Decoding Shared Genetics: Unveiling the Link Between Major Depressive Disorder and Glioblastoma Multiforme
Bibliographic record
Abstract
Major depressive disorder (MDD) is a common psychiatric disorder, and glioblastoma multiforme (GBM) is the most common primary central nervous system tumor. Patients with GBM have been shown to have a high incidence of MDD, but the pathogenesis of these two diseases remains unclear. This study utilized a high-throughput omics approach to explore the genetic link between MDD and GBM. First, five shared genes between MDD and GBM were identified using differential expression analysis, including EN1 and UBE2C. The result showed that the shared genes EN1 and UBE2C were both differentially expressed in the two diseases, respectively, and related to the development of glioma, dopamine regulation and Alzheimer's disease. Subsequently, weighted gene co-expression network analysis (WGCNA) revealed different functional enrichments in neural activity for GBM and MDD, respectively. The co-expression network results highlighted the common molecular mechanisms between MDD and GBM gene modules, emphasizing neuralrelated activities and gene expression regulation. Our study reveals a compelling genetic link between MDD and GBM, revealing potential co-pathogenesis. And EN1 and UBE2C emerged as key genes, indicating common signaling pathways and potential therapeutic targets. Further exploration of these genes and pathways could provide avenues for targeted therapeutic intervention in these devastating diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".