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通过 eQTL 定位识别结直肠癌免疫生物标志物:一项孟德尔随机化和转录组分析研究

英文原题:Identification of colorectal cancer immune biomarkers via eQTL mapping: a Mendelian randomization and transcriptomic analysis study.

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Identification of colorectal cancer immune biomarkers via eQTL mapping: a Mendelian randomization and transcriptomic analysis study.

PubMed 2026/04/14(内容时间) PeerJ Q2 · IF 2.9(JCR 2025)

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研究概要

本研究表明 CXCR1、HECW2 和 ATP13A4 可能参与 CRC 的发生发展,为靶向和免疫治疗研究提供了参考。

研究思路结论见上方概要

结直肠癌(CRC)仍然是全球癌症相关死亡的主要原因。识别有效的分子靶点对于推进精准医学和预后策略至关重要。本研究旨在通过整合生物信息学分析揭示关键的CRC生物标志物,为治疗开发提供机制性见解。

我们分析了来自基因表达综合数据库(GEO)的三个CRC数据集。通过表达数量性状位点(eQTL)分析识别工具变量(IVs),随后将其用于与CRC全基因组关联研究(GWAS)数据的孟德尔随机化(MR)分析。将MR相关基因与差异表达基因(DEGs)取交集,以筛选疾病相关的关键基因。使用基因本体论(GO)、京都基因与基因组百科全书(KEGG)和基因集富集分析(GSEA)进行功能富集分析。此外,还进行了免疫细胞浸润和基因-免疫相关性分析。最后,使用独立的GEO、癌症基因组图谱(TCGA)数据集、基于汇总数据的孟德尔随机化(SMR)以及CRC细胞系中的定量逆转录聚合酶链反应(qRT-PCR)进行验证。

共鉴定出776个上调DEGs和981个下调DEGs。优先筛选出9个关键基因:ATP13A4、CD1C、METTL7A、SLC18A1、CREB5、CXCR1、GZMB、HECW2和TEAD2,主要参与细胞因子受体相互作用通路。CIBERSORT分析显示,CRC中活化CD4+记忆T细胞和M0巨噬细胞增加,同时浆细胞和自然杀伤(NK)细胞减少。关键基因与免疫细胞亚群(例如中性粒细胞、肥大细胞)呈显著相关性,突显其在CRC免疫生物学中的作用。通过SMR和qRT-PCR检测验证,显示四个靶基因(CXCR1、HECW2、ATP13A4)存在显著失调(P < 0.05)。

展开英文摘要原文

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. The identification of effective molecular targets is crucial for advancing precision medicine and prognostic strategies. This study aims to uncover key CRC biomarkers through integrative bioinformatics analyses, providing mechanistic insights for therapeutic development.

We analyzed three CRC datasets from the Gene Expression Omnibus (GEO) database. Expression quantitative trait loci (eQTL) analysis was performed to identify instrumental variables (IVs), which were subsequently used in Mendelian Randomization (MR) analysis with CRC Genome-Wide Association Study (GWAS) data. MR-associated genes were intersected with differentially expressed genes (DEGs) to screen disease-related key genes. Functional enrichment analyses were conducted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Additionally, immune cell infiltration and gene-immune correlation analyses were performed. Finally, validation was performed using independent GEO, The Cancer Genome Atlas (TCGA) datasets, summary data-based Mendelian randomization (SMR) and quantitative reverse transcription polymerase chain reaction (qRT-PCR) in CRC cell lines.

A total of 776 upregulated and 981 downregulated DEGs were identified. Nine key genes were prioritized: ATP13A4, CD1C, METTL7A, SLC18A1, CREB5, CXCR1, GZMB, HECW2, and TEAD2 , predominantly involved in cytokine receptor interaction pathways. CIBERSORT analysis revealed increased activated CD4+ memory T cells and M0 macrophages, alongside decreased plasma cells and natural killer (NK) cells in CRC. Key genes demonstrated significant correlation with immune cell subsets ( e.g. , neutrophils, mast cells), highlighting their role in CRC immunobiology. Validation via SMR and qRT-PCR assays demonstrated significant dysregulation of four target genes ( CXCR1 , HECW2 , ATP13A4 ) ( P < 0.05). DISCUSSION: This study suggests that CXCR1 , HECW2 , and ATP13A4 may be involved in CRC development, providing a reference for targeted and immunotherapy research.

论文信息

作者
Pu G、Yin Z、Zhang X、Liu Z、Yang C、Xu C、Lai M
单位
School of Basic Medicine, Dali University, Dali, China.China
期刊
PeerJ2026
原文标识
PubMed 42004723 · DOI 10.7717/peerj.21070