Clinical trial NCT06100471: Evaluation of Genetic Signature in Endometriosis Disease by Non Invasive Sampling
Official title: Evaluation of Genetic Signature in Endometriosis Disease by Non Invasive Sampling
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Endometriosis
Hypofertility
Keywords: endo
Lead sponsor: Eurofins Genoma
Sponsor class: INDUSTRY
First posted: 2023-10-25
Enrollment: 150
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 45
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Groups of study:

* patients with diagnosed endometriotic adnexal pathology (case, CA)
* patients with non-endometriotic adnexal pathology (control, CO)
* patients with no gynecological pathology, not undergoing surgery (analytical control)

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.

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).

Eligibility criteria: Inclusion Criteria:

* Case population (CA), patients with endometriotic adnexal pathology
* Control population (CO), patients with non-endometriotic adnexal pathology
* Analytical control population (C-), patients with no gynecologic pathology, not undergoing surgery

Exclusion Criteria:

* Women with ages outside the inclusion range
* Pregnant patient
* Patient with a personal history of cancer
* Patient infected with HIV

Primary outcomes:
measure: Next Generation Sequencing technical validation
timeFrame: 1 month
measure: Wet lab kit validation
timeFrame: 1 month
Secondary outcomes:
measure: Tissue validation
timeFrame: 2 months
measure: Clinical validation
timeFrame: 4 months
measure: Biomarkers identification
timeFrame: 7 months
Other outcomes:
Locations:
city: Rome
country: Italy
facility: Eurofins Genoma
Study officials:
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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 Plasma Micro-RNA Expression. Diagnostics (Basel). 2022 Jan 12;12(1):175. doi: 10.3390/diagnostics12010175.
citation: 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.