Integrating Bulk and Single-Cell RNA Sequencing to Identify Prognostic Genes Associated with Adaptive Immunity and the Tumor Microenvironment in Cholangiocarcinoma for Risk Model Construction

IF 3.1 4区医学 Q3 IMMUNOLOGY

Immunobiology Pub Date : 2026-07-01 Epub Date: 2026-07-06 DOI:10.1016/j.imbio.2026.153213

Wang Jie, Zhou Jixiang, Bao Yang, Wang Yanting, Liu Ling

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引用次数: 0

Abstract

Background

Cholangiocarcinoma (CCA) has a complex tumor microenvironment (TME) and poor immunotherapy response. Understanding how the adaptive immune response (AIR) affects the TME and prognosis is critical.

Methods

All data were obtained from publicly available resources. Differentially expressed genes (DEGs) were identified by comparing tumor vs. normal and stromal/immune score-based samples, and were then intersected with an AIR gene set to obtain candidate genes. Prognostic genes identified by univariate Cox regression were used to build a risk model. Analyses included functional enrichment, immune landscape, drug sensitivity, and single-cell RNA sequencing (scRNA-seq) to identify key cell types, cell–cell communication, and pseudotime trajectories.

Results

Intersection analysis yielded 68 candidate genes, from which eight prognostic genes (C1QA, CD55, ZAP70, CLEC4M, FCAMR, LILRA1, CD80, C17orf99) were identified. The risk model stratified patients into high-risk (HRG) and low-risk (LRG) groups with significantly different overall survival. LRG was enriched for immune activation pathways, whereas HRG exhibited an immune-excluded phenotype (lower CD8+ T cell infiltration, higher exclusion score). LRG showed higher sensitivity to gemcitabine and ABT-737. Macrophages were identified as key cells, with enhanced malignant cell–macrophage communication via the MIF–(CD74 + CXCR4) axis in CCA. Macrophages comprised seven heterogeneous subtypes and five distinct differentiation states. Firmicutes abundance was lower in the HRG. A nomogram integrating risk score and Firmicutes demonstrated favorable predictive performance.

Conclusion

An AIR-related gene signature stratifies CCA prognosis by reflecting TME composition rather than tumor-intrinsic drivers, highlighting immune cell–centric mechanisms and intratumoral microbiota. The nomogram may aid prognostic stratification in research settings.

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整合整体和单细胞RNA测序鉴定与胆管癌适应性免疫和肿瘤微环境相关的预后基因，用于风险模型构建。

背景：胆管癌（CCA）肿瘤微环境（TME）复杂，免疫治疗反应差。了解适应性免疫反应（AIR）如何影响TME和预后是至关重要的。方法：所有数据均来自公开资源。通过比较肿瘤与正常和基质/免疫评分样本来鉴定差异表达基因（DEGs），然后与AIR基因集相交以获得候选基因。采用单因素Cox回归鉴定的预后基因建立风险模型。分析包括功能富集、免疫景观、药物敏感性和单细胞RNA测序（scRNA-seq），以确定关键细胞类型、细胞间通讯和假时间轨迹。结果：交叉分析获得68个候选基因，从中鉴定出8个预后基因（C1QA、CD55、ZAP70、CLEC4M、FCAMR、LILRA1、CD80、C17orf99）。风险模型将患者分为高危组（HRG）和低危组（LRG），总生存率有显著差异。LRG富含免疫激活途径，而HRG表现出免疫排斥表型（CD8+ T细胞浸润较低，排斥评分较高）。LRG对吉西他滨和ABT-737敏感性较高。巨噬细胞被认为是关键细胞，在CCA中通过MIF-(CD74 + CXCR4)轴增强了恶性细胞与巨噬细胞之间的通讯。巨噬细胞包括7种异质亚型和5种不同的分化状态。HRG中厚壁菌的丰度较低。综合风险评分和厚壁时间的nomogram显示了良好的预测性能。结论：air相关基因标记通过反映TME组成而不是肿瘤内在驱动因素来对CCA的预后进行分层，突出了免疫细胞中心机制和肿瘤内微生物群。在研究中，nomogram可以帮助进行预后分层。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Immunobiology 医学-免疫学

CiteScore

5.00

自引率

3.60%

发文量

108

审稿时长

55 days

期刊介绍： Immunobiology is a peer-reviewed journal that publishes highly innovative research approaches for a wide range of immunological subjects, including • Innate Immunity, • Adaptive Immunity, • Complement Biology, • Macrophage and Dendritic Cell Biology, • Parasite Immunology, • Tumour Immunology, • Clinical Immunology, • Immunogenetics, • Immunotherapy and • Immunopathology of infectious, allergic and autoimmune disease.