May 19, 2026
New Publication: Network-based Prediction of Driver Candidates for Oligodendrogliomas
A new computational analysis of oligodendrogliomas – a specific type of brain tumors – has been published in Computational and Structural Biotechnology Journal by PD Dr. Michael Seifert.
The publication presents a computational network-based approach for the analysis of single-cell oligodendroglioma transcriptomes to predict potential driver gene candidates within the region of the 1p/19q co-deletion. Since many years, the 1p/19q co-deletion is known as a characteristic molecular marker of oligodendrogliomas that alters the expression of hundreds of genes on both affected chromosomal arms. Such recurrent gene expression alterations do not allow to directly distinguish between potential driver and passenger genes. Therefore, the search for potential oligodendroglioma driver gene candidates on 1p and 19q has only made little progress over the last years. To contribute to overcome this, a computational network-based analysis of single-cell oligodendroglioma transcriptomes was performed to predict potential driver gene candidates purely based on tumor cells.
The study consistently predicted nine genes with strong impact on signaling pathways (ATP6V0B, F3, FUCA1, FTL, HNRNPR, ID3, JUN, MIIP, and PGM1) and six partially overlapping genes with strong impact on immune pathways (F3, FTL, FOSB, IFI6, ISG15, and SPINT2) in at least two of the three analyzed oligodendrogliomas. Almost all of these genes are known to play important roles in cell growth, cell proliferation, and stem cells of closely related gliomas. Further, also roles in migration or reprogramming of the microenvironment had been reported in glioma studies. Comparisons to a previous network-based bulk oligodendroglioma analysis and additional validations based on two independent oligodendrogliomas support the predicted candidate genes.
Network-Based Prediction of Oligodendroglioma Driver Gene Candidates within the Region of the 1p/19q Co-deletion Utilizing Single-Cell Transcriptomes
Michael Seifert, Comput Struct Biotechnol J. 2026;35:0059.DOI:10.34133/csbj.0059