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. 2020 Nov 6;64(4):581-588.
doi: 10.2478/jvetres-2020-0070. eCollection 2020 Dec.

Serum Metabolomic Analysis of Feline Mammary Carcinomas based on LC-MS and MRM Techniques

Jia-San Zheng  1 , Ren-Yue Wei  1 , Zheng Wang  1 , Jun Song  1 , Yan-Song Ge  1 , Rui Wu  1
Affiliations

Serum Metabolomic Analysis of Feline Mammary Carcinomas based on LC-MS and MRM Techniques

Jia-San Zheng et al. J Vet Res. .

Abstract

Introduction: To date, there have been no panoramic studies of the serum metabolome in feline mammary carcinoma. As the first such study, metabolomics techniques were used to analyse the serum of cats with these tumours. Three important metabolic pathways of screened differential metabolites closely related to feline mammary carcinomas were analysed to lay a theoretical basis for further study of the pathogenesis of these carcinomas.

Material and methods: Blood in a 5-8 mL volume was sampled from twelve cats of the same breed and similar age (close to nine years on average). Six were feline mammary carcinoma patients and six were healthy. L glutamate, L alanine, succinate, adenine, hypoxanthine, and inosine were screened as were alanine, aspartate, and glutamate metabolism, the tricarboxylid acid (TCA) cycle, and purine metabolism. Data were acquired with LC-MS non-target metabolomics, multiple reaction monitoring target metabolomics, and multivariate statistical and bioinformatic analysis.

Results: Expression of five of the metabolites was upregulated and only inosine expression was downregulated. Up- and downregulation of metabolites related to glycometabolism, potentiation of the TCA cycle, greater content of lipid mobilisation metabolites, and abnormality of amino acid metabolism were closely related to the occurrence of the carcinomas.

Conclusion: These findings provide a new direction for further study of the mechanisms associated with cat mammary neoplasms.

Keywords: LC-MS; cats; feline mammary carcinomas; metabolomics; multiple reaction monitoring (MRM).

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Conflict of interest statement

Conflict of Interest Conflict of Interests Statement: The authors declare that there is no conflict of interests regarding the publication of this article.

Figures

Fig. 1
Fig. 1
PCA score diagram in NEG mode and POS mode In the figure, the ellipse represents the 95% confidence interval, and the abscissa PC [1] and ordinate PC [2] represent the score of the principal component ranking first and second, respectively. The scatter shapes and colours represent the different experimental groups
Fig. 2
Fig. 2
OPLS-DA score in the NEG mode and POS mode In the figure, the ellipse represents the 95% confidence interval, and the abscissa t[1]P represents the predicted principal component score of the first principal component. The ordinate t[1]O represents the orthogonal principal component score, and the scatter shapes and colours represent the different experimental groups
Fig. 3
Fig. 3
Volcano map of NEG (A) and POS (B) modes differential metabolite screening The abscissa represents the fold-change of the group comparing each substance (taking the logarithm base 2). The ordinate represents the P-value of the Student’s t-test (taking the negative logarithm base 10)
Fig. 4
Fig. 4
HCA analysis results in NEG (A) and POS (B) modes
Fig. 5
Fig. 5
Bubble diagram of the metabolic pathway analysis in NEG and POS mode
Fig. 6
Fig. 6
Interaction network diagram of six biomarker differential metabolites. Red indicates that the metabolites are up regulated in group T, blue indicates that the metabolite is down regulated in group T, yellow indicates metabolic pathways, and metabolites without a coloured background are the upstream and downstream substances that are differentially metabolised
See this image and copyright information in PMC

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