Research and Applications of Non-target and Targeted Metabolomics in Urine Samples

Lung cancer mortality ranks first among all malignancies worldwide, and the survival rate is quite low. Currently, there are two methods for diagnosis, low-dose spiral CT (LDCT) (high false positive rate and radiation damage) and tumor tissue testing (gene mutation screening). There is no non-invasive clinical diagnosis mark to guide the treatment for now. The research paper (Non-invasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer, Cancer Research IF=9.3215) introduced today aims to find biomarkers for lung cancer diagnosis by performing metabolomics tests on urine.

There are three main reasons why this paper can get a good score, great samples, techniques and data analysis.

Samples Used in the Experiment

Pre-screening samples (1005 cases): samples collected in 10 years(1998~2007) from 469 patients (urine collected before the treatment) and 536 healthy people.

Samples for later validation (158 cases): Samples collected in 2008-2010 from 80 patients (urine collected before the treatment) and 78 healthy people.

Tissue samples: 48 tumor tissue samples and its surrounding non-tumor tissue samples 

Technical Workflow: initial non-targeted screening and post targeted MRM validation

Research results

Initial screening

Untargeted metabolomics analysis of pre-screened samples detected 1807 new numbers in positive ion mode and 1359 in negative ions. Through the detection of smoking-related metabolites (cotinine, nicotine-N'-oxide and trans-3'-hydroxycotinine), it was found that the smoking population was well separated from the non-smokers, and the feasibility of the analytical method was verified.

Data Analysis

Diagnostic and typing study: After applying statistical analysis to exclude human and gender interference, the authors identified four differential metabolites: NANA, cortisol sulfate, creatine riboside and 561+ (unidentified material). ROC analysis found that four differential metabolites had an AUC value between 0.63 and 0.76 in all populations and between 0.59 and 0.70 in stage I-II lung cancer patients. Among them, creatine riboside or all four metabolites were predicted to be more accurate (P<0.00001).

Overlap between signals that are predictive of lung cancer status in the training set based on the Random Forests classification.

Receiver Operating Characteristic (ROC) analysis of individual metabolites and their combination

Prognostic study: The authors found high levels of NANA after comprehensive consideration of gender, race, disease stage, tissue section, smoking history, radiotherapy and chemotherapy, surgery, etc.; cortisol sulfate. Creatine riboside and 561+ bring negative prognostic effect (low survival rate), and the correlation between these four metabolites and survival rate is independent but has an additive effect. In patients with stage I-II lung cancer, high levels of creatine riboside and 561+ will also reduce patients’ survival.

Post Validation

Similarly, using non-targeted metabolomics, it was found that creatine riboside, NANA and 561+ were significantly elevated in the urine of the tested population (80 patients and 78 healthy individuals). After exclusion of age, gender, and ethnic interference, 198 samples (92 patients and 106 healthy subjects) were selected for targeted metabolomics to validate the association of these four metabolites with disease. In addition, in order to prove the stability of these four metabolites, the authors repeated the experiments again after two years of storage, demonstrating their clinical value.

Extension of Mechanism Research

Finally, the authors tested the contents of the above four metabolites in tumor tissues and found that the content of creatine riboside and NANA in tumor tissues was significantly higher than that in adjacent tissues, and the content of creatine was also increased.

Conclusion

This paper is a classic paradigm biomarker research paradigm. Pre-targeted screening with post-targeting validation is worth learning. In addition, it finally returned to the tissue samples from the body fluid samples, which opened the door to the later mechanism research. This  research idea is also worthy of reference for clinical research. 

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