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. 2024 Mar 27:15:1345520.
doi: 10.3389/fneur.2024.1345520. eCollection 2024.

Strength of spatial correlation between gray matter connectivity and patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival

Shelli R Kesler  1 , Rebecca A Harrison  2 , Alexa De La Torre Schutz  1 , Hayley Michener  3 , Paris Bean  3 , Veronica Vallone  3 , Sarah Prinsloo  3
Affiliations

Strength of spatial correlation between gray matter connectivity and patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival

Shelli R Kesler et al. Front Neurol. .

Abstract

Introduction: Like other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics.

Methods: We retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes.

Results: We found that all 17 genes were significantly co-localized with brain connectivity (p < 0.03, corrected). The strength of co-localization was highly predictive of overall survival in a cross-validated Cox Proportional Hazards model (mean area under the curve, AUC = 0.68 +/- 0.01) and significantly (p < 0.001) more so for a random forest survival model (mean AUC = 0.97 +/- 0.06). Bayesian network analysis demonstrated direct and indirect causal relationships among gene-brain co-localizations and survival. Gene ontology analysis showed that metabolic processes were overexpressed when spatial co-localization between brain connectivity and gene transcription was highest (p < 0.001). Drug-gene interaction analysis identified 84 potential candidate therapies based on our findings.

Discussion: Our findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival.

Keywords: MRI; connectome; glioma; imaging transcriptomics; transcriptome.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Glioma survival data. (A) Kaplan–Meier survival curve (blue line) with 95% confidence interval (gray shading) showing that median overall survival (dotted gray lines) for our cohort of glioma patients was 81 months. (B) The mean cross-validated, time dependent area under the curve for predicting survival from imaging transcriptomic relationships was 0.68 +/− 0.01 for Cox PH and 0.97 +/− 0.06 for random forest (RF). Random forest significantly outperformed Cox PH (p < 0.001, corrected).
Figure 2
Figure 2
Bayesian network path analysis. Causal relationships are represented by directed arrows between imaging transcriptomes and glioma survival. The gene labeled nodes represent the strength of the relationship between the gene and brain connectivity, not gene expression itself. The direction of the coefficient is represented by a + for direct and - for inverse.
Figure 3
Figure 3
Gene ontology. Gene ontology analysis of the genes whose relationship with brain connectivity was predictive of glioma survival.
See this image and copyright information in PMC

Update of

References

    1. Stoecklein VM, Stoecklein S, Galie F, Ren J, Schmutzer M, Unterrainer M, et al. . Resting-state fMRI detects alterations in whole brain connectivity related to tumor biology in glioma patients. Neuro-Oncology. (2020) 22:1388–98. doi: 10.1093/neuonc/noaa044, PMID: - DOI - PMC - PubMed
    1. Kesler SR, Noll K, Cahill DP, Rao G, Wefel JS. The effect of IDH1 mutation on the structural connectome in malignant astrocytoma. J Neuro-Oncol. (2017) 131:565–74. doi: 10.1007/s11060-016-2328-1, PMID: - DOI - PMC - PubMed
    1. Derks J, Dirkson AR, de Witt Hamer PC, van Geest Q, Hulst HE, Barkhof F, et al. . Connectomic profile and clinical phenotype in newly diagnosed glioma patients. NeuroImage Clin. (2017) 14:87–96. doi: 10.1016/j.nicl.2017.01.007, PMID: - DOI - PMC - PubMed
    1. Zhou J, Gennatas ED, Kramer JH, Miller BL, Seeley WW. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron. (2012) 73:1216–27. doi: 10.1016/j.neuron.2012.03.004, PMID: - DOI - PMC - PubMed
    1. Huo L, Du X, Li X, Liu S, Xu Y. The emerging role of neural cell-derived exosomes in intercellular communication in health and neurodegenerative diseases. Front Neurosci. (2021) 15:738442. doi: 10.3389/fnins.2021.738442, PMID: - DOI - PMC - PubMed

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