High-throughput lipidomics reveal mirabilite regulating lipid metabolism as anticancer therapeutics

Abstract

Altered lipid metabolism is an emerging hallmark of cancers. Mirabilite has a therapeutic effect on colorectal cancer (CRC); however, its metabolic mechanism remains unclear. This study aims to explore the potential therapeutic targets of mirabilite protection against colorectal cancer in APC^min/+ mice model. Oral administration of mirabilite was started from the ninth month, while the same dosage of distilled water was given to both the control group and the model group. Based on lipidomics, we collected serum samples of all mice at the 20^th week and used a non-targeted method to identify the lipid biomarkers of CRC. Compared with C57BL/6J mice, the metabolic profile of CRC model mice was significantly disturbed, and we identified that 25 lipid-related biomarkers, including linoleic acid, 2-hydroxybutyric acid, 6-deoxocastasterone, hypoxanthine, PC(16:1), PC(18:4), and retinyl acetate, were associated with CRC. According to the abovementioned results, there were six lipid molecules with significant differences that can be used as new targets for handling of CRC through six metabolic pathways, namely, linoleic acid metabolism, retinol metabolism, propanoate metabolism, arachidonic acid metabolism, biosynthesis of unsaturated fatty acids and purine metabolism. Compared with the model group, the metabolic profiles of these disorders tend to recover after treatment. These results indicated that the lipid molecules associated with CRC were regulated by mirabilite. In addition, we identified seven key lipid molecules, of which four had statistical significance. After administration of mirabilite, all disordered metabolic pathways showed different degrees of regulation. In conclusion, high-throughput lipidomics approach revealed mirabilite regulating the altered lipid metabolism as anticancer therapeutics.

Fig. 1. Data processing workflow of high-throughput lipidomics revealing mirabilite regulating lipid metabolism.

Fig. 2. The histopathological test of H&E stained intestinal segment in the CON (A), MOD (B), and Mirabilite-treated group (C).

Fig. 3. Serum BPI chromatograms of CON and MOD in positive ion mode (A and B) and negative ion mode (C and D).

Fig. 4. Score plot of PCA in serum metabolism profile of control and model groups. ((A and B) 2D and 3D positive ion mode in PCA analysis; (C and D) 2D and 3D negative ion mode in PCA analysis).

Fig. 5. Score plot of OPLS-DA analysis in serum metabolism profile of control and model groups ((A) positive ion mode in OPLS-DA analysis, (B) negative ion mode in OPLS-DA analysis). VIP plots of control and model groups analyzed by OPLS-DA. ((C) Positive ion mode in OPLS-DA analysis, (D) negative ion mode in OPLS-DA analysis).

Fig. 6. Heat map showed relationship of lipid compounds in serum samples from control, model and mirabilite-treated groups.

Fig. 7. Differences of twenty-five potential lipid biomarkers in the serum of CON, MOD and mirabilite-treated groups. (Compared with CON, “*” was p < 0.05, “**” was p < 0.01; compared with MOD, “#” was p < 0.05, “##*” was p < 0.01.)

Fig. 8. The relational pathways associated with the biomarkers that were an abnormal expression in MOD. (a) Linoleic acid metabolism; (b) retinol metabolism; (c) propanoate metabolism; (d) arachidonic acid metabolism; (e) biosynthesis of unsaturated fatty acids; (f) purine metabolism.

Fig. 9. The PLS-DA score plot of control, model and mirabilite-treated groups in serum metabolism profile ((A) positive ion mode, (B) negative ion mode).