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

Importance of the topic

The integration of transcriptomics and metabolomics provides a comprehensive view of molecular changes driving disease progression. In retinoblastoma, a pediatric eye tumor, combining gene expression profiling with metabolic analysis can uncover novel pathways and biomarkers, guiding diagnosis and therapy development.

Objectives and overview of the study

This study aimed to apply a multi-omics approach to retinoblastoma by:

• Profiling mRNA and miRNA expression in tumor and control tissues using microarrays.
• Analyzing metabolite profiles in aqueous humor, vitreous humor, and tears via LC/MS and GC/Q-TOF GC/MS.
• Integrating transcriptomic and metabolomic data to identify altered pathways in retinoblastoma.

Materials and methods

Samples from nine retinoblastoma and two control tissues were processed for total RNA (mRNA and miRNA) extraction. Gene expression used Agilent SurePrint G3 microarrays; data were normalized, filtered (p ≤ 0.05, fold change ≥ 2), and statistical analysis employed moderated t-test with Benjamini–Hochberg correction. Metabolites were extracted from ocular fluids by monophasic methanol:ethanol (1:1) with isotopic internal standards. LC/MS used an Agilent 1290 Infinity LC coupled to a 6550 iFunnel Q-TOF, employing both C18 and HILIC columns in positive/negative ESI; GC/MS relied on an Agilent 7200 GC/Q-TOF with DB-5MS column after derivatization (Fiehn kit). Data processing utilized Agilent MassHunter Qualitative, Profinder, Mass Profiler Professional, GeneSpring GX, and METLIN/Fiehn libraries. miRNA targets were predicted via TargetScan and validated with miRWalk.

Instrumentation

• Agilent SureScan Microarray Scanner
• Agilent 2100 Bioanalyzer and 2200 TapeStation
• Agilent 1290 Infinity LC System with 6550 iFunnel Q-TOF
• Agilent 7200 GC/Q-TOF and 7890C GC System
• Agilent GeneSpring GX, MassHunter, Profinder, and Mass Profiler Professional software

Key results and discussion

• Transcriptomics: ~1,600 genes were differentially expressed (FC ≥ 10; p ≤ 0.05), including down-regulation in the phototransduction pathway.
• miRNA: 18 miRNAs showed significant regulation; 12 of their predicted mRNA targets were differentially expressed.
• Metabolomics (GC/MS): Identified 32 differential metabolites (mainly carbohydrates, amino acids) in aqueous humor; LC/MS revealed decreased inosine and uric acid in tears.
• Multi-omics integration highlighted suppression of branched-chain amino acid biosynthesis (valine accumulation paired with reduced BCAT1 expression) and altered purine metabolism.

Benefits and practical applications

The combined Agilent workflow enables simultaneous exploration of gene and metabolite alterations, revealing novel disease mechanisms. This integrative strategy supports biomarker discovery, therapeutic target identification, and enhanced molecular characterization in research and clinical settings.

Future trends and potential uses

• Incorporation of proteomics and lipidomics for richer multi-layered insights.
• Single-cell and spatial omics to map tumor heterogeneity.
• Machine learning–driven pathway modeling for predictive diagnostics.
• Application in monitoring treatment response and personalized therapy design.

Conclusion

This study demonstrates an end-to-end Agilent multi-omics solution that integrates microarray and mass spectrometry data to uncover previously unrecognized pathways in retinoblastoma. The approach identified key metabolic and transcriptomic dysregulations, illustrating its value for comprehensive disease profiling.

References

1. Dweep H, et al. miRWalk - database: prediction of possible miRNA binding sites by “walking” the genes of 3 genomes. Journal of Biomedical Informatics. 2011;44(5):839–47.
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.