Application of omics techniques on various health endpoints, including prostate cancer, bone health, and liver disease are listed below:
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- Prostates cancer, Metabolomics: Evaluating Differential Metabolic Profile by Prostate Cancer Grade among Prostate Cancer Patients
- Conducted metabolomics analysis on 22 urine samples from prostate cancer patients who were disgnozed with prostate cancer
- Developed and validated statistical models to compare metabolic profiles across high vs. low PCa risk group
- Lead author for a manuscript (submitted to Metabolites) (Liu et al., 2025)
- Bone Health, Metabolomics: TBD
- Multi-cohort spanning from childhood, adolescence, to young adulthood
- Analyzed 304 plasma samples for BMD-related metabolic signals
- Integrated metabolomics datasets for comparative analysis
- Lead author for a manuscript (under development)
- Liver Disease, Exposomics: TBD
References
2025
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Evaluating Differential Metabolic Profile by Prostate Cancer Risk among Prostate Cancer Patients
Tuo Liu, Jahnvi Roorkeewal, Melissa Furlong, and 6 more authors
Metabolites, 2025
Background: Currently there are no clinically validated biomarkers recommended for prostate cancer (PCa) risk stratification other than prostate specific antigen (PSA). Objectives: This study aimed to identify urine metabolites that are associated with presence of high-grade PCa at the time of radical prostatectomy. Methods: Urine samples were collected from patients who underwent radical prostatectomy. High-resolution metabolomics were implemented using mass liquid-chromatography mass spectrometry (LC-MS). To enhance metabolic feature identification, sample extracts were analyzed in two modes, a C18 chromatography [reverse-phase (RP)] and a hydrophilic interaction chromatography (HILIC). Results: This analysis included a total of 22 patients with PCa (10 high-grade and 12 low-grade) and identified 52 differential metabolites, 40 in RP and 12 in HILIC, at p-value 0.05 level. Among these, methyl alpha-aspartyl phenylalaninate was most significantly differentiated, while 3-methylbutanoicacid had the largest difference (slope -3.488). In pathway analysis, the histidine metabolic pathway was significantly enriched with an enrichment factor of 3.5. Our research also identified alterations in vitamins B12, B7 (biotin), B6, and B3 (niacin) pathways. Conclusion: Urine metabolites have the potential to differentiate high-grade from low-grade PCa. Our study also highlights the metabolic reprogramming that occurs as PCa becomes more aggressive.
