niEndometriosi: Evaluation of Genetic Signature in Endometriosis Disease by Non Invasive Sampling
Study Details
Study Description
Brief Summary
Endometriosis is a disease that affects 10-15% of the general population and 50% of infertile women. It is characterized by the presence of endometrial tissue outside the uterine cavity. Endometriosis can lead to infertility by interfering through endocrine and mechanical alterations on the function of the ovaries, fallopian tubes, and uterus. The aim of the study is to define the differential expression of a cluster of RNAs tissue driven for the identification of an RNA profile in saliva, specific for endometriosis. This study focuses on the expression of genes involved in the control and regulation of apoptosis, cell survival, metabolism, cell adhesion and invasion, angiogenesis, inflammation, and estrogen receptor expression levels.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
Detailed Description
Retrospective selection based on anamnestic criteria of: 50 patients with diagnosed endometriotic adnexal pathology (case, CA), 50 patients with non-endometriotic adnexal pathology (control, CO) and 50 patients with no gynecological pathology, not undergoing surgery (analytical control).
The study involves collecting a saliva sample from all patients involved in the study, and performing a biopsy from both patients with endometriotic adnexal pathology (CA) and patients with non-endometriotic adnexal pathology (CO).
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| case Patients with endometriotic adnexal pathology |
Other: Observational study
Observational study in different tissues in identifing new genetic markers related to endometriosis disease
|
| control Patients with non endometriotic adnexal pathology |
Other: Observational study
Observational study in different tissues in identifing new genetic markers related to endometriosis disease
|
| analytical control Patients with no gynecological pathology, not undergoing surgery |
Other: Observational study
Observational study in different tissues in identifing new genetic markers related to endometriosis disease
|
Outcome Measures
Primary Outcome Measures
- Technical validation [1 months]
Technical workflow validation of RNA exctraction from saliva and FFPE-tissues. Sequencing reads quality will be assessed via the FASTQC tool, while proper targeting of miRNome will be checked considering the reads aligned to public miRNA sequence databases.
Secondary Outcome Measures
- Tissue validation [2 months]
Genetic profiling of miRNOME from saliva versus FFPE-tissues
- Clinical validation [4 months]
Correlation of miRNOME signature in saliva among cases, controls and analytical groups
- Biomarkers identification [7 months]
Identification of specific signature related to endometriosis, with at least 2 Log2fold change
Eligibility Criteria
Criteria
Inclusion Criteria:
-
Case population (CA), patients with endometriotic adnexal pathology
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Control population (CO), patients with non-endometriotic adnexal pathology
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Analytical control population (C-), patients with no gynecologic pathology, not undergoing surgery
Exclusion Criteria:
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Women with ages outside the inclusion range
-
Pregnant patient
-
Patient with a personal history of cancer
-
Patient infected with HIV
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Eurofins Genoma | Rome | Italy | 00138 |
Sponsors and Collaborators
- Eurofins Genoma
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
- Bendifallah S, Dabi Y, Suisse S, Jornea L, Bouteiller D, Touboul C, Puchar A, Darai E. A Bioinformatics Approach to MicroRNA-Sequencing Analysis Based on Human Saliva Samples of Patients with Endometriosis. Int J Mol Sci. 2022 Jul 21;23(14):8045. doi: 10.3390/ijms23148045.
- Bendifallah S, Dabi Y, Suisse S, Jornea L, Bouteiller D, Touboul C, Puchar A, Darai E. MicroRNome analysis generates a blood-based signature for endometriosis. Sci Rep. 2022 Mar 8;12(1):4051. doi: 10.1038/s41598-022-07771-7.
- Bendifallah S, Puchar A, Suisse S, Delbos L, Poilblanc M, Descamps P, Golfier F, Touboul C, Dabi Y, Darai E. Machine learning algorithms as new screening approach for patients with endometriosis. Sci Rep. 2022 Jan 12;12(1):639. doi: 10.1038/s41598-021-04637-2.
- Bendifallah S, Suisse S, Puchar A, Delbos L, Poilblanc M, Descamps P, Golfier F, Jornea L, Bouteiller D, Touboul C, Dabi Y, Darai E. Salivary MicroRNA Signature for Diagnosis of Endometriosis. J Clin Med. 2022 Jan 26;11(3):612. doi: 10.3390/jcm11030612.
- Dabi Y, Suisse S, Jornea L, Bouteiller D, Touboul C, Puchar A, Darai E, Bendifallah S. Clues for Improving the Pathophysiology Knowledge for Endometriosis Using Serum Micro-RNA Expression. Diagnostics (Basel). 2022 Jan 12;12(1):175. doi: 10.3390/diagnostics12010175.
- Dabi Y, Suisse S, Puchar A, Delbos L, Poilblanc M, Descamps P, Haury J, Golfier F, Jornea L, Bouteiller D, Touboul C, Darai E, Bendifallah S. Endometriosis-associated infertility diagnosis based on saliva microRNA signatures. Reprod Biomed Online. 2023 Jan;46(1):138-149. doi: 10.1016/j.rbmo.2022.09.019. Epub 2022 Sep 27.
- 01-23