Development of a Novel Risk Signature for Predicting the Prognosis and Immunotherapeutic Response of Prostate Cancer by Integrating Ferroptosis and Immune-Related Genes.

IF 2.4 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular Biotechnology Pub Date : 2024-10-28 DOI:10.1007/s12033-024-01293-5
Yang Song, Qiang Zhang
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Abstract

Ferroptosis and immune response correlation studies have not been reported in prostate cancer (PCa), and the main goal of this paper is to identify biomarkers that can be used for early diagnosis of prostate cancer. Data on PCa were retrieved from the TCGA and MSKCC2010 databases. Thereafter, the differentially expressed ferroptosis-related genes (DE-FRGs: ACSF2) and immune-related genes (DE-IRGs: ANGPT1, NPPC, and PTGDS) were identified using the "limma" package. Additionally, we used univariate and multivariate Cox regression analyses to obtain biochemical relapse (BCR)-free survival-related genes and construct a risk signature. Patients with high-risk scores were characterized by poor BCR-free survival, relatively low immune cell abundance, and comparably weak expression of immune checkpoint molecules. Moreover, gene set variation analysis (GSVA) was performed to explore the biological pathways related to the risk signature. Single sample gene set enrichment analysis (ssGESA) was applied to evaluate the status of immune cells in patients with PCa, which demonstrated that the risk score was intimately affiliated with immune response and cancer pathways. Ultimately, the connection between the risk score and response of PCa patients to immunotherapy was appraised using the TIDE algorithm. The TIDE algorithm implied that the high-risk score PCa population might benefit more from immunotherapy regimens. Finally, qRT-PCR were used to evaluate the expression of DE-FRGs and DE-IRGs in PCa cell and normal prostate epithelial cells. The result of qRT-PCR showed that the mRNA expression levels of ACSF2, ANGPT1, NPPC, and PTGDS in normal prostate epithelial cell were higher than that in PCa cells. Therefore, a risk score model was generated based on one DE-FRG and three DE-IRGs, which could predict the BCR-free survival and response of immunotherapy for patients with PCa.

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通过整合铁蛋白沉积和免疫相关基因,开发用于预测前列腺癌预后和免疫治疗反应的新型风险特征。
前列腺癌(PCa)中的铁蛋白沉积和免疫反应相关研究尚未见报道,本文的主要目的是找出可用于前列腺癌早期诊断的生物标志物。PCa 的数据来自 TCGA 和 MSKCC2010 数据库。之后,我们使用 "limma "软件包识别了差异表达的铁突变相关基因(DE-FRGs:ACSF2)和免疫相关基因(DE-IRGs:ANGPT1、NPPC 和 PTGDS)。此外,我们还使用单变量和多变量 Cox 回归分析来获得无生化复发(BCR)生存相关基因并构建风险特征。高风险评分患者的特点是无 BCR 生存率低、免疫细胞丰度相对较低、免疫检查点分子表达相对较弱。此外,还进行了基因组变异分析(GSVA),以探索与风险特征相关的生物学通路。应用单样本基因组富集分析(ssGESA)评估了PCa患者的免疫细胞状况,结果表明风险评分与免疫反应和癌症通路密切相关。最后,利用 TIDE 算法评估了风险评分与 PCa 患者对免疫疗法反应之间的联系。TIDE 算法表明,高风险评分的 PCa 患者可能从免疫疗法中获益更多。最后,研究人员利用 qRT-PCR 技术评估了 PCa 细胞和正常前列腺上皮细胞中 DE-FRGs 和 DE-IRGs 的表达情况。qRT-PCR 结果显示,正常前列腺上皮细胞中 ACSF2、ANGPT1、NPPC 和 PTGDS 的 mRNA 表达水平高于 PCa 细胞。因此,基于一个DE-FRG和三个DE-IRG建立的风险评分模型可以预测PCa患者的无BCR生存率和免疫治疗反应。
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来源期刊
Molecular Biotechnology
Molecular Biotechnology 医学-生化与分子生物学
CiteScore
4.10
自引率
3.80%
发文量
165
审稿时长
6 months
期刊介绍: Molecular Biotechnology publishes original research papers on the application of molecular biology to both basic and applied research in the field of biotechnology. Particular areas of interest include the following: stability and expression of cloned gene products, cell transformation, gene cloning systems and the production of recombinant proteins, protein purification and analysis, transgenic species, developmental biology, mutation analysis, the applications of DNA fingerprinting, RNA interference, and PCR technology, microarray technology, proteomics, mass spectrometry, bioinformatics, plant molecular biology, microbial genetics, gene probes and the diagnosis of disease, pharmaceutical and health care products, therapeutic agents, vaccines, gene targeting, gene therapy, stem cell technology and tissue engineering, antisense technology, protein engineering and enzyme technology, monoclonal antibodies, glycobiology and glycomics, and agricultural biotechnology.
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