Identification of Disulfidptosis-Related LncRNA Subtypes, Establishment of a Prognostic Signature, and Characterization of Immune Infiltration in Ovarian Cancer.

IF 1.6 4区医学 Q4 BIOCHEMICAL RESEARCH METHODS

Combinatorial chemistry & high throughput screening Pub Date : 2024-10-03 DOI:10.2174/0113862073326170240923061119

Jie Lin, Linying Liu, Xintong Cai, Anyang Li, Yixin Fu, Huaqing Huang, Yang Sun

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引用次数: 0

Abstract

Background: 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.

Method: 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.

Results: 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.

Conclusion: 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.

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鉴定与二硫化硫相关的 LncRNA 亚型、建立预后特征以及描述卵巢癌的免疫渗透。

背景：卵巢癌（OC）是一种致命的恶性肿瘤，预后极差。二硫化硫是一种新发现的由二硫化物应激引起的细胞死亡形式。以二硫化硫为靶点是治疗癌症的一种新的代谢治疗策略。我们旨在建立一个与二硫化硫相关的lncRNA特征来预测预后，并探索其在OC患者中的治疗价值：方法：我们利用TCGA和GTEx数据库的数据以及二硫化基因集建立了用于预测OC患者预后的二硫化相关lncRNA特征。然后，我们对模型进行了内部和外部（PCR）验证。我们还建立了一个提名图，以提高模型的预测能力。之后，我们采用了 GSEA 来探索模型的潜在功能。ESTIMATE、CIBERSORT、TIMER 和 ssGSEA 被用来估计免疫景观。最后，分析了OC患者对某些药物的敏感性：我们建立了一个基于7个drlncRNA的预后模型，包括AL157871.2、HCP5、AC027348.1、AL109615.3、AL928654.1、LINC02585和AC011445.1。通过内部验证，我们的模型表现良好。PCR 数据也证实了 lncRNA 水平的相同趋势。此外，整合了年龄、分级、分期和风险评分的提名图可以准确预测 OC 患者的生存结果。随后，GSEA揭示了我们的模型基因富含刺猬信号通路，这是OC肿瘤发生过程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PD-1（P < 0.01）、PD-L1（P < 0.001）和CTLA4（P < 0.01），这可能有助于筛选出对免疫疗法敏感的OC患者。AZD-2281、GDC-0449、伊马替尼和尼洛替尼等小分子药物可能会使不同风险评分的OC患者受益：由AL157871.2、HCP5、AC027348.1、AL109615.3、AL928654.1、LINC02585和AC011445.1组成的二硫化相关lncRNA特征可作为OC患者的预后生物标志物和治疗反应指南。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Combinatorial chemistry & high throughput screening 化学-生化研究方法

CiteScore

3.10

自引率

5.60%

发文量

327

审稿时长

7.5 months

期刊介绍： Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.