Metabolomic Detection Between Pancreatic Cancer and Liver Metastasis Nude Mouse Models Constructed by Using the PANC1-KAI1/CD82 Cell Line

利用PANC1-KAI1/CD82细胞系构建胰腺癌及肝转移裸鼠模型的代谢组学检测

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作者:Shuo Wang, Jiang Chen, Hongyu Li, Xingshun Qi, Xu Liu, Xiaozhong Guo

Background

Pancreatic cancer (PC) has a poor prognosis and is prone to liver metastasis. The KAI1/CD82 gene inhibits PC metastasis. This study aimed to explore differential metabolites and enrich the pathways in serum samples between PC and liver metastasis nude mouse models stably expressing KAI1/CD82.

Conclusions

Our findings suggested that the metabolomic approach may be a useful method to detect potential biomarkers in PC.

Methods

KAI1/CD82-PLV-EF1α-MCS-IRES-Puro vector and PANC1 cell line stably expressing KAI1/CD82 were constructed for the first time. This cell line was used to construct 3 PC nude mouse models and 3 liver metastasis nude mouse models. The different metabolites and Kyoto encyclopedia of genes and genomes (KEGG) and human metabolome database (HMDB) enrichment pathways were analyzed using the serum samples of the 2 groups of nude mouse models on the basis of untargeted ultra-performance liquid chromatography-tandem mass spectrometry platform.

Results

KAI1/CD82-PLV-EF1α-MCS-IRES-Puro vector and PANC1 cell line stably expressing KAI1/CD82 were constructed successfully, and all nude mouse models survived and developed cancers. Among the 1233 metabolites detected, 18 metabolites (9 upregulated and 9 downregulated) showed differences. In agreement with the literature data, the most significant differences between both groups were found in the levels of bile acids (taurocholic acid, chenodeoxycholic acid), glycine, prostaglandin E2, vitamin D, guanosine monophosphate, and inosine. Bile recreation, primary bile acid biosynthesis, and purine metabolism KEGG pathways and a series of HMDB pathways (P < .05) contained differential metabolites that may be associated with liver metastasis from PC. However, the importance of these metabolites on PC liver metastases remains to be elucidated. Conclusions: Our findings suggested that the metabolomic approach may be a useful method to detect potential biomarkers in PC.

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