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

Abstract

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. 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.

Keywords: KAI1/CD82; biomarker; metabolomics; nude mouse models; pancreatic cancer.

Figure 1.

Lentivirus packaging and nude mouse xenograft models. (a) Polymerase chain reaction (PCR) identification results of KAI1/CD₈₂ PLV-EF1α-positive bacterial solution. (b) Efficiency of PANC1 cell lentivirus infection. (c and d) Western blot of KAI1/CD₈₂ protein expression. (e) Three PC tissues of nude mouse models. (f) Three liver metastasis tissues of nude mouse models.

Figure 2.

Flow chart of metabolomic analysis.

Figure 3.

Principal component analysis (PCA) was performed on the samples (including the quality control samples) in order to preliminarily understand the total metabolic differences between the samples of each group and the degree of variation between the samples within the group. (a) PCA 2D results of grouped metabolites. (b) PCA 3D results of grouped metabolites. (c) Explainable variation in the first 5 principal components of PCA. PCA showed a distinct clustering between groups.

Figure 4.

OPLS-DA score plot. (a) OPLS-DA score plot. The abscissa is the difference between groups. The ordinate represents the score value of orthogonal components, and the ordinate direction shows the difference within the group. (b) OPLS-DA model validation. The prediction parameters of the evaluation model include R²X, R²Y, and Q². R²X and R²Y, respectively, represent the interpretation rate of the established model to the X and Y matrix, and Q² represents the prediction ability of the model. The closer these 3 indicators are to 1, the more stable and reliable the model will be.

Figure 5.

(a) VIP value plot. VIP values of the differentially expressed metabolites. (b) Bar chart. The difference multiple changes of the quantitative information of metabolites in each group were compared according to the grouping of specific samples.

Figure 6.

(a) OPLS-DA S-plot. The red points indicate that the VIP value of these metabolites ≥1; the green points indicate that the VIP value of these metabolites ≤1. The upper right corner or lower left corner metabolites have more significant difference. (b) Volcano plot. Each point in the map represents a metabolite, and the abscisic coordinate represents the logarithm of the quantitative difference multiples of a metabolite in the 2 samples. (c) Violin plot. Violin plot is a combination of boxplot and density map and is mainly used to display the data distribution and its probability density. (d) Z-score plot. The different metabolites in different samples were normalized by calculating the Z-value. The distribution of each differential metabolite between different groups can be distinguished visually. (e). Heatmap clustering. The clustering tree on the left of the figure represents differential metabolites, and the scale represents the expression amount obtained after standardized processing.

Figure 7.

Enrichment analysis. (a) Statistics of KEGG enrichment. (b) Statistics of HMDB enrichment. Rich factor is the ratio between the number of differentially expressed metabolites in the corresponding pathway and the total number of metabolites detected and annotated in the pathway. The size of the points in the figure represents the number of significantly different metabolites enriched to the corresponding pathways.

Figure 8.

Kyoto encyclopedia of genes and genomes (KEGG) annotation of bile secretion.

Figure 9.

Kyoto encyclopedia of genes and genomes (KEGG) annotation of primary bile acid biosynthesis.

Figure 10.

Kyoto encyclopedia of genes and genomes (KEGG) annotation of purine metabolism.