The Discovery of Potential Cancer Biomarkers in Human Plasma Using GC-and GCxGC-TOFMS

Significance of the Topic

Hepatocellular carcinoma (HCC) ranks among the leading causes of cancer mortality worldwide, highlighting the urgent need for early detection strategies. Plasma-based biomarkers offer a minimally invasive means to diagnose HCC at treatable stages.

Objectives and Study Overview

This investigation aimed to identify potential HCC biomarkers by comparing metabolite profiles in plasma from 64 patients with HCC and liver cirrhosis (CIRR). Employing a non-targeted, multiplatform approach combined one-dimensional GC-TOFMS with comprehensive two-dimensional GC×GC-TOFMS to broaden metabolite coverage.

Methodology

Samples underwent protein precipitation and metabolite extraction using an acetonitrile/isopropanol/water mixture. A two-step derivatization included methoximation (60 °C, 30 min) followed by silylation (60 °C, 30 min). Untargeted peak finding algorithms detected features across the full m/z range (45–750). Target Analyte Finding (TAF) generated retention time and area matrices for selected compounds. Statistical filtering (p < 0.05, FDR < 5%) isolated significant differences between HCC and CIRR groups.

Used Instrumentation

• LECO GC×GC system with dual-stage quad-jet thermal modulation
• LECO Pegasus BT 4D time-of-flight mass spectrometer
• LECO L-PAL 3 autosampler
• Rxi-5 ms primary column (30 m × 0.25 mm ID × 0.25 µm)
• Rxi-17sil ms secondary column (0.6 m × 0.25 mm ID × 0.25 µm)

Results and Discussion

One-dimensional GC-TOFMS demonstrated broad metabolic diversity, while GC×GC-TOFMS significantly enhanced chromatographic resolution, signal-to-noise ratios, and spectral matching. Untargeted processing identified hundreds of features; TAF refined this list to targeted metabolites with precise two-dimensional retention times. Statistical analysis revealed 29 metabolites that differed significantly between HCC and cirrhotic patients, suggesting their potential as HCC biomarkers.

Practical Implications

The superior separation and detection capabilities of GC×GC-TOFMS improve the reliability of biomarker discovery workflows. This methodology can be implemented in clinical and industrial laboratories for early disease screening, therapeutic monitoring, and quality control.

Future Trends and Applications

Future developments will likely focus on integrating GC×GC-TOFMS data with genomics and proteomics to build multi-omics biomarker panels. Advances in machine learning and automated data analysis will accelerate feature selection and validation. Expanding cohort sizes and standardizing protocols will be key for clinical translation.

Conclusion

The combination of untargeted GC-TOFMS and comprehensive GC×GC-TOFMS, supported by rigorous derivatization and data-processing workflows, effectively distinguished plasma metabolite differences between HCC and cirrhosis cohorts. This approach paves the way for validated plasma biomarkers to facilitate early HCC diagnosis and personalized treatment strategies.

Reference

• NIH grant U01CA185188
• NIH grant R01GM123766