Host Mesothelin Expression Increases Ovarian Cancer Metastasis in the Peritoneal Microenvironment.
Tyvette S HilliardBrooke KowalskiKyle IwamotoElizabeth A AgadiYueying LiuJing YangMarwa AsemYuliya KlymenkoJeff JohnsonZonggao ShiGifty MarfowaaMadeleine G YemcPhillip PetraskoM Sharon StackPublished in: International journal of molecular sciences (2021)
Mesothelin (MSLN), a glycoprotein normally expressed by mesothelial cells, is overexpressed in ovarian cancer (OvCa) suggesting a role in tumor progression, although the biological function is not fully understood. OvCa has a high mortality rate due to diagnosis at advanced stage disease with intraperitoneal metastasis. Tumor cells detach from the primary tumor as single cells or multicellular aggregates (MCAs) and attach to the mesothelium of organs within the peritoneal cavity producing widely disseminated secondary lesions. To investigate the role of host MSLN in the peritoneal cavity we used a mouse model with a null mutation in the MSLN gene (MSLNKO). The deletion of host MSLN expression modified the peritoneal ultrastructure resulting in abnormal mesothelial cell surface architecture and altered omental collagen fibril organization. Co-culture of murine OvCa cells with primary mesothelial cells regardless of MSLN expression formed compact MCAs. However, co-culture with MSLNKO mesothelial cells resulted in smaller MCAs. An allograft tumor study, using wild-type mice (MSLNWT) or MSLNKO mice injected intraperitoneally with murine OvCa cells demonstrated a significant decrease in peritoneal metastatic tumor burden in MSLNKO mice compared to MSLNWT mice. Together, these data support a role for host MSLN in the progression of OvCa metastasis.
Keyphrases
- induced apoptosis
- cell cycle arrest
- poor prognosis
- stem cells
- mouse model
- endoplasmic reticulum stress
- wild type
- type diabetes
- squamous cell carcinoma
- signaling pathway
- small cell lung cancer
- gene expression
- skeletal muscle
- dna methylation
- binding protein
- genome wide
- long non coding rna
- machine learning
- risk factors
- transcription factor
- big data
- cardiovascular disease
- pi k akt
- data analysis