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单细胞RNA测序和全基因组孟德尔随机化以及大量机器学习方法在胃癌中鉴定出一种新的B细胞特征。

Single-cell RNA-sequencing and genome-wide Mendelian randomisation along with abundant machine learning methods identify a novel B cells signature in gastric cancer.

作者信息

Ma Qi, Gao Jie, Hui Yuan, Zhang Zhi-Ming, Qiao Yu-Jie, Yang Bin-Feng, Gong Ting, Zhao Duo-Ming, Huang Bang-Rong

机构信息

Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, 730050, China.

Gansu University of Traditional Chinese Medicine, Lanzhou, China.

出版信息

Discov Oncol. 2025 Jan 6;16(1):11. doi: 10.1007/s12672-025-01759-1.

Abstract

BACKGROUND

Gastric cancer (GC) has a poor prognosis, considerable cellular heterogeneity, and ranks fifth among malignant tumours. Understanding the tumour microenvironment (TME) and intra-tumor heterogeneity (ITH) may lead to the development of novel GC treatments.

METHODS

The single-cell RNA sequencing (scRNA-seq) dataset was obtained from the Gene Expression Omnibus (GEO) database, where diverse immune cells were isolated and re-annotated based on cell markers established in the original study to ascertain their individual characteristics. We conducted a weighted gene co-expression network analysis (WGCNA) to identify genes with a significant correlation to GC. Utilising bulk RNA sequencing data, we employed machine learning integration methods to train specific biomarkers for the development of novel diagnostic combinations. A two-sample Mendelian randomisation study was performed to investigate the causal effect of biomarkers on gastric cancer (GC). Ultimately, we utilised the DSigDB database to acquire associations between signature genes and pharmaceuticals.

RESULTS

The 18 genes that made up the signature were as follows: ZFAND2A, PBX4, RAMP2, NNMT, RNASE1, CD93, CDH5, NFKBIE, VWF, DAB2, FAAH2, VAT1, MRAS, TSPAN4, EPAS1, AFAP1L1, DNM3. Patients were categorised into high-risk and low-risk groups according to their risk scores. Individuals in the high-risk cohort exhibited a dismal outlook. The Mendelian randomisation study demonstrated that individuals with a genetic predisposition for elevated NFKBIE levels exhibited a heightened likelihood of acquiring GC. Molecular docking indicates that gemcitabine and chloropyramine may serve as effective therapeutics against NFKBIE.

CONCLUSIONS

We developed and validated a signature utilising scRNA-seq and bulk sequencing data from gastric cancer patients. NFKBIE may function as a novel biomarker and therapeutic target for GC.

摘要

背景

胃癌(GC)预后较差,具有显著的细胞异质性,在恶性肿瘤中排名第五。了解肿瘤微环境(TME)和肿瘤内异质性(ITH)可能会推动新型胃癌治疗方法的发展。

方法

单细胞RNA测序(scRNA-seq)数据集取自基因表达综合数据库(GEO),基于原始研究中确定的细胞标志物分离并重新注释了多种免疫细胞,以确定它们各自的特征。我们进行了加权基因共表达网络分析(WGCNA),以识别与胃癌显著相关的基因。利用批量RNA测序数据,我们采用机器学习整合方法来训练特定的生物标志物,以开发新型诊断组合。进行了一项两样本孟德尔随机化研究,以调查生物标志物对胃癌(GC)的因果效应。最终,我们利用DSigDB数据库获取特征基因与药物之间的关联。

结果

构成特征的18个基因如下:ZFAND2A、PBX4、RAMP2、NNMT、RNASE1、CD93、CDH5、NFKBIE、VWF、DAB2、FAAH2、VAT1、MRAS、TSPAN4、EPAS1、AFAP1L1、DNM3。根据风险评分将患者分为高风险组和低风险组。高风险队列中的个体预后不佳。孟德尔随机化研究表明,具有NFKBIE水平升高遗传倾向的个体患胃癌的可能性更高。分子对接表明吉西他滨和氯吡胺可能是针对NFKBIE的有效治疗药物。

结论

我们利用胃癌患者的scRNA-seq和批量测序数据开发并验证了一种特征。NFKBIE可能作为胃癌的一种新型生物标志物和治疗靶点发挥作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aea/11703799/8aeaa8f63f09/12672_2025_1759_Fig1_HTML.jpg

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