CILP2 is a potential biomarker for the prediction and therapeutic target of peritoneal metastases in colorectal cancer

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Title
CILP2 is a potential biomarker for the prediction and therapeutic target of peritoneal metastases in colorectal cancer
Author(s)
Y J Ha; Seong-Hwan Park; K H Tak; J L Lee; C W Kim; Jeong Hwan KimSeon-Young KimSeon-Kyu Kim; Y S Yoon
Bibliographic Citation
Scientific Reports, vol. 14, pp. 12487-12487
Publication Year
2024
Abstract
Peritoneal metastases (PM) in colorectal cancer (CRC) is associated with a dismal prognosis. Identifying and exploiting new biomarkers, signatures, and molecular targets for personalised interventions in the treatment of PM in CRC is imperative. We conducted transcriptomic profiling using RNA-seq data generated from the primary tissues of 19 CRC patients with PM. Using our dataset established in a previous study, we identified 1422 differentially expressed genes compared to non-metastatic CRC. The profiling demonstrated no differential expression in liver and lung metastatic CRC. We selected 12 genes based on stringent criteria and evaluated their expression patterns in a validation cohort of 32 PM patients and 84 without PM using real-time reverse transcription-polymerase chain reaction. We selected cartilage intermediate layer protein 2 (CILP2) because of high mRNA expression in PM patients in our validation cohort and its association with a poor prognosis in The Cancer Genome Atlas. Kaplan-Meier survival analysis in our validation cohort demonstrated that CRC patients with high CILP2 expression had significantly poor survival outcomes. Knockdown of CILP2 significantly reduced the proliferation, colony-forming ability, invasiveness, and migratory capacity and downregulated the expression of molecules related to epithelial-mesenchymal transition in HCT116 cells. In an in vivo peritoneal dissemination mouse knockdown of CILP2 also inhibited CRC growth. Therefore, CILP2 is a promising biomarker for the prediction and treatment of PM in CRC.
ISSN
2045-2322
Publisher
Springer-Nature Pub Group
Full Text Link
http://dx.doi.org/10.1038/s41598-024-63366-4
Type
Article
Appears in Collections:
Aging Convergence Research Center > 1. Journal Articles
Division of A.I. & Biomedical Research > Genomic Medicine Research Center > 1. Journal Articles
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