ArticleCombinatorial chemistry & high throughput screening2025
Identification of Disulfidptosis-Related LncRNA Subtypes, Establishment of a Prognostic Signature, and Characterization of Immune Infiltration in Ovarian Cancer.
Article in Combinatorial chemistry & high throughput screening, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Advancing the frontiers of ovarian cancer therapy: a comprehensive synthesis of emerging cell death paradigms.Oncology reviews · 2026Review
- Leveraging machine learning models to evaluate immune infiltration in the ovarian cancer microenvironment: a single-cell analysis approach.Discover oncology · 2025Article
- Research progress and potential therapeutic targets of a novel disulfide stress-driven cell death-disulfidptosis in gynecological tumors and other gynecological disorders.American journal of cancer research · 2025Review
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7 authors.
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Abstract
backgroundOvarian Cancer (OC) is a lethal malignant tumor with a poor prognosis. Disulfidptosis is a newly identified form of cell death caused by disulfide stress. Targeting disulfidptosis is a new metabolic therapeutic strategy in cancer treatment. We aimed to establish a disulfidptosis- related lncRNA signature for prognosis prediction and explore its treatment values in OC patients.
methodData from the TCGA and GTEx databases and a disulfidptosis gene set were used to establish a disulfidptosis-related lncRNA signature for prognosis prediction in OC patients. Then, we internally and externally (PCR) validated our model. We also built a nomogram to improve our model's predictive power. Afterward, GSEA was employed to explore our model's potential functions. The ESTIMATE, CIBERSORT, TIMER, and ssGSEA were applied to estimate the immune landscape. Finally, the drug sensitivity of certain drugs for OC patients was analyzed.
resultsWe built a prognosis model based on seven drlncRNAs, including AL157871.2, HCP5, AC027348.1, AL109615.3, AL928654.1, LINC02585, and AC011445.1. Our model performed well by internal validation. PCR data also confirmed the same trend in the lncRNA levels. Furthermore, the nomogram-integrated age, grade, stage, and risk score could accurately predict the survival outcomes of OC patients. Subsequently, GSEA unveiled that our model genes enriched the Hedgehog signaling pathway, a key regulator in OC tumorigenesis. Our predictive signature was associated with immune checkpoints, such as PD-1(P < 0.01), PD-L1(P < 0.001), and CTLA4 (P < 0.01), which might help screen out OC patients who are sensitive to immunotherapy. Small molecule drugs, such as AZD-2281, GDC-0449, imatinib, and nilotinib, might benefit OC patients with different risk scores.
conclusionOur disulfidptosis-related lncRNA signature comprised of AL157871.2, HCP5, AC027348.1, AL109615.3, AL928654.1, LINC02585, and AC011445.1 could serve as a prognostic biomarker and guidance to therapy response for OC patients.
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