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Excess shed mesothelin disrupts pancreatic cancer cell clustering to impair peritoneal colonization

GSE260587 Homo sapiens Expression profiling by high throughput sequencing 39 samples 2024/12/19 GPL16791
Summary
Peritoneum is the second most common site of metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Peritoneal colonization is impaired in PDAC cells with knock-out (KO) of the cancer surface antigen mesothelin (MSLN) or by introducing Y318A mutation in MSLN to prevent binding to mucin-16 (MUC-16). MSLN has a membrane-bound form but is also shed to release soluble MSLN (sMSLN). Their individual roles in peritoneal metastasis are unknown. Here, a C-terminal truncated MSLN mutant (∆591) incapable of cell membrane insertion but proficient in secretion was engineered. Expression of ∆591 MSLN failed to rescue peritoneal metastasis in MSLN KO cells and inhibited peritoneal colonization when overexpressed in WT PDAC cells. Exposing PDAC cells to conditioned medium (CM) containing excess sMSLN impaired cancer cell clustering in vitro and in peritoneal fluid in vivo, while CM containing only Y318A sMSLN did not. These data demonstrate that interaction of membrane-bound MSLN with MUC-16 promotes cell clustering that is critical for efficient peritoneal metastasis. However, peritoneal colonization by MSLN KO cells was rescued by expression of ∆591 mutant MSLN bearing Y318A mutation, suggesting that sMSLN also has a MUC-16-independent role in peritoneal spread. Alterations in inflammatory signaling pathways occurred following KO cell exposure to CM containing sMSLN, and CM from cancer cells with intact peritoneal metastasis provoked increased KO cell secretion of IL-1α. While excess sMSLN inhibits cell clustering and peritoneal colonization, sMSLN may also promote PDAC peritoneal metastasis independent of MUC-16.
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NCBI GEO page ↗ Paper (PMID 39673668) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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