Lin Jie, Liu Linying, Cai Xintong, Li Anyang, Fu Yixin, Huang Huaqing, Sun Yang
Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China.
Department of Pain Management, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China.
Comb Chem High Throughput Screen. 2024 Oct 3. doi: 10.2174/0113862073326170240923061119.
Ovarian 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.
Data 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.
We 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.
Our 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.
卵巢癌（OC）是一种预后较差的致命性恶性肿瘤。二硫化物诱导的细胞焦亡是一种由二硫键应激引起的新发现的细胞死亡形式。靶向二硫化物诱导的细胞焦亡是癌症治疗中的一种新的代谢治疗策略。我们旨在建立一种与二硫化物诱导的细胞焦亡相关的lncRNA特征用于预后预测，并探索其在OC患者中的治疗价值。
使用来自TCGA和GTEx数据库的数据以及一个二硫化物诱导的细胞焦亡基因集来建立一种与二硫化物诱导的细胞焦亡相关的lncRNA特征，用于预测OC患者的预后。然后，我们在内部和外部（PCR）验证了我们的模型。我们还构建了一个列线图以提高我们模型的预测能力。之后，采用基因集富集分析（GSEA）来探索我们模型的潜在功能。应用ESTIMATE、CIBERSORT、TIMER和单样本基因集富集分析（ssGSEA）来评估免疫格局。最后，分析了某些药物对OC患者的药物敏感性。
我们基于7个二硫化物诱导的细胞焦亡相关lncRNA构建了一个预后模型，包括AL157871.2、HCP5、AC027348.1、AL109615.3、AL928654.1、LINC02585和AC011445.1。我们的模型通过内部验证表现良好。PCR数据也证实了lncRNA水平的相同趋势。此外，整合了年龄、分级、分期和风险评分的列线图可以准确预测OC患者的生存结果。随后，GSEA揭示我们模型中的基因富集了刺猬信号通路，这是OC肿瘤发生中的一个关键调节因子。我们的预测特征与免疫检查点相关，如程序性死亡受体1（PD - 1，P < 0.01）、程序性死亡配体1（PD - L1，P < 0.001）和细胞毒性T淋巴细胞相关蛋白4（CTLA4，P < 0.01），这可能有助于筛选出对免疫治疗敏感的OC患者。小分子药物，如AZD - 2281、GDC - 0449、伊马替尼和尼罗替尼，可能使具有不同风险评分的OC患者受益。
我们的由AL157871.2、HCP5、AC027348.1、AL109615.3、AL928654.1、LINC02585和AC组织。我们的模型通过内部验证表现良好。PCR数据也证实了lncRNA水平的相同趋势。此外，整合了年龄、分级、分期和风险评分的列线图可以准确预测OC患者的生存结果。随后，GSEA揭示我们模型中的基因富集了刺猬信号通路，这是OC肿瘤发生中的一个关键调节因子。我们的预测特征与免疫检查点相关，如程序性死亡受体1（PD - 1，P < 0.01）、程序性死亡配体1（PD - L1，P < 0.001）和细胞毒性T淋巴细胞相关蛋白4（CTLA4，P < 0.01），这可能有助于筛选出对免疫治疗敏感的OC患者。小分子药物，如AZD - 2281、GDC - 0449、伊马替尼和尼罗替尼，可能使具有不同风险评分的OC患者受益。
我们的由AL157871.2、HCP5、AC027348.1、AL109615.3、AL928654.1、LINC02585和AC011445.1组成的与二硫化物诱导的细胞焦亡相关的lncRNA特征可作为OC患者的预后生物标志物和治疗反应指导。
