature in immunotherapy. Further, the CellMiner and Genomics of Drug Sensitivity in Cancer (GDSC) databases were also applied in investigating the significance of OSRG signature in chemotherapy.\nResults: Altogether, 34 prognostic OSRGs were identified, among which 14 were chosen to establish the most valuable prognostic signature. The Kaplan-Meier (KM) analysis suggested that patients with lower OS-related risk score had better prognosis. The area under the curve (AUC) values were 0.71, 0.76, and 0.85 in 3, 5, and 7 years separately, and the stability of this prognostic signature was confirmed in three GEO datasets. As revealed by GSEA and TMB analysis results, OC patients in low-risk group might have better immunotherapeutic response, which was consistent with ICG expression and TIDE analyses. Moreover, both IMvigor210 and GSE78220 cohorts demonstrated that patients with lower OS-related risk score were more likely to benefit from anti-PD-1/L1 immunotherapy. In addition, the association between prognostic signature and drug sensitivity was explored.\nConclusion: According to our results in this work, OSRG signature can act as a powerful prognostic predictor for OC, which contributes to generating more individualized therapeutic strategies for OC patients.", "claims": [{"id": "claim-method", "statement": "OSRGs with potential prognostic value were identified by univariate Cox regression analysis, with P < 0.05 being the threshold to select prognostic OSRGs. The Least Absolute Shrinkage and Selection Operator (LASSO), a kind of regression algorithm preserving valuable variables and avoiding overfitting [43], was utilized to shrink the number of OSRGs and to select the most valuable prognostic signature from all those identified OSRGs. Thereafter, the multivariate Cox regression algorithm was utilized to calculate individual-level risk scores.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36561981", "locator": "sections[7] \"2.5. Establishment and Verification of the Prognostic Signature\" paragraph 2", "quote": "OSRGs with potential prognostic value were identified by univariate Cox regression analysis, with P < 0.05 being the threshold to select prognostic OSRGs. The Least Absolute Shrinkage and Selection Operator (LASSO), a kind of regression algorithm preserving valuable variables and avoiding overfitting [43], was utilized to shrink the number of OSRGs and to select the most valuable prognostic signature from all those identified OSRGs. Thereafter, the multivariate Cox regression algorithm was utilized to calculate individual-level risk scores."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data from TCGA and GEO datasets used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36561981", "locator": "sections[27] \"Data Availability\" paragraph 2", "quote": "The data from TCGA and GEO datasets used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The OSRG-based prognostic signature exhibited good accuracy, with area under the curve values of 0.71, 0.76, and 0.85 at 3, 5, and 7 years, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36561981", "locator": "sections[19] \"3.4. 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