Decoding Shared Genetics: Unveiling the Link Between Major Depressive Disorder and Glioblastoma Multiforme
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it