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

Significance of the Topic

Hepatocellular carcinoma (HCC) ranks among the top causes of cancer-related mortality worldwide. Early detection and effective intervention remain major clinical challenges. Profiling plasma metabolites through advanced gas chromatography techniques offers a promising route to uncover novel biomarkers for HCC.

Objectives and Study Overview

This study aimed to support a broader investigation of metabolite differences between HCC and liver cirrhosis (CIRR) patients. A non-targeted, multiplatform workflow combining one-dimensional GC-TOFMS and comprehensive two-dimensional GCxGC-TOFMS was applied to identify potential HCC biomarkers in human plasma.

Methodology and Instrumental Setup

Sixty-four plasma samples from HCC and CIRR patients underwent metabolite extraction using an acetonitrile/isopropanol/water mixture, followed by a two-step derivatization: methoximation at 60 °C for 30 minutes and silylation at 60 °C for 30 minutes. Instrumental details:

• Gas Chromatograph: LECO GCxGC with dual-stage quad jet thermal modulator
• Columns: Rxi-5ms (30 m × 0.25 mm × 0.25 µm) primary; Rxi-17sil ms (0.6 m × 0.25 mm × 0.25 µm) secondary
• Autosampler: LECO L-PAL 3; Injection: 1 µL split 20:1 at 250 °C
• Carrier Gas: Helium at 1.4 mL/min constant flow
• Temperature Program: 50 °C (1 min), ramp 10 °C/min to 300 °C (12 min); secondary oven +5 °C offset
• Modulation: 3 s period, 15 °C offset on modulator
• Mass Spectrometer: LECO Pegasus BT, EI ionization, 45–750 m/z range, 10 spectra/s (200 spectra/s for GCxGC)

Main Results and Discussion

GC-TOFMS generated an initial profile of plasma metabolites. Transitioning to GCxGC-TOFMS markedly increased chromatographic resolution and signal-to-noise ratios, enabling detection of co-eluting compounds such as myo-inositol and uric acid. Untargeted peak finding revealed hundreds of features. Target Analyte Finding (TAF) on the GCxGC data identified 29 metabolites with statistically significant differences (p<0.05, FDR<5%) between HCC and CIRR groups, validated through spectral similarity scores and mass delta calculations.

Benefits and Practical Applications

• Comprehensive two-dimensional chromatography enhances detection of low-abundance metabolites.
• Non-targeted multiplatform analysis uncovers novel biomarker candidates without prior assumptions.
• Statistical filtering streamlines selection of significant metabolites for clinical validation.
• Approach adaptable to QA/QC and broader clinical metabolomic studies.

Future Trends and Applications

Integration of high-resolution MS, ion mobility separation, and machine learning–driven data analytics will further deepen metabolomic coverage and biomarker discovery. Advances in automation and miniaturization may enable high-throughput, point-of-care metabolic profiling in clinical settings.

Conclusion

The combined GC- and GCxGC-TOFMS workflow substantially expanded metabolic profiling in HCC plasma, yielding a set of candidate biomarkers with robust statistical support. This methodology demonstrates the value of two-dimensional GC in clinical metabolomics and holds promise for improving early detection of hepatocellular carcinoma.