Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | e0198325 |
| Journal | PLoS One |
| Volume | 13 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 1 2018 |
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ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
Cite this
Identification of a gene-expression predictor for diagnosis and personalized stratification of lupus patients. / Ding, Yan; Li, Hongai; He, Xiaojie; Liao, Wang; Yi, Zhuwen; Yi, Jia; Chen, Zhibin; Moore, Daniel J.; Yi, Yajun; Xiang, Wei.
In: PLoS One, Vol. 13, No. 7, e0198325, 01.07.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Identification of a gene-expression predictor for diagnosis and personalized stratification of lupus patients
AU - Ding, Yan
AU - Li, Hongai
AU - He, Xiaojie
AU - Liao, Wang
AU - Yi, Zhuwen
AU - Yi, Jia
AU - Chen, Zhibin
AU - Moore, Daniel J.
AU - Yi, Yajun
AU - Xiang, Wei
PY - 2018/7/1
Y1 - 2018/7/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049536849&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049536849&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0198325
DO - 10.1371/journal.pone.0198325
M3 - Article
C2 - 29975701
AN - SCOPUS:85049536849
VL - 13
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 7
M1 - e0198325
ER -