作者
Joel M Guthridge, Rufei Lu, Ly Thi-Hai Tran, Cristina Arriens, Teresa Aberle, Stan Kamp, Melissa E Munroe, Nicolas Dominguez, Timothy Gross, Wade DeJager, Susan R Macwana, Rebecka L Bourn, Stephen Apel, Aikaterini Thanou, Hua Chen, Eliza F Chakravarty, Joan T Merrill, Judith A James
发表日期
2020/3/1
期刊
EClinicalMedicine
卷号
20
出版商
Elsevier
简介
Background
The clinical and pathologic diversity of systemic lupus erythematosus (SLE) hinders diagnosis, management, and treatment development. This study addresses heterogeneity in SLE through comprehensive molecular phenotyping and machine learning clustering.
Methods
Adult SLE patients (n = 198) provided plasma, serum, and RNA. Disease activity was scored by modified SELENA-SLEDAI. Twenty-nine co-expression module scores were calculated from microarray gene-expression data. Plasma soluble mediators (n = 23) and autoantibodies (n = 13) were assessed by multiplex bead-based assays and ELISAs. Patient clusters were identified by machine learning combining K-means clustering and random forest analysis of co-expression module scores and soluble mediators.
Findings
SLEDAI scores correlated with interferon, plasma cell, and select cell cycle modules, and with circulating IFN-α …
学术搜索中的文章
JM Guthridge, R Lu, LTH Tran, C Arriens, T Aberle… - EClinicalMedicine, 2020