Rapid Molecular Profiling of a Bladder and Lung Cancer Human Plasma Cohort using MetaboQuan-R

Significance of the Topic

Cancer remains a leading cause of mortality worldwide, driving demand for rapid and comprehensive molecular profiling to improve early diagnosis, personalized treatment, and surveillance. Combining targeted protein, lipid, and metabolite analysis enables deeper insight into cancer biology and supports large-cohort studies with robust statistical power.

Study Objectives and Overview

This research applied the MetaboQuan-R workflow to semi-quantitatively profile plasma from bladder and lung cancer patients (n=6 each) alongside healthy controls (n=6). The aim was to demonstrate a unified high-throughput LC-MS approach for simultaneous detection of proteins, amino acids, and acylcarnitines without bespoke method development.

Methodology and Instrumentation

Sample preparation followed prebuilt protocols: proteins underwent reduction, alkylation and tryptic digestion with biognosys spike-in standards; amino acids were precipitated, derivatized using AccQ•Tag chemistry; acylcarnitines were extracted via methanol precipitation with labeled internal standards.
Targeted LC-MS methods were generated from Quanpedia MetaboQuan-R databases and executed on a single platform. Data acquisition employed multiple reaction monitoring and processing used Skyline and MetaboAnalyst for visualization and statistical analysis.

Used Instrumentation

• ACQUITY UPLC I-Class System
• CORTECS UPLC T3 Column (2.7 μm, 2.1×30 mm)
• Xevo TQ-S micro Triple Quadrupole Mass Spectrometer

Key Results and Discussion

• A total of 128 analytes (80 proteins, 28 amino acids, 20 acylcarnitines) were quantified with QC median CV below 10% (5%).
• Statistical analysis (t-test, p<0.05, fold change >2) identified 11 differentiating proteins in bladder cancer and 12 in lung cancer, including acute-phase glycoproteins and apolipoproteins.
• Seven amino acids (notably elevated sarcosine) and eight to nine acylcarnitines (including decreased C8:1 in bladder cancer) showed significant expression changes versus controls.
• These findings align with prior studies implicating altered energy metabolism in tumor growth, validating the platform’s capacity for multi-class biomarker discovery.

Benefits and Practical Applications

MetaboQuan-R offers a streamlined high-throughput solution for multi-omics profiling that requires no customized method development. Its semi-quantitative output and rapid turnaround are well suited to large-scale clinical and research cohorts, facilitating biomarker identification and hypothesis generation.

Future Trends and Opportunities

Integration of larger, diverse patient cohorts and orthogonal validation will enhance biomarker robustness. Extending the platform to additional metabolite classes and integrating advanced data analytics could accelerate personalized medicine efforts and deepen understanding of cancer metabolism.

Conclusion

The MetaboQuan-R workflow demonstrated efficient, precise molecular profiling of bladder and lung cancer plasma across multiple analyte classes within a single LC-MS platform. This approach supports high-throughput biomarker discovery and paves the way for broader application in oncology research.

References

1. International Agency for Research on Cancer. Cancer Today. 2018.
2. Hanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell. 2011.
3. Tsun ZY. Amino Acid Management in Cancer. Seminars in Cell & Developmental Biology. 2015.
4. Ganti S et al. Urinary Acylcarnitines Are Altered in Human Kidney Cancer. International Journal of Cancer. 2015.
5. Waters Corporation. MetaboQuan-R Method Manuals. 2019.
6. Chong J et al. MetaboAnalyst 4.0: Transparent and Integrative Metabolomics Analysis. Nucleic Acids Research. 2018.
7. Chen CL et al. Potential Bladder Cancer Markers in Urine by Proteomics. Journal of Proteomics. 2013.
8. Sreekumar A et al. Sarcosine in Prostate Cancer Progression. Nature. 2009.
9. Huang Z et al. Urinary Metabolites for Bladder Cancer Detection. Molecular & Cellular Proteomics. 2012.