Brain Cancer

Brain cancer can be life-threatening due to the changes it causes to the vital structures of the brain. Common types include astrocytomas, oligodendrogliomas, glioblastomas and mixed gliomas. Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. A large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets.The systems biology approaches used in brain cancers include bioinformatics and mathematical modeling. Bioinformatics has been used for identifying the molecular mechanisms driving brain metastasis and mathematical modeling methods for analyzing dynamics of a system and predicting optimal therapeutic strategies.

Multi-omics Datasets Resource

The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/)
Genotype-Tissue Expression Portal (GTEx, https://gtexportal.org/home/)
Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo)
Ivy Glioblastoma Atlas Project (Ivy GAP, http://glioblastoma.alleninstitute.org)
Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn)

Computational Approaches

• Collect a large-scale assemble of multi-omics datasets.
• Comprehensive multi-omics molecular profiling across different datasets.
• Identification of brain-specific genes and statistical analysis.

Computational integrative multi-omics data analysis contributes to oncology research, representing a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.

Brain Tumor Models

Data-driven computational brain tumor models are quantitative and cost-efficient tools to generate and test hypotheses on tumor progression, and to infer fundamental operating principles governing bidirectional signal propagation in multicellular cancer systems. In silico brain tumor models can be constructed by two distinct computational approaches: discrete and continuum. Hybrid multiscale multiresolution modeling has been developed in the integrative computational neuro-oncology field.

Why choose Us?

CD ComputaBio utilizes advanced biostatistical approaches and computational analysis methods to interpret multi-omics data. We are also interested in how the machine learning-based integration of different datasets can aid in the discovery of new cancer subgroups and biomarkers. In addition, we have multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.

References

1. Lin Liu, et al. Computational genomics of brain tumors: identification and characterization of glioma candidate biomarkers through multi-omics integrative molecular profiling. bioRxiv. 2020.
2. Wang, Z., Deisboeck, T.S. Computational modeling of brain tumors: discrete, continuum or hybrid?. Scientific Modeling and Simulations. 2008.