Identification of a gene-expression predictor for diagnosis and personalized stratification of lupus patients

Yan Ding, Hongai Li, Xiaojie He, Wang Liao, Zhuwen Yi, Jia Yi, Zhibin Chen, Daniel J. Moore, Yajun Yi, Wei Xiang

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 languageEnglish (US)
Article numbere0198325
JournalPLoS One
Volume13
Issue number7
DOIs
StatePublished - Jul 1 2018

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lupus erythematosus
Gene expression
Systemic Lupus Erythematosus
Gene Expression
gene expression
Biomarkers
Genes
Monitoring
DNA
Sensors
Testing
autoimmune diseases
Autoimmune Diseases
meta-analysis
Cluster Analysis
Meta-Analysis
biomarkers
Databases

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 journalArticle

Ding, Yan ; Li, Hongai ; He, Xiaojie ; Liao, Wang ; Yi, Zhuwen ; Yi, Jia ; Chen, Zhibin ; Moore, Daniel J. ; Yi, Yajun ; Xiang, Wei. / Identification of a gene-expression predictor for diagnosis and personalized stratification of lupus patients. In: PLoS One. 2018 ; Vol. 13, No. 7.
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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.",
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