基于抗体依赖性细胞吞噬和单细胞景观相关基因揭示结直肠癌预后和肿瘤微环境

IF 3.3 3区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Leilei Yang, Jiaju Han, Weiwei Ma, Ruili Zhang, Shenkang Zhou
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引用次数: 0

摘要

背景:越来越多的证据强调了抗体依赖性细胞吞噬(ADCP)在结直肠癌(CRC)中的关键作用。然而,如何利用adcp相关基因预测结直肠癌的预后并指导治疗尚不清楚。方法:CRC的基因表达谱和临床数据信息来源于癌症基因组图谱(TCGA)数据库。我们从Gene Expression Omnibus (GEO)数据库中获得验证集GSE29621和CRC单细胞数据集GSE178341,并从文献中获得adcp相关基因集。基于TCGA-CRC队列,采用单变量Cox和LASSO Cox回归分析筛选与预后相关的adcp相关基因。然后通过多变量Cox回归分析建立预后模型。我们进一步绘制了基于临床信息和风险评分的nomogram,并使用Kaplan-Meier (K-M)生存曲线和受试者工作特征(ROC)曲线评估其预后价值。基于单细胞数据分析模型,利用AUCell R软件包对单个细胞进行评分,评估不同细胞簇中基因的表达水平。最后,对高、低adcp相关风险评分(ADCPRS)组进行功能富集、免疫浸润和体细胞突变分析。此外,采用IC50药物敏感性分析和分子对接对治疗结直肠癌患者的小分子药物进行分析。结果:本项目利用TCGA训练集建立了基于7个特征基因的预后模型。K-M生存曲线和ROC曲线表明该模型及nomogram能够准确预测结直肠癌患者的预后。基于scRNA-seq数据分析,检测了7个特征基因在8个细胞簇(单核细胞、CD8 + T细胞、上皮细胞、B细胞、巨噬细胞、HSC、内皮细胞和成纤维细胞)中的表达。我们对单个细胞进行了评分,发现得分较高的细胞主要集中在B细胞和巨噬细胞中。功能富集分析表明,高adcprs组差异表达基因(DEGs)的上调主要富集在药物代谢细胞色素P450、神经活性配体-受体相互作用、钙信号通路等信号通路。免疫浸润分析显示,低adcprs组Th1细胞、iDCs和Th2细胞丰度较高。基因突变分析发现,高adcprs组和低adcprs组的突变率都很高,其中APC和TP53是突变率最高的两个基因。此外,药物敏感性分析和分子对接发现达沙替尼、苯甲醛和替加富可能有助于治疗结直肠癌患者。结论:本项目建立的预后模型可作为风险评估的潜在工具。这7个模式基因可能作为结直肠癌的预后生物标志物,指导结直肠癌患者的治疗决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Revelation of prognosis and tumor microenvironment of colorectal cancer based on genes related to antibody-dependent cellular phagocytosis and single-cell landscape.

Background: Increasing evidence highlights the crucial role of antibody-dependent cellular phagocytosis (ADCP) in colorectal cancer (CRC). However, how to use ADCP-related genes to predict prognosis in CRC and guide treatment remains unelucidated.

Methods: Gene expression profiles and clinical data information on CRC were sourced from the Cancer Genome Atlas (TCGA) database. We obtained the validation set GSE29621 and CRC single-cell dataset GSE178341 from the Gene Expression Omnibus (GEO) database and the ADCP-related gene set from the literature. Based on the TCGA-CRC cohort, univariate Cox and LASSO Cox regression analyses were employed to screen for ADCP-related genes linked with prognosis. Then a prognostic model was set up through multivariate Cox regression analysis. We further graphed a nomogram based on clinical information and risk scoring and evaluated its prognostic value using Kaplan-Meier (K-M) survival curves and receiver operation characteristic (ROC) curves. Based on the single-cell data analysis model, the expression levels of genes in different cell clusters were evaluated by scoring individual cells using the AUCell R package. Finally, functional enrichment, immune infiltration, and somatic mutation analyses were performed on the high- and low-ADCP-related risk score (ADCPRS) groups clustered by the median value of the ADCPRS. In addition, small molecular drugs for the treatment of CRC patients were analyzed using drug sensitivity analysis of IC50 and molecular docking.

Results: This project created a prognostic model based on 7 feature genes using the TCGA training set. The K-M survival curves and ROC curves indicated that the model, as well as the nomogram, was capable of accurately predicting prognosis for CRC patients. Based on scRNA-seq data analysis, the 7 feature genes were examined to be expressed across 8 cell clusters (Monocytes, CD8 + T cells, Epithelial cells, B cells, Macrophages, HSC, Endothelial cells, and Fibroblasts). We scored individual cells and revealed that cells with higher scores were mainly concentrated in B cells and macrophages. Functional enrichment analysis manifested that the upregulated differentially expressed genes (DEGs) in the high-ADCPRS group were mainly enriched in signaling pathways such as the Drug metabolism cytochrome P450, Neuroactive ligand-receptor interaction, and Calcium signaling pathway. Immune infiltration analysis manifested that Th1 cells, iDCs, and Th2 cells had higher abundance in the low-ADCPRS group. Gene mutation analysis uncovered that both high- and low-ADCPRS groups had high mutation rates, with APC and TP53 being the top two genes with the highest mutation rates. Moreover, the drug sensitivity analysis and molecular docking uncovered that Dasatinib, Benzaldehyde, and Tegafur may aid in treating CRC patients.

Conclusion: The prognostic model developed in this project functioned as a potential tool for risk assessment. The 7 model genes may serve as prognostic biomarkers for CRC, which can guide treatment decisions for CRC patients.

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来源期刊
Clinical proteomics
Clinical proteomics BIOCHEMICAL RESEARCH METHODS-
CiteScore
5.80
自引率
2.60%
发文量
37
审稿时长
17 weeks
期刊介绍: Clinical Proteomics encompasses all aspects of translational proteomics. Special emphasis will be placed on the application of proteomic technology to all aspects of clinical research and molecular medicine. The journal is committed to rapid scientific review and timely publication of submitted manuscripts.
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