Titre :

Exploration of the core protein network under endometriosis symptomatology using a computational approach.

Auteur(s) :

El Idrissi, Fatima [Auteur]
Fruchart, Mathilde [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Belarbi, Karim-Ali [Auteur]
Lille Neurosciences & Cognition (LilNCog) - U 1172
Lamer, Antoine [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Dubois-Deruy, Emilie [Auteur]
Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement (RID-AGE) - U1167
Lemdani, Mohamed [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
N'Guessan, Assi [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Laboratoire Paul Painlevé - UMR 8524
Guinhouya, Benjamin [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Zitouni, Djamel [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Titre de la revue :

Frontiers in Endocrinology

Nom court de la revue :

Front Endocrinol (Lausanne)

Numéro :

13

Pagination :

869053

Date de publication :

2022-09-20

ISSN :

1664-2392

Mot(s)-clé(s) en anglais :

endometrium
cell signaling
female infertility
systems biology
text-mining

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

Background: Endometriosis is defined by implantation and invasive growth of endometrial tissue in extra-uterine locations causing heterogeneous symptoms, and a unique clinical picture for each patient. Understanding the ...
Lire la suite >Background: Endometriosis is defined by implantation and invasive growth of endometrial tissue in extra-uterine locations causing heterogeneous symptoms, and a unique clinical picture for each patient. Understanding the complex biological mechanisms underlying these symptoms and the protein networks involved may be useful for early diagnosis and identification of pharmacological targets. Methods: In the present study, we combined three approaches (i) a text-mining analysis to perform a systematic search of proteins over existing literature, (ii) a functional enrichment analysis to identify the biological pathways in which proteins are most involved, and (iii) a protein–protein interaction (PPI) network to identify which proteins modulate the most strongly the symptomatology of endometriosis. Results: Two hundred seventy-eight proteins associated with endometriosis symptomatology in the scientific literature were extracted. Thirty-five proteins were selected according to degree and betweenness scores criteria. The most enriched biological pathways associated with these symptoms were (i) Interleukin-4 and Interleukin-13 signaling (p = 1.11 x 10-16), (ii) Signaling by Interleukins (p = 1.11 x 10-16), (iii) Cytokine signaling in Immune system (p = 1.11 x 10-16), and (iv) Interleukin-10 signaling (p = 5.66 x 10-15). Conclusion: Our study identified some key proteins with the ability to modulate endometriosis symptomatology. Our findings indicate that both pro- and anti-inflammatory biological pathways may play important roles in the symptomatology of endometriosis. This approach represents a genuine systemic method that may complement traditional experimental studies. The current data can be used to identify promising biomarkers for early diagnosis and potential therapeutic targets.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

• Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement (RID-AGE) - U1167
• Laboratoire Paul Painlevé - UMR 8524
• Lille Neurosciences & Cognition (LilNCog) - U 1172
• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2023-11-15T03:20:16Z
2024-04-08T12:50:36Z