利用单细胞和批量RNA测序数据相结合的方法探索间变性甲状腺癌中的免疫基因表达及潜在调控机制。
Exploring immune gene expression and potential regulatory mechanisms in anaplastic thyroid carcinoma using a combination of single-cell and bulk RNA sequencing data.
作者信息
Zhou Kehui, Zhang Shijia, Shang Jinbiao, Lan Xiabin
机构信息
Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang 310022, China; Department of Thyroid Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China.
Department of Thyroid Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, Zhejiang 310022, China.
出版信息
Comput Biol Chem. 2025 Apr;115:108311. doi: 10.1016/j.compbiolchem.2024.108311. Epub 2024 Dec 7.
Thyroid cancer includes papillary thyroid carcinoma (PTC) and anaplastic thyroid carcinoma (ATC). While PTC has an excellent prognosis, ATC has a dismal prognosis, necessitating the identification of novel targets in ATC to aid in ATC diagnosis and treatment. Therefore, we analyzed ATC single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data from the Gene Expression Omnibus (GEO), retrieved immune-related genes from the ImmPort database, and identified differentially expressed immune genes within single-cell subgroups. The AUCell package in R was used to calculate activity scores for single-cell subgroups and identify active cell populations. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on differentially expressed genes (DEGs) in active cell populations. Then, we integrated thyroid-cancer scRNA-seq and bulk RNA-seq data to identify overlapping DEGs. Relevant transcription factors (TFs) were retrieved from the TRRUST database. A protein-protein interaction (PPI) network for key TFs was created using the STRING database. Simultaneously, drugs associated with key TFs were obtained from DGIdb. ScRNA-seq cluster analysis showed that T/natural killer (NK) cells were more distributed in ATC and that thyrocytes cells were more distributed in PTC. We obtained 264 differential immune genes (DIGs) from the IMMPORT database and integrated scRNA-seq cluster analysis to identify the active cell T/NK cells and myeloid cells. Integrated bulk RNA-seq analysis obtained common immune genes (CIGs) such as TMSB4X, NFKB1, TNFRSF1B, and B2M. The nine CIG-related TFs (CEBPB, SPI1, NFKB1, RUNX1, NFE2L2, REL, CIITA, KLF6, and CEBPD) in myeloid cells and three TFs (NFKB1, FOXO1, and NR3C1) in T/NK cells were obtained from the TRRUST database. The key genes we identified represent potential targets for treating ATC.
甲状腺癌包括乳头状甲状腺癌（PTC）和未分化甲状腺癌（ATC）。虽然PTC预后良好，但ATC预后很差，因此需要在ATC中鉴定新的靶点以辅助其诊断和治疗。因此，我们分析了来自基因表达综合数据库（GEO）的ATC单细胞RNA测序（scRNA-seq）和批量RNA测序（bulk RNA-seq）数据，从免疫数据库（ImmPort）中检索免疫相关基因，并在单细胞亚组中鉴定差异表达的免疫基因。使用R语言中的AUCell软件包计算单细胞亚组的活性评分并识别活跃细胞群体。对活跃细胞群体中的差异表达基因（DEG）进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。然后，我们整合甲状腺癌scRNA-seq和bulk RNA-seq数据以识别重叠的DEG。从TRRUST数据库中检索相关转录因子（TF）。使用STRING数据库创建关键TF的蛋白质-蛋白质相互作用（PPI）网络。同时，从DGIdb获得与关键TF相关的药物。scRNA-seq聚类分析表明，T/自然杀伤（NK）细胞在ATC中分布更多，而甲状腺细胞在PTC中分布更多。我们从IMMPORT数据库中获得了264个差异免疫基因（DIG），并整合scRNA-seq聚类分析以识别活跃细胞T/NK细胞和髓样细胞。整合的批量RNA-seq分析获得了常见免疫基因（CIG），如TMSB4X、NFKB1、TNFRSF1B和B2M。从TRRUST数据库中获得了髓样细胞中的9个与CIG相关的TF（CEBPB、SPI1、NFKB1、RUNX1、NFE2L2、REL、CIITA、KLF6和CEBPD）以及T/NK细胞中的3个TF（NFKB1、FOXO1和NR3C1）。我们鉴定出的关键基因代表了治疗ATC的潜在靶点。
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