Clinical Application of Metabolomics in Pancreatic Diseases

A Mini-Review

Wang Gu, MS; Zhong Tong, MS


Lab Med. 2020;51(2):116-121. 

In This Article

The Study of Metabolomics in Early Diagnosis of PC

PC cells exist in an adverse local environment, surrounded by dense connective tissue and poor angiogenesis. This hypoxic and nutrition-deficient microenvironment, along with the signature Kras oncogene mutation, reprograms PC cell metabolism and promotes tumor growth.[45–49] Thus, cancer cells have metabolic needs that are different from those of normally differentiated cells and exhibit metabolic changes to support abnormal cell division.[50] As a result of these metabolic changes, specific metabolites have been identified to support cancer growth, and the growth of some cancer cells has been shown[45,51,52] to depend on specific metabolic pathways.

There are some studies in the literature on the metabonomics of PC, in which NMR spectroscopy for noninvasive and repeatable metabolic signal recognition has served as a useful tool. For instance, Smith et al[53] used 1H NMR spectroscopy to detect and quantify D-glucuronic acid in human bile. The glucuronic acid (GlcA) content indicated that patients with PC have a greater amount of d-Portuguese hyaluronic acid in their bile specimens, compared with the control group and patients with CP. Although the collection of bile specimens is invasive, bringing a great deal of inconvenience to diagnosis, this method may yield a novel point of view that is useful in approaching diagnosis.

The results of studies by other researchers, such as Zhang et al,[54] have used 1H NMR spectroscopy combined with multivariate statistical analysis to study the metabolic changes induced by PC. The results showed that some metabolites, such as GlcA, could be used in the early diagnosis and differential diagnosis of PC. However, further confirmatory studies are needed to confirm these findings before they can be transferred from laboratories to clinics.

Kobayashi et al[55] developed a serum metabolomics-based diagnostic model for PC using multiple logistic regression analysis. Although no single biomarker can characterize PC, the specific metabolite combination obtained in the experiment can be used as a marker for the diagnosis of PC. The model by Kobayashi et al is more accurate than traditional tumor markers, especially in tumor detection of patients who have undergone resection. This novel diagnostic method is expected to improve the prognosis of patients with PC by detecting early cancer when it is still curable. And I have compared NMR with LC-MS in Table 1