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.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 PLoS+One
2018 ; 13
(7
): e0198325
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Identification of a gene-expression predictor for diagnosis and personalized
stratification of lupus patients
#MMPMID29975701
Ding Y
; Li H
; He X
; Liao W
; Yi Z
; Yi J
; Chen Z
; Moore DJ
; Yi Y
; Xiang W
PLoS One
2018[]; 13
(7
): e0198325
PMID29975701
show ga
Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a
wide spectrum of clinical manifestations and degrees of severity. Few genomic
biomarkers for SLE have been validated and employed to inform clinical
classifications and decisions. To discover and assess the gene-expression based
SLE predictors in published studies, we performed a meta-analysis using our
established signature database and a data similarity-driven strategy. From 13
training data sets on SLE gene-expression studies, we identified a SLE
meta-signature (SLEmetaSig100) containing 100 concordant genes that are involved
in DNA sensors and the IFN signaling pathway. We rigorously examined
SLEmetaSig100 with both retrospective and prospective validation in two
independent data sets. Using unsupervised clustering, we retrospectively
elucidated that SLEmetaSig100 could classify clinical samples into two groups
that correlated with SLE disease status and disease activities. More importantly,
SLEmetaSig100 enabled personalized stratification demonstrating its ability to
prospectively predict SLE disease at the individual patient level. To evaluate
the performance of SLEmetaSig100 in predicting SLE, we predicted 1,171 testing
samples to be either non-SLE or SLE with positive predictive value (97-99%),
specificity (85%-84%), and sensitivity (60-84%). Our study suggests that
SLEmetaSig100 has enhanced predictive value to facilitate current SLE clinical
classification and provides personalized disease activity monitoring.