Network-Based Prediction of Oligodendroglioma Driver Gene Candidates within the Region of the 1p/19q Co-deletion Utilizing Single-Cell Transcriptomes.

Overview

1. Institute for Medical Informatics and Biometry (IMB), Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Fetscherstr. 74, Dresden D-01307, Germany

Abstract

All oligodendrogliomas have a characteristic 1p/19q co-deletion that alters the expression of hundreds of genes on both affected chromosomal arms. The search for genes on 1p and 19q that drive oligodendroglioma development has only made little progress over the last years. Therefore, a computational network-based approach for the analysis of single-cell oligodendroglioma transcriptomes is developed to predict potential driver gene candidates within the region of the 1p/19q co-deletion purely based on tumor cells. Nine genes with strong impact on signaling pathways (ATP6V0B, F3, FUCA1, FTL, HNRNPR, ID3, JUN, MIIP, and PGM1) and 6 partially overlapping genes with strong impact on immune pathways (F3, FTL, FOSB, IFI6, ISG15, and SPINT2) were consistently predicted in at least 2 of the 3 analyzed oligodendrogliomas. Almost all of these genes are known to play important roles in growth, proliferation, and stem cells of closely related gliomas, but also roles in migration or reprogramming of the microenvironment had been reported in experimental glioma studies. Comparisons to a previous network-based bulk oligodendroglioma analysis and additional evaluations of the expression behavior of candidate genes in related normal brain cells further strengthen the study. Additional validations based on 2 independent oligodendrogliomas support the candidate genes. Robustness of the predictions is shown for imputed and nonimputed data. Strengths of the network-based approach are demonstrated by comparisons to related approaches. All findings clearly suggest that the developed network-based approach for the analysis of single-cell tumor transcriptomes is able to predict novel potential driver gene candidates for oligodendrogliomas. These are very valuable information for future experimental studies. The computational network-based approach can also be transferred to the analysis of single-cell transcriptomes of other types of cancer.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• geo:GSE70630 — at NCBI GEO; found in the text, “Oligodendroglioma single-cell gene expression…”
• zenodo:19128102 — at Zenodo; found in the text, “Oligodendroglioma single-cell gene expression…”

Data Availability

Publicly available single-cell transcriptomes of oligodendrogliomas from [21] (GEO: GSE70630 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70630)) were analyzed in this study. The preprocessed omics datasets of the 3 deeply analyzed oligodendrogliomas (MGH36, MGH53, and MGH54) and the used gene annotations are provided as supplemental information (omics data: Tables S1, S3, S8, and S9; gene annotations: Table S7). The R package regNet [34] used for network inference and network propagation is publicly available from GitHub at https://github.com/seifemi/regNet under GNU GPL-3. All performed regNet computations, underlying datasets of all 5 analyzed oligodendrogliomas (MGH36, MGH53, MGH54, MGH60, and MGH93), all datasets of the simulation study with known ground truth, and corresponding R scripts of the regNet-based analyses are available from Zenodo at https://doi.org/10.5281/zenodo.19128102.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 66 references.

Cite

This paper

Seifert, M. (2026). Network-Based Prediction of Oligodendroglioma Driver Gene Candidates within the Region of the 1p/19q Co-deletion Utilizing Single-Cell Transcriptomes. Computational and structural biotechnology journal, 35(1), 0059. https://doi.org/10.34133/csbj.0059

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