Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020 (WHO). The most common causes of cancer death in 2020 were:

  • lung (1.80 million deaths);
  • colon and rectum (916 000 deaths);
  • liver (830 000 deaths);
  • stomach (769 000 deaths); and.
  • breast (685 000 deaths).

The cancer burden can be reduced through early detection of cancer and appropriate treatment and care of patients who develop cancer. Many cancers have a high chance of cure if diagnosed early and treated appropriately.  However, 70% to 80% of cancer patients in low- and lower-middle-income countries were diagnosed at the terminal stage.

In term biology, cancer is a metabolic disease (Seyfried et al., 2014). Cancer processes share a common phenotype of uncontrolled cell proliferation and disease-specific changes by significant modifications in various metabolic processes (glycolysis, TCA cycles, oxidative phosphorylation, etc.) as well as lipid and amino acid metabolism. Today, cancer treatment decisions are based on the clinical stage of the disease but do not identify the basic biological individual and its role in the treatment of malignancy. The lack of sufficient data regarding the characteristics of specific biochemical markers related to each specific cancer patient or group of patients is a major limitation of cancer treatments.

In this context, metabolomics promise to provide a new avenue for cancer research, diagnosis and treatment because the chemical entities can reflect the cellular and microenvironment of cancer. Through a comprehensive analysis of biological fluids or tissue samples, the metabolic phenotype allows a person’s biochemical classification of physiological or pathological states and can be extremely helpful in stratification of patients. On the other hand, identifying metabolic reprogramming events or metabolic subgroups in cancer patients is likely to inform clinicians about factors that will enhance diagnosis, prognosis, or therapeutic options (Nicholson, J.K.; Holmes, E.; Kinross, J.M.; Darzi, A.W.; Takats, Z.; Lindon, 2012).

Moreover, metabolomics is a more convenient, less damaged and easier than traditional detection methods that can be invasive or radiant in detecting disease (Patti GJ, Yanes O, 2012). Metabolomics samples can be obtained in a variety of ways. The types of metabolomics are also extensive, such as blood, urine, saliva and tissue. Metabolites extracted from different clinical samples may reveal abnormal situations or diseases in the body (Chen et al., 2019).

Currently, metabolomics research is being used wildly to discover diagnostic cancer biomarkers in the clinic, to better understand its complex heterogeneous nature, to discover pathways involved in cancer that could be used for new targets and to monitor metabolic biomarkers during therapeutic intervention. These metabolomics approaches may also provide clues to personalized cancer treatments by providing useful information to the clinician about the cancer patient’s response to medical interventions (Beger et al., 2013).

Our experts have a highly qualified professional NMR metabolomics, biomarker development, pathogenic model building and statistical analysis with many years of excellent experience. Our major focus areas include the development of diagnostic biomarkers on various medical studies, including  liver disease (Nguyen et al., 2021), liver cancer, endometrial cancer (Lund et al., 2020), Covid-19, pancreatic cancer, and healthy subjects. We are looking forward to collaborator with the partners and customers to solve the challenging problems in medical science such as cancer problems.


Chen, Z., Li, Z., Li, H., Jiang, Y., 2019. Metabolomics: A promising diagnostic and therapeutic implement for breast cancer. Onco. Targets. Ther. 12, 6797–6811.

Nicholson, J.K.; Holmes, E.; Kinross, J.M.; Darzi, A.W.; Takats, Z.; Lindon, J.C., 2012. Metabolic phenotyping in clinical and surgical environments. Nature 491, 384–392.

Patti GJ, Yanes O, S.G., 2012. Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13, 263–269.

Seyfried, T.N., Flores, R.E., Poff, A.M., D’Agostino, D.P., 2014. Cancer as a metabolic disease: Implications for novel therapeutics. Carcinogenesis 35, 515–527.

Beger, R.D., 2013. A Review of Applications of Metabolomics in Cancer. Metabolites 3, 552-574; doi:10.3390/metabo3030552

Lunde, S., Nguyen, H.T.T., Petersen, K.K., Arendt-Nielsen, L., Krarup, H.B., Søgaard-Andersen, E., 2020. Chronic Postoperative Pain After Hysterectomy for Endometrial Cancer: A Metabolic Profiling Study. Mol. Pain 16.

Nguyen, H.T.T., Wimmer, R., Le, V.Q., Krarup, H.B., 2020. Metabolic fingerprint of progression of chronic hepatitis B – changes in the metabolome and novel diagnostic possibilities. Metabolomics.

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