TARGETED MULTI-OMICS: RAPID PLASMA PROFILING OF A BLADDER AND LUNG CANCER HUMAN COHORT

Significance of the Topic

The molecular complexity of cancer demands comprehensive profiling approaches to uncover biomarkers and pathways involved in disease onset and progression. Rapid plasma multi-omics combines lipidomics, metabolomics and proteomics to reveal disease signatures in a minimally invasive manner. Such workflows support early detection, patient stratification and therapeutic monitoring in lung and bladder carcinoma.

Aims and Study Overview

This study applied the MetaboQuan-R high-throughput platform to analyze plasma samples from human cohorts with lung and bladder cancers alongside healthy controls. The objective was to identify statistically significant changes in acylcarnitines, amino acids, bile acids and proteins that distinguish cancer patients from controls, and to demonstrate the platform’s speed and reproducibility.

Methodology and Instrumentation

The workflow utilized targeted assays for acylcarnitines, amino acids, bile acids and proteins, achieving sample analysis in three minutes each. Quality control involved pooled samples to assess precision, with peak area coefficient of variation below 10% across assays. Data acquisition used the MetaboQuan-R platform (Waters Corporation) coupled to mass spectrometry. Statistical evaluation included ANOVA, t-tests with false discovery rate control, fold-change thresholds and principal component analysis for multivariate separation.

Main Results and Discussion

• Acylcarnitines: Nine species in lung and eight in bladder cancer showed significant elevation or alteration over a linear concentration range of 4.8 to 625 ng/mL. Notable examples included C14:2, C8:1 and C16:1, with lung cancer samples displaying higher abundance compared to controls.
• Bile Acids: Lung cancer patients exhibited a general down-regulation of bile acids, with only deoxycholic acid and taurochenodeoxycholic acid significantly increased relative to controls (156 to 2500 ng/mL range).
• Proteins: Seventy-three proteins were quantified. Multivariate analysis separated healthy and cancer cohorts, and further distinguished histological subtypes within lung cancer. Ten proteins met stringent p-value and fold-change criteria; examples include alpha-1-acid glycoprotein 2, hemoglobin subunit beta and leucine-rich alpha-2-glycoprotein. In bladder cancer, most proteins were overexpressed, except coagulation factor XII A chain, which was downregulated.

Benefits and Practical Applications

The MetaboQuan-R single-platform approach offers high sensitivity, rapid turnaround and robust reproducibility, enabling large-scale plasma profiling. It supports biomarker discovery, disease monitoring and may be integrated into quality control workflows in clinical and pharmaceutical laboratories.

Future Trends and Applications

Advancements may include integration with additional omics layers, automated sample handling and machine learning for pattern recognition. Scaling to larger, diverse cohorts will enhance biomarker validation. Ultimately, personalized multi-omic signatures could guide tailored therapies and real-time disease monitoring.

Conclusion

The MetaboQuan-R multi-omics workflow demonstrated fast, reliable profiling of plasma from lung and bladder cancer cohorts. Statistically significant changes in lipidomic, metabolomic and proteomic markers were identified, illustrating the method’s potential for cancer biomarker research and clinical application.

Reference

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