基于乳腺癌单细胞联合大体 RNA 分析的新型预后特征的开发。

IF 3.2 4区 医学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Ying Xiao, Ge Hu, Ning Xie, Liang Yin, Yaqiang Pan, Cong Liu, Shihan Lou, Cunzhi Zhu
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引用次数: 0

摘要

背景:乳腺癌(BC)是一种恶性肿瘤,是导致全球妇女死亡和残疾的重要原因。最新研究表明,拷贝数变异在肿瘤发生发展中起着至关重要的作用。在这项研究中,我们采用了单细胞变异非整倍体分析(SCEVAN)算法来区分恶性细胞和非恶性细胞,旨在找出与预测患者生存率预后相关的基因特征:我们分析了基因表达总库(GEO)和癌症基因组图谱(TCGA)数据库中的基因表达谱和相关临床数据。利用 SCEVAN 算法,我们区分了恶性和非恶性细胞,并研究了肿瘤微环境(TME)中的细胞相互作用。我们根据这些细胞类型之间的差异表达基因(DEGs)对 TCGA 样本进行了分类。随后进行了京都基因和基因组百科全书通路分析。此外,我们还利用最小绝对缩减和选择算子惩罚性 Cox 回归分析为 DEGs 建立了多基因模型。为了评估这些特征的预后准确性,我们从训练数据集和验证数据集生成了 Kaplan-Meier 和接收者操作特征曲线。我们还监测了预后基因在恶性细胞假时空的表达变化。根据中位风险评分将患者分为高风险组和低风险组,以比较他们的 TME 并确定潜在的治疗药物。最后,利用聚合酶链反应验证了七个关键基因:结果:SCEVAN 算法在 GSE180286 中识别出了不同的恶性和非恶性细胞。Cellchat分析显示,细胞间的交流明显增加,尤其是成纤维细胞、内皮细胞和恶性细胞之间的交流。DEGs主要涉及免疫相关通路。根据这些基因,TCGA样本被分为A组和B组。A组富含免疫通路,预后较差,而B组主要涉及昼夜节律通路,预后较好。我们构建了14个基因的预后特征,并在1:1内部TCGA队列和外部GEO数据集(GSE42568和GSE146558)中进行了验证。Kaplan-Meier分析证实了预后特征的准确性(p 结论:我们的研究结果表明,14个基因的预后特征是准确的:我们的研究结果表明,14 个基因的预后特征可作为预测 BC 患者预后的新型生物标志物。此外,不同风险组的免疫细胞和免疫途径表明,免疫疗法可能是 BC 患者治疗策略的重要组成部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer

Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer

Background

Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival.

Methods

We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan–Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes.

Results

The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan–Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs.

Conclusion

Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.

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来源期刊
Journal of Gene Medicine
Journal of Gene Medicine 医学-生物工程与应用微生物
CiteScore
6.40
自引率
0.00%
发文量
80
审稿时长
6-12 weeks
期刊介绍: The aims and scope of The Journal of Gene Medicine include cutting-edge science of gene transfer and its applications in gene and cell therapy, genome editing with precision nucleases, epigenetic modifications of host genome by small molecules, siRNA, microRNA and other noncoding RNAs as therapeutic gene-modulating agents or targets, biomarkers for precision medicine, and gene-based prognostic/diagnostic studies. Key areas of interest are the design of novel synthetic and viral vectors, novel therapeutic nucleic acids such as mRNA, modified microRNAs and siRNAs, antagomirs, aptamers, antisense and exon-skipping agents, refined genome editing tools using nucleic acid /protein combinations, physically or biologically targeted delivery and gene modulation, ex vivo or in vivo pharmacological studies including animal models, and human clinical trials. Papers presenting research into the mechanisms underlying transfer and action of gene medicines, the application of the new technologies for stem cell modification or nucleic acid based vaccines, the identification of new genetic or epigenetic variations as biomarkers to direct precision medicine, and the preclinical/clinical development of gene/expression signatures indicative of diagnosis or predictive of prognosis are also encouraged.
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