Peritoneal Fluid Cytokines Reveal New Insights of Endometriosis Subphenotypes.
Jieliang ZhouBernard Su Min ChernPeter Barton-SmithJessie Wai Leng PhoonTse Yeun TanVeronique Viardot-FoucaultChee Wai KuHeng Hao TanJerry Kok Yen ChanYie Hou LeePublished in: International journal of molecular sciences (2020)
Endometriosis is a common inflammatory gynecological disorder which causes pelvic scarring, pain, and infertility, characterized by the implantation of endometrial-like lesions outside the uterus. The peritoneum, ovaries, and deep soft tissues are the commonly involved sites, and endometriotic lesions can be classified into three subphenotypes: superficial peritoneal endometriosis (PE), ovarian endometrioma (OE), and deep infiltrating endometriosis (DIE). In 132 women diagnosed laparoscopically with and without endometriosis (n = 73, 59 respectively), and stratified into PE, OE, and DIE, peritoneal fluids (PF) were characterized for 48 cytokines by using multiplex immunoassays. Partial-least-squares-regression analysis revealed distinct subphenotype cytokine signatures-a six-cytokine signature distinguishing PE from OE, a seven-cytokine signature distinguishing OE from DIE, and a six-cytokine-signature distinguishing PE from DIE-each associated with different patterns of biological processes, signaling events, and immunology. These signatures describe endometriosis better than disease stages (p < 0.0001). Pathway analysis revealed the association of ERK1 and 2, AKT, MAPK, and STAT4 linked to angiogenesis, cell proliferation, migration, and inflammation in the subphenotypes. These data shed new insights on the pathophysiology of endometriosis subphenotypes, with the potential to exploit the cytokine signatures to stratify endometriosis patients for targeted therapies and biomarker discovery.
Keyphrases
- cell proliferation
- signaling pathway
- oxidative stress
- genome wide
- gene expression
- machine learning
- risk assessment
- type diabetes
- rectal cancer
- newly diagnosed
- dna methylation
- spinal cord injury
- skeletal muscle
- metabolic syndrome
- vascular endothelial growth factor
- big data
- artificial intelligence
- human health
- endothelial cells