BACKGROUND To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. METHODS Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). RESULTS In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. CONCLUSIONS MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.

中文翻译：

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通过全血转录特征诊断儿童多系统炎症综合征。

背景旨在鉴定区分儿童多系统炎症综合征（MIS-C）与川崎病（KD）、细菌感染和病毒感染的诊断性血液转录组特征。方法 前瞻性招募 2020 年 4 月至 2021 年 4 月期间在英国和欧盟参与医院就诊的 MIS-C 儿童。进行了全血 RNA 测序，将 MIS-C 儿童 (n = 38) 的转录组与 KD 儿童 (n = 136)、明确细菌感染 (DB; n = 188) 和病毒感染 (DV; n = 138）。确定了 MIS-C 组和比较组之间显着差异表达 (SDE) 的基因。使用特征选择来识别能够最佳区分 MIS-C 与其他疾病的基因，随后被转化为 RT-qPCR 检测，并在一个独立的验证集中进行评估，该验证集包括 MIS-C (n = 37)、KD (n = 19)、DB (n = 56)、DV (n = 43) 和 COVID- 19（n = 39）。结果 在发现组中，MIS-C 组和组合比较疾病组之间有 5696 个基因是 SDE。五个基因被确定为潜在的 MIS-C 诊断生物标志物（HSPBAP1、VPS37C、TGFB1、MX2 和 TRBV11-2），在发现集中实现了 96.8%（95% CI：94.6%-98.9%）的 AUC，并被转化为 RT-qPCR 检测。当区分 MIS-C 与 KD、DB 和 DV 时，RT-qPCR 5 基因特征在独立验证集中实现了 93.2%（95% CI：88.3%-97.7%）的 AUC。结论 使用 5 基因血液 RNA 表达特征，可以将 MIS-C 与 KD、DB 和 DV 组区分开来。