A Bioinformatics Approach to MicroRNA-Sequencing Analysis Based on Human Saliva Samples of Patients with Endometriosis

Sofiane Bendifallah, Yohann Dabi, Stéphane Suisse, Ludmila Jornea, Delphine Bouteiller, Cyril Touboul, Anne Puchar, Emile Daraï, Sofiane Bendifallah, Yohann Dabi, Stéphane Suisse, Ludmila Jornea, Delphine Bouteiller, Cyril Touboul, Anne Puchar, Emile Daraï

Abstract

Endometriosis, defined by the presence of endometrium-like tissue outside the uterus, affects 2-10% of the female population, i.e., around 190 million women, worldwide. The aim of the prospective ENDO-miRNA study was to develop a bioinformatics approach for microRNA-sequencing analysis of 200 saliva samples for miRNAome expression and to test its diagnostic accuracy for endometriosis. Among the 200 patients, 76.5% (n = 153) had confirmed endometriosis and 23.5% (n = 47) had no endometriosis (controls). Small RNA-seq of 200 saliva samples yielded ~4642 M raw sequencing reads (from ~13.7 M to ~39.3 M reads/sample). The number of expressed miRNAs ranged from 1250 (outlier) to 2561 per sample. Some 2561 miRNAs were found to be differentially expressed in the saliva samples of patients with endometriosis compared with the control patients. Among these, 1.17% (n = 30) were up- or downregulated. Among these, the F1-score, sensitivity, specificity, and AUC ranged from 11-86.8%, 5.8-97.4%, 10.6-100%, and 39.3-69.2%, respectively. Here, we report a bioinformatic approach to saliva miRNA sequencing and analysis. We underline the advantages of using saliva over blood in terms of ease of collection, reproducibility, stability, safety, non-invasiveness. This report describes the whole saliva transcriptome to make miRNA quantification a validated, standardized, and reliable technique for routine use. The methodology could be applied to build a saliva signature of endometriosis.

Keywords: NGS; bioinformatics; endometriosis; miRNA; saliva.

Conflict of interest statement

S. Suisse is a former employee of Ziwig, Inc. The remaining authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.

Figures

Figure 1
Figure 1
(A) Distribution of expressed miRs in the 200 saliva samples; (B) distribution of expressed miRNAs in the samples by diagnosis.
Figure 2
Figure 2
Overall composition of processed reads for saliva sample RNA reads = miRNAs + piRNAs + rRNAs + tRNAs + mRNAs + other; filtered reads = reads with no adapters + reads with low quality bases + reads too short; not characterized/mappable reads = mapped reads to GRCh38 that could not be characterized as a particular type; not characterized/not mappable reads = reads that could not be mapped.
Figure 3
Figure 3
Volcano plot of expressed miRNAs in saliva for endometriosis.
Figure 4
Figure 4
Small RNA-seq defines differentially expressed miRNAs in the saliva of endometriosis patients.
Figure 5
Figure 5
Clustering of accuracy values. In blue: F1-Score; In orange: Sensitivity; In grey: Specificity.

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