Potential genetic biomarkers predict adverse pregnancy outcome during early and mid-pregnancy in women with systemic lupus erythematosus

Abstract

BackgroundEffectively predicting the risk of adverse pregnancy outcome (APO) in women with systemic lupus erythematosus (SLE) during early and mid-pregnancy is a challenge. This study was aimed to identify potential markers for early prediction of APO risk in women with SLE.MethodsThe GSE108497 gene expression dataset containing 120 samples (36 patients, 84 controls) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed, and differentially expressed genes (DEGs) were screened to define candidate APO marker genes. Next, three individual machine learning methods, random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator, were combined to identify feature genes from the APO candidate set. The predictive performance of feature genes for APO risk was assessed using area under the receiver operating characteristic curve (AUC) and calibration curves. The potential functions of these feature genes were finally analyzed by conventional gene set enrichment analysis and CIBERSORT algorithm analysis.ResultsWe identified 321 significantly up-regulated genes and 307 down-regulated genes between patients and controls, along with 181 potential functionally associated genes in the WGCNA analysis. By integrating these results, we revealed 70 APO candidate genes. Three feature genes, SEZ6, NRAD1, and LPAR4, were identified by machine learning methods. Of these, SEZ6 (AUC = 0.753) showed the highest in-sample predictive performance for APO risk in pregnant women with SLE, followed by NRAD1 (AUC = 0.694) and LPAR4 (AUC = 0.654). After performing leave-one-out cross validation, corresponding AUCs for SEZ6, NRAD1, and LPAR4 were 0.731, 0.668, and 0.626, respectively. Moreover, CIBERSORT analysis showed a positive correlation between regulatory T cell levels and SEZ6 expression (P < 0.01), along with a negative correlation between M2 macrophages levels and LPAR4 expression (P < 0.01).ConclusionsOur preliminary findings suggested that SEZ6, NRAD1, and LPAR4 might represent the useful genetic biomarkers for predicting APO risk during early and mid-pregnancy in women with SLE, and enhanced our understanding of the origins of pregnancy complications in pregnant women with SLE. However, further validation was required.

Changes in Clinical Manifestations and Course of Systemic Lupus Erythematosus and Secondary Antiphospholipid Syndrome over Three Decades

“…Regarding other factors, the aberrant expression and the potential regulatory function of long noncoding RNAs in the placenta may play roles in the pathomechanism of lupus pregnancies [ 25 ]. Furthermore, three predictive gene biomarkers of adverse pregnancy outcomes were identified in pregnant women with SLE (SEZ6, NRAD1, and LPAR4) [ 26 ]. Based on these studies, the causes of pregnancy complications in lupus are very diverse and not fully known yet; therefore, further research is needed.…”

Systemic lupus in the era of machine learning medicine

“…147 Potential genetic biomarkers identified with ML for predicting adverse pregnancy outcomes during early and mid-pregnancy in patients with SLE are SEZ6, NRAD1 and LPAR4. 160 There were also prediction models that used only routinely available clinical variables (eg, levels of alanine transaminase, alkaline phosphatase, lactate dehydrogenase, gammaglutamyl transferase, erythrocytes, C3, C4, autoantibodies as well as maternal age, smoking status, hydroxychloroquine use and disease duration) (super learning, AUC 0.78; RF, AUC 0.917). 161 162 Other outcomes for patients with SLE included reduced risk of breast cancer with the presence of prognostic genetic biomarkers (ie, IRF7, IFI35 and EIF2AK2 gene expression) identified with LASSO.…”