Integrated Transcriptomics and Metabolomics Study of Retinoblastoma Using Agilent Microarrays and LC/MS/GC/MS Platforms

Significance of the Topic

Multi-omics profiling combines transcriptomic and metabolomic data to reveal complex molecular mechanisms in disease. In retinoblastoma, integrating gene expression patterns and metabolic changes can uncover pathways driving tumor initiation and progression, identify potential biomarkers, and suggest new targets for therapy.

Objectives and Study Overview

This study aimed to apply a unified Agilent multi-omics workflow to retinoblastoma tissues and associated biofluids. Key goals included:

• Profiling mRNA and miRNA expression in tumor versus control retina samples using Agilent microarrays.
• Analyzing metabolites in aqueous humor, vitreous humor, and tears via LC/MS and GC/MS.
• Performing pathway-centric integration to identify altered biological routes in tumor samples.

Methodology

Tissue samples (nine retinoblastoma, two controls) underwent RNA extraction, quality assessment (Agilent TapeStation), one-color labeling, and hybridization on SurePrint G3 Human microarrays. Biofluids were processed by monophasic extraction (50:50 methanol:ethanol) and subjected to:

• LC/Q-TOF MS on C18 and HILIC columns (positive/negative ESI; 50–1200 m/z).
• GC/Q-TOF MS after derivatization (Agilent Fiehn standards; EI mode; DB-5ms column).

Feature extraction and normalization used Agilent MassHunter, GeneSpring GX, and Mass Profiler Professional. Differential entities (p≤0.05, fold change≥2) were mapped to KEGG pathways and visualized in GeneSpring’s Pathway Architect.

Instrumentation Used

• Agilent SurePrint G3 Human GE and miRNA microarrays with SureScan scanner
• Agilent 7200 GC/Q-TOF and 6550 iFunnel Q-TOF LC/MS systems
• Agilent 1290 Infinity II LC system
• Agilent MassHunter Qualitative, Profinder, GeneSpring GX and Mass Profiler Professional software

Key Results and Discussion

Transcriptomics revealed ~1,600 genes and 18 miRNAs significantly dysregulated in tumors. Metabolomics identified over 1,300 annotated compounds across biofluids. Integrated analysis highlighted:

• Down-regulation of valine, leucine, and isoleucine biosynthesis, linked to reduced BCAT1 expression and elevated valine levels in aqueous humor.
• Altered purine metabolism in tears, with decreased inosine and uric acid corroborated by MS/MS spectral matching.
• Disruption of fructose, mannose, starch and sucrose pathways in tear fluid.

These findings point to novel metabolic vulnerabilities and confirm coordination between gene expression and metabolite profiles.

Benefits and Practical Applications

• Enhanced mechanistic insight into retinoblastoma biology through complementary omics layers.
• Identification of potential metabolic biomarkers in accessible biofluids.
• Framework for discovery-driven research using standardized Agilent multi-omics tools.

Future Trends and Opportunities

• Extension to single-cell and spatial multi-omics for intratumoral heterogeneity.
• Integration with proteomics and lipidomics to capture additional pathway alterations.
• Development of predictive models and targeted therapies based on identified metabolic dependencies.
• Application of AI-driven network analysis to refine biomarker panels.

Conclusion

This multi-omics study demonstrates how combining Agilent transcriptomics and metabolomics platforms uncovers novel pathways in retinoblastoma. The coordinated dysregulation of amino-acid and purine metabolism suggests new avenues for biomarker development and therapeutic intervention. The unified workflow supports reproducible discovery in oncology research.

References

1. Dweep H, Gretz N. miRWalk2.0: a comprehensive atlas of microRNA–target interactions. Nat Methods. 2015;12(8):697–698.
2. Palazoglu M, Fiehn O. Metabolite identification in blood plasma using GC/MS and the Agilent Fiehn GC/MS metabolomics RTL library. Agilent Technologies Application Note. 2009;5990-3638EN.