He Jinhua, Luo 罗海涛 Haitao, Wang 王伟 Wei, Bu 卜德超 Dechao, Zou 邹正楷 Zhengkai, Wang 王浩霖 Haolin, Tang Hongzhen, Han Zeping, Luo Wenfeng, Shen Jian, Xie Fangmei, Zhao 赵屹 Yi, Xiang Zhiming
Central Laboratory, The Affiliated Panyu Central Hospital of Guangzhou Medical University, Guangzhou 511400, China.
Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co., Ltd., Shenzhen 518081, China.
Genomics Proteomics Bioinformatics. 2025 May 10;23(1). doi: 10.1093/gpbjnl/qzae067.
Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells, revealing their tissue-specific gene expression patterns and functions in cancer immunity. Comprehensive assessments of immune cells within and across tissues will provide us with a deeper understanding of the tumor immune system in general. Here, we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer (CIEC), the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell types or states. CIEC version 1.0 consists of 480 samples covering primary tumor, adjacent normal tissue, lymph node, metastasis tissue, and peripheral blood from 323 cancer patients. By applying integrative analysis, we constructed an immune cell type/state map for each context, and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) algorithm to estimate the enrichment for context-specific immune cell types/states. In addition, CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues, including expression map, correlation, similar gene detection, signature score, and expression comparison. We believe that CIEC will be a valuable resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune biomarker development and immunotherapy strategies. CIEC is freely accessible at http://ciec.gene.ac/.
单细胞转录组测序技术已被应用于解码免疫细胞的细胞类型和功能状态，揭示它们在癌症免疫中的组织特异性基因表达模式和功能。对组织内和组织间的免疫细胞进行全面评估，将使我们对肿瘤免疫系统有更深入的总体了解。在此，我们展示了癌症基因列表的跨组织免疫细胞类型或状态富集分析（CIEC），这是首个基于网络的应用程序，它整合了数据库和富集分析，以估计跨组织免疫细胞类型或状态。CIEC 1.0版本包含来自323名癌症患者的480个样本，涵盖原发性肿瘤、癌旁正常组织、淋巴结、转移组织和外周血。通过应用综合分析，我们为每个背景构建了免疫细胞类型/状态图谱，并采用我们之前开发的基于京都基因与基因组百科全书（KEGG）直系同源注释系统（KOBAS）算法来估计特定背景免疫细胞类型/状态的富集情况。此外，CIEC还为用户提供了一个易于使用的在线界面，以全面分析跨多个组织映射的免疫细胞特征，包括表达图谱、相关性、相似基因检测、特征分数和表达比较。我们相信，CIEC将成为探索癌症患者免疫细胞内在特征以及潜在指导新型癌症免疫生物标志物开发和免疫治疗策略的宝贵资源。可通过http://ciec.gene.ac/免费访问CIEC。
