结合单细胞和大容量 RNA 测序数据探索甲状腺无节细胞癌的免疫基因表达和潜在调控机制

Kehui Zhou, Shijia Zhang, Jinbiao Shang, Xiabin Lan
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引用次数: 0

摘要

甲状腺癌包括甲状腺乳头状癌(PTC)和间变性甲状腺癌(ATC)。PTC预后良好,而ATC预后不佳,因此需要在ATC中发现新的靶点,以帮助ATC的诊断和治疗。因此,我们分析了来自Gene Expression Omnibus (GEO)的ATC单细胞RNA测序(scRNA-seq)和大量RNA测序(bulk RNA-seq)数据,从import数据库检索免疫相关基因,并鉴定了单细胞亚群中差异表达的免疫基因。使用R中的AUCell包计算单细胞亚组的活性评分并识别活性细胞群。对活性细胞群体中的差异表达基因(DEGs)进行基因本体(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和大量RNA-seq数据来识别重叠的基因。相关转录因子(tf)从trust数据库中检索。利用STRING数据库建立了关键tf的蛋白-蛋白相互作用(PPI)网络。同时,从DGIdb中获得了与关键tf相关的药物。ScRNA-seq聚类分析显示,T/ NK细胞在ATC中分布较多,甲状腺细胞在PTC中分布较多。我们从import数据库中获得264个差异免疫基因(DIGs),并整合scRNA-seq聚类分析来鉴定活性细胞T/NK细胞和骨髓细胞。综合整体RNA-seq分析获得常见免疫基因(CIGs),如TMSB4X、NFKB1、TNFRSF1B和B2M。从trust数据库中获得骨髓细胞中9个与cigg相关的tf (CEBPB、SPI1、NFKB1、RUNX1、NFE2L2、REL、CIITA、KLF6和CEBPD)和T/NK细胞中3个tf (NFKB1、FOXO1和NR3C1)。我们发现的关键基因代表了治疗ATC的潜在靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring immune gene expression and potential regulatory mechanisms in anaplastic thyroid carcinoma using a combination of single-cell and bulk RNA sequencing data.

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.

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