Multi-omics Tools in Cancer Research
Introduction: The Era of Integrated Biology
In recent years, cancer research has shifted from studying single genes to exploring the complex networks that drive disease. Multi-omics, the integration of genomics, transcriptomics, proteomics, metabolomics, and epigenomics, allows scientists to see cancer as a connected system rather than isolated mutations.
By merging these data layers, researchers can identify new biomarkers, understand therapy resistance, and design precision treatments that truly match a patient’s molecular profile.
Why Multi-omics Matters
Traditional cancer research often focused on one “omics” layer, for example, analyzing gene mutations alone. However, tumors evolve through intricate interactions between DNA, RNA, proteins, and metabolites.
Multi-omics tools combine these insights, giving a holistic view of how a tumor develops, spreads, and responds to therapy. This systems-level understanding is transforming early diagnosis and precision oncology.
For example, bioinformatics-driven platforms such as UALCAN, GEPIA, and TIMER2 enable large-scale analysis of gene expression, methylation, and immune infiltration patterns across cancers, insights that were previously impossible to obtain at this scale (Chandrashekar et al., 2017; Tang et al., 2017; Li et al., 2020).
Case Example: Uncovering the Role of GNB2 in Cancer
In our recent multi-omics study published in the Brazilian Journal of Biology (Zhang, Sahar, Li, et al., 2024), we applied a combination of transcriptomic, proteomic, and epigenetic analyses to explore the role of the GNB2 gene across multiple cancer types.
The results were striking, GNB2 was significantly upregulated in 23 human cancers and closely linked with poor overall survival in Liver Hepatocellular Carcinoma (LIHC) and Rectum Adenocarcinoma (READ) patients (Zhang et al., 2024).
We further observed that promoter hypomethylation, a key epigenetic alteration was associated with the elevated expression of GNB2, highlighting how DNA methylation changes can drive tumor progression (Zhang et al., 2024).
Such findings emphasize the power of integrating multiple omics layers to decode complex molecular mechanisms in cancer.
Powerful Tools Behind Multi-omics Discoveries
Modern cancer biology depends on computational platforms and visualization tools such as:
UALCAN – for gene expression and methylation analysis (Chandrashekar et al., 2017).
GEPIA – for validating mRNA expression using independent cohorts (Tang et al., 2017).
STRING & Cytoscape – for protein–protein interaction and pathway mapping (Zhang et al., 2024).
TIMER2 – for immune microenvironment correlation (Li et al., 2020).
DAVID & KEGG – for functional and pathway enrichment studies (Zhang et al., 2024).
These tools collectively create a robust analytical framework, transforming raw genomic data into clinically actionable insights.
Implications for Future Cancer Research
Multi-omics approaches have the potential to:
Identify diagnostic and prognostic biomarkers (like GNB2) for various cancer types (Zhang et al., 2024).
Reveal therapeutic targets to improve personalized treatment strategies (Zhang et al., 2024; Tang et al., 2017).
Enhance understanding of tumor–immune interactions, supporting the next generation of immunotherapies (Li et al., 2020).
However, integration and interpretation of vast omics datasets require strong bioinformatics expertise, standardized pipelines, and data-sharing initiatives to ensure reproducibility and translation into clinical practice.
Conclusion
Multi-omics tools are redefining cancer research by merging biological complexity with computational precision. Studies such as ours demonstrate that unraveling the molecular networks of genes such as GNB2 can guide early detection, improve treatment outcomes, and open new avenues for personalized medicine (Zhang et al., 2024).
As technology evolves, multi-omics will be at the heart of precision oncology, turning data into discoveries and discoveries into cures.
References
Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., & Varambally, S. (2017). UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia, 19(8), 649–658. https://doi.org/10.1016/j.neo.2017.05.002
Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., Li, B., & Liu, X. S. (2020). TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Research, 48(W1), W509–W514. https://doi.org/10.1093/nar/gkaa407
Tang, Z., Li, C., Kang, B., Gao, G., & Zhang, Z. (2017). GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Research, 45(W1), W98–W102. https://doi.org/10.1093/nar/gkx247
Zhang, L., Sahar, A. M., Li, C., & others. (2024). A detailed multi-omics analysis of GNB2 gene in human cancers. Brazilian Journal of Biology, 84, e260169. https://doi.org/10.1590/1519-6984.260169