Gene Expression Signatures Can Aid Diagnosis of Sexually Transmitted Infection-Induced Endometritis in Women

Xiaojing Zheng, Catherine M O'Connell, Wujuan Zhong, Taylor B Poston, Harold C Wiesenfeld, Sharon L Hillier, Maria Trent, Charlotte Gaydos, George Tseng, Brandie D Taylor, Toni Darville, Xiaojing Zheng, Catherine M O'Connell, Wujuan Zhong, Taylor B Poston, Harold C Wiesenfeld, Sharon L Hillier, Maria Trent, Charlotte Gaydos, George Tseng, Brandie D Taylor, Toni Darville

Abstract

Sexually transmitted infection (STI) of the upper reproductive tract can result in inflammation and infertility. A biomarker of STI-induced upper tract inflammation would be significant as many women are asymptomatic and delayed treatment increases risk of sequelae. Blood mRNA from 111 women from three cohorts was profiled using microarray. Unsupervised analysis revealed a transcriptional profile that distinguished 9 cases of STI-induced endometritis from 18 with cervical STI or uninfected controls. Using a hybrid feature selection algorithm we identified 21 genes that yielded maximal classification accuracy within our training dataset. Predictive accuracy was evaluated using an independent testing dataset of 5 cases and 10 controls. Sensitivity was evaluated in a separate test set of 12 women with asymptomatic STI-induced endometritis in whom cervical burden was determined by PCR; and specificity in an additional test set of 15 uninfected women with pelvic pain due to unknown cause. Disease module preservation was assessed in 42 women with a clinical diagnosis of pelvic inflammatory disease (PID). We also tested the ability of the biomarker to discriminate STI-induced endometritis from other diseases. The biomarker was 86.7% (13/15) accurate in correctly distinguishing cases from controls in the testing dataset. Sensitivity was 83.3% (5/6) in women with high cervical Chlamydia trachomatis burden and asymptomatic endometritis, but 0% (0/6) in women with low burden. Specificity in patients with non-STI-induced pelvic pain was 86.7% (13/15). Disease modules were preserved in all 8 biomarker predicted cases. The 21-gene biomarker was highly discriminatory for systemic infections, lupus, and appendicitis, but wrongly predicted tuberculosis as STI-induced endometritis in 52.4%. A 21-gene biomarker can identify asymptomatic women with STI-induced endometritis that places them at risk for chronic disease development and discriminate STI-induced endometritis from non-STI pelvic pain and other diseases.

Keywords: Chlamydia; biomarker; gonorrhea; mRNA; pelvic inflammatory disease.

Figures

Figure 1
Figure 1
Diagram of study organization. Blood transcriptional mRNA profiles from cases and controls were analyzed in a training dataset. Identified classifier genes were subsequently validated in an independent testing dataset. The classifier genes were also evaluated in three additional independent patient datasets for sensitivity, specificity, and disease module preservation, respectively. †Cases: women with symptoms consistent with PID and with biopsy confirmed endometrial STI with N. gonorrhoeae and/or C. trachomatis and/or M. genitalium and endometritis. ‡‡Controls: asymptomatic women who were uninfected or infected at the cervix only, with no endometrial inflammation. *Participants recruited from TRAC cohort. **Participants recruited from ACE cohort. ***Participants recruited from TECH-N cohort comprised women with symptoms of PID who had cervical microbiology determined but lacked endometrial data.
Figure 2
Figure 2
Unsupervised hierarchical clustering and principal component analysis (PCA) revealed distinctive blood mRNA transcriptional profiles separating cases from controls. Unsupervised hierarchical clustering of blood transcriptional profiles of women in training (A) and testing (B) datasets using 4952 transcripts. Samples were ordered by hierarchical clustering (Spearman correlation with average linkage) creating a condition tree, upper horizontal edge of heat map; study groupings (clinical phenotypes) are the colored blocks on the top of each profile. Heat map rows are genes; columns are participants. Principle component analysis of the variance in mRNA expression of the subjects depicted in the heat maps in training (C) and testing (D) datasets, using 4,952 transcripts and the same color scheme with each colored square depicting one subject. The x axis represents the first principal component, PC1, which accounts for the largest variance of mRNA expression, and the y axis, PC2, explains the second largest variance.
Figure 3
Figure 3
Predicted probability of STI-induced endometritis in testing datasets using a 21-gene signature and SVM learning algorithm. Each dot represents one subject. The x-axis indicates the biopsy confirmed groups of subjects and y-axis indicates the predicted probability of STI-induced endometritis. The dotted line corresponds to a predicted probability cutoff of 0.5. If the predicted probability of PID is > 0.5, we consider the predicted response as high risk.
Figure 4
Figure 4
Predicted probability using 21-gene signature and SVM of STI-induced endometritis in a target dataset of asymptomatic subjects with biopsy proven subclinical CT/GC-induced endometritis. Each dot represents one subject. The x-axis indicates the log10 cervical C. trachomatis load and the y-axis indicates the predicted probability of STI-induced endometritis.
Figure 5
Figure 5
Predicted probability using 21-gene signature and SVM of STI-induced endometritis in a dataset of women who were symptomatic but were uninfected. Subsets of patients within the dataset were positive and negative for histologic endometritis. Each dot represents one subject. The x-axis indicates two groups of women with pelvic pain without STI. One group has normal histology, the other group has chronic endometritis; y-axis indicates the predicted probability of STI-induced endometritis.
Figure 6
Figure 6
Zsummary statistics reveal module preservation of blood profiles from women with biopsy-confirmed CT/GC-induced endometritis compared to women with biomarker-predicted STI-induced endometritis from independent TECH-N cohort. Colored circles correspond with highly correlated gene modules identified in biopsy-confirmed CT/GC-induced endometritis. The x-axis indicates the number of genes in each module; y axis is the preservation Zsummary value. The dotted lines with cutoff Zsummary of 2 and 10 indicate preservation and high level preservation respectively.
Figure 7
Figure 7
The 21-gene biomarker distinguishes STI-induced endometritis from other infectious and inflammatory conditions. (A) The number of samples within each disease and control group predicted as STI-induced endometritis is summarized in the table. (B) The predicted probability of STI-induced endometritis in each disease and control group using a 21-gene biomarker. Each dot represents one subject. The x-axis indicates the groups of subjects and y-axis indicates the predicted probability of STI-induced endometritis. The dotted line corresponds to a predicted probability cutoff of 0.5. (C) Unsupervised hierarchical clustering using 21-genes revealed distinctive blood mRNA transcriptional profiles separating STI-induced endometritis from other diseases and controls. Study groupings (clinical phenotypes) are the colored blocks on the top of each profile. Heat map rows are genes; columns are participants. Transformed expression levels are indicated by color scale, with red representing relative high expression and blue relative low expression.

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