中性粒细胞相关特征描绘出浸润性乳腺癌的免疫格局并预测其预后

IF 2.1 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Biochemical Genetics Pub Date : 2024-10-17 DOI:10.1007/s10528-024-10940-0
Wenge Dong, Jiejing Li, Zhigang Zhuang
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引用次数: 0

摘要

作为一种主要的流行性恶性肿瘤,乳腺癌仍然是一个重要的世界性健康问题。最新研究表明,中性粒细胞在乳腺癌的发生发展中起着至关重要的作用。然而,嗜中性粒细胞相关基因(NRGs)或嗜中性粒细胞相关特征免疫图谱在浸润性乳腺癌(IBC)中的预后意义尚不清楚。为了找到创新疗法的替代方案,本文对中性粒细胞相关特征在 IBC 中的重要性进行了评估。简而言之,我们创建了一个基于中性粒细胞相关核心预后基因和癌症基因组图谱数据(TCGA)的预测模型。该模型可评估 IBC 患者的预后。基因表达总库(GEO)中的 IBC 数据证实了该模型预后的准确性。与 NRGs 得分低的组别相比,NRGs 得分高的组别患者的总生存期(OS)更短。此外,NRGs评分低的患者对长春瑞滨、环磷酰胺、表柔比星、吉西他滨、紫杉醇、5-氟尿嘧啶、多西他赛和顺铂的敏感性更高。NRGs评分低的患者对CTLA-4和PD-1治疗的反应更好。此外,NRGs得分低的患者免疫微环境成分更丰富。此外,qRT-PCR 结果证实,与正常样本相比,LEF1 在肿瘤样本中的表达水平更高,而 NRG1 和 STX11 在肿瘤样本中的表达水平低于正常样本。这些结果表明,NRGs可作为生物标记物来预测IBC患者的预后,从而为IBC的定制疗法铺平道路。
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Neutrophil-related Signature Characterizes Immune Landscape and Predicts Prognosis of Invasive Breast Cancer.

As a leading prevalent malignancy, breast cancer remains a significant worldwide health issue. Recent research indicates that neutrophils play a crucial role in breast cancer development. The prognostic significance of neutrophil-related genes (NRGs) or the immune landscape of the neutrophil-related signature in invasive breast cancer (IBC) is, nevertheless, unknown. To uncover innovative therapy alternatives, the significance of the neutrophil-related signatures in IBC was evaluated here. Briefly, a prediction model based on neutrophil-related core prognostic genes and The Cancer Genome Atlas data was created (TCGA). The model may assess IBC patients' prognosis. The IBC data from the Gene Expression Omnibus (GEO) confirmed the prognostic accuracy of the model. The overall survival (OS) of patients was worse in the group with a high NRGs score compared to the group with a low NRGs score. In addition, patients with low NRGs scores were considerably more sensitive to vinorelbine, cyclophosphamide, epirubicin, gemcitabine, paclitaxel, 5-fluorouracil, docetaxel, and cisplatin. Patients with low NRGs scores responded better to CTLA-4 and PD-1 treatments. Additionally, the immune microenvironment components were more abundant in patients with low NRGs scores. Moreover, qRT-PCR results confirmed that LEF1 had a higher expression level in tumor samples compared to normal samples, whereas NRG1 and STX11 exhibited lower expression levels in tumor samples than in normal samples. These results suggest that NRGs might be utilized as biomarkers to predict the prognosis of individuals with IBC, thereby paving the way for the creation of customized therapies for IBC.

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来源期刊
Biochemical Genetics
Biochemical Genetics 生物-生化与分子生物学
CiteScore
3.90
自引率
0.00%
发文量
133
审稿时长
4.8 months
期刊介绍: Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses. Although studies focusing on particular regions and target organisms are welcome, it is not the journal’s goal to publish essentially descriptive studies that provide results with narrow applicability, or are based on very small samples or pseudoreplication. Rather, Biochemical Genetics welcomes review articles that go beyond summarizing previous publications and create added value through the systematic analysis and critique of the current state of knowledge or by conducting meta-analyses. Methodological articles are also within the scope of Biological Genetics, particularly when new laboratory techniques or computational approaches are fully described and thoroughly compared with the existing benchmark methods. Biochemical Genetics welcomes articles on the following topics: Genomics; Proteomics; Population genetics; Phylogenetics; Metagenomics; Microbial genetics; Genetics and evolution of wild and cultivated plants; Animal genetics and evolution; Human genetics and evolution; Genetic disorders; Genetic markers of diseases; Gene technology and therapy; Experimental and analytical methods; Statistical and computational methods.
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