Assessment of Alterations in Gene Expression in Recurrent Malignant Glioma after Radiotherapy Using Complementary Deoxyribonucleic Acid Microarrays
Bibliographic record
Abstract
OBJECTIVE: We used complementary deoxyribonucleic acid expression microarrays to assess the effects of radiotherapy on gene expression in glioblastoma multiforme. We hypothesized that postradiation recurrent tumors may demonstrate alterations in gene expression from the primary tumor specimen. METHODS: Patients were diagnosed with glioblastoma multiforme at resection of the initial tumor, and they received 60 Gy of fractionated radiotherapy before recurrence. Ribonucleic acid samples from both the primary and the postradiation recurrent tumor in each patient were screened and compared using complementary deoxyribonucleic acid expression arrays and Northern blot analysis. RESULTS: Messenger ribonucleic acid levels of growth factors participating in paracrine loops, such as vascular endothelial growth factor and platelet-derived growth factor receptor beta, were decreased in postradiation recurrent tumors as compared with primary tumors in three of four patients. However, messenger ribonucleic acid levels of growth factors involved in autocrine loops, such as epidermal growth factor receptor, platelet-derived growth factor alpha, platelet-derived growth factor A, and basic fibroblast growth factor, were decreased in two of four, two of four, three of four, and three of four patients' recurrent tumors, respectively. Microvessel counts demonstrated that blood vessel growth was decreased significantly in postradiation recurrent tumor specimens. CONCLUSION: After radiotherapy of glioblastoma multiforme, levels of paracrine-acting growth factors are diminished in correspondence with the reduction in vascular density. In contrast, growth factors that participate in autocrine loops demonstrate elevated levels of gene expression. These results suggest that maintenance of autocrine loops may be important in tumor regrowth after radiotherapy.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".