Novel Post-Translational Modifications and Molecular Substrates in Glioma Identified by Bioinformatics.
Devasahayam Arokia Balaya RexSumaithangi Thattai Arun KumarAkhila Balakrishna RaiChinmaya Narayana KotimoolePrashant Kumar ModiThottethodi Subrahmanya Keshava PrasadPublished in: Omics : a journal of integrative biology (2021)
Glioma is the most common type of brain cancer that originates from the glial cells. It constitutes about one-third of all brain cancers. Recently, transcriptomics, proteomics, and multiomics approaches have been harnessed to discover potential biomarkers and therapeutic targets in glioma. Moreover, post-translational modifications (PTMs) of proteins play a major role in cell biology and function and offer new avenues of research in cancer. Using unbiased multi-PTM bioinformatics analyses of two proteomic datasets of glioma available in the public domain, we identified 866 proteins with common PTMs from both studies. Out of these 866 proteins, 19 proteins were identified with the common PTMs, with the same site modifications pertaining to glioma. Importantly, the identified PTMs belonged to proteins involved in integrin PI3K/Akt/mTOR, JAK/STAT, and Ras/Raf/MAPK pathways. These pathways are essential for cell proliferation in tumor cells and thus involved in glioma progression. Taken together, these findings call for validation in larger datasets in glioma and brain cancers and with an eye to future drug discovery and diagnostic innovation. Bioinformatics-guided discovery of novel PTMs from the publicly available proteomic data can offer new avenues for innovation in cancer research.
Keyphrases
- papillary thyroid
- cell proliferation
- drug discovery
- single cell
- white matter
- healthcare
- resting state
- small molecule
- oxidative stress
- emergency department
- squamous cell carcinoma
- mesenchymal stem cells
- induced apoptosis
- rna seq
- mental health
- machine learning
- functional connectivity
- multiple sclerosis
- lymph node metastasis
- bone marrow
- spinal cord
- deep learning
- pi k akt
- big data
- cell death
- adverse drug