Astrocytoma

Astrocytoma is the type of glioma which is the tumor that arises from the supportive tissue or glial cell, of the  brain. Astrocytomas are originated from astrocytes that are star-shaped cells causing the tumor. According to the degree of abnormality, astrocytomas are graded into I to IV classes.

Astrocytoma Related Pathways

Significantly enriched pathways are PI3K-Akt, Cytokine-cytokine receptor, NODlike receptor, Jak-STAT, RIG-II-like receptor and Toll-like receptor pathways. HPV and herpex simplex infection and inflammation pathways are also represented. Present study brings new data to astrocytoma research amplifying the wide spectrum of changes which could help researchers identify the regions critical for tumorigenesis.

Datasets Resource and Computational Tools

The Cancer Genome Atlas (TCGA)
Genotype-Tissue Expression Portal (GTEx)
Gene Expression Omnibus (GEO)
Ivy Glioblastoma Atlas Project (Ivy GAP)
Protein Data Bank (PDB)
Iterative Threading Assembly Refinement (I-TASSER) 
KEGG Database
PROCHECK Program
SPICKER (a clustering algorithm)
DAVID Software
PolyPhen-2

Computational Approaches

Array comparative genomic hybridization (aCGH) is a reliable and sensitive technique for detecting gene copy number aberration (CNA) across the entire genome. Oligonucleotide microarrays provide high resolution and diagnostic yield of detection of copy number changes comprised in the tumor genome. The emergence of new omics approaches, such as genomic algorithms to identify tumor mutations and molecular modeling tools to predict the three-dimensional structure of proteins, has facilitated the understanding of the dynamic mechanisms involved in the pathogenesis of low-grade gliomas including oligodendrogliomas and astrocytomas. Construction and extension of target-drug interaction networks improves the process of network based drug re-positioning.

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. Bendahou MA, Arrouchi H, Lakhlili W, Allam L, Aanniz T, Cherradi N, Ibrahimi A, Boutarbouch M. Computational Analysis of IDH1, IDH2, and TP53 Mutations in Low-Grade Gliomas Including Oligodendrogliomas and Astrocytomas. Cancer Inform. 2020.
2. Nives Pećina-Šlaus, et al. Comparable genomic copy number aberrations differ across astrocytoma malignancy grades. bioRxiv. 2018.