Diurnal rhythms (DR) in gene expression in human oral mucosa: Implications for gender differences in toxicity, response and survival and optimal timing of targeted therapy (Rx)
Bibliographic record
Abstract
2507 Background: DR in processes relevant to oncology, including cell cycle, apoptosis, and angiogenesis, have been demonstrated (Nat Rev Cancer 3:350, Cancer Res 63:7277). In rodents, 10–20% of the genome has a 24-hour (h) rhythm in RNA expression (Cell 109:307). A molecular clock consisting of transcription/translation feedback loops of clock-genes controls this rhythmicity (Nat Rev Neurosci 4:649). We studied, for the first time, daily whole genome RNA expression patterning in healthy human volunteers. Methods: RNA samples extracted from oral mucosa biopsies (bx) obtained every 4h over 24h (6 bx) from 5 males (M) and 5 females (F) were subjected to microarray analysis (Affymetrix HG_U133_Plus2 chip, 54,679 transcripts). COSOPT, designed for circadian microarray time series analysis (Methods Enzymol 383: 149) was used to detect genes with significant rhythms (pMMC-Beta = 0.1). Expression patterns were visualized in GeneSpring GX7.3 (Agilent) and validated by real-time PCR and ANOVA. Results: There were 801 and 810 rhythmic genes in M and F respectively, with most genes peaking at 4AM or 4PM in M but at 6AM or 11AM in F. Only 90 rhythmic genes (including core clock-genes and clock controlled genes) were common to M and F, with over 700 genes only rhythmic in M and not in F and vice versa. The profiles of clock- genes and clock-controlled genes were inverted relative to the nocturnal rodent data. There were 75 and 67 rhythmic transcription factor genes in M and F respectively, with 28 common to both M and F. There were 71 rhythmic human cancer genes (Nat Rev Cancer 4:177) with significant gender differences. This group includes rhythmic gene products involved in signaling pathways currently targeted for cancer Rx. Conclusions: We show for the first time a significant gender specific DR in gene expression involving multiple genes of interest in oncology. This may contribute to the documented gender differences in toxicity, response and survival (J Clin Onc 24: 3562, NEJM 353:133), and can inform future trials of optimal timing of antisense Rx and other targeted Rx. The inverted DR in rodents vs. humans has implications for translating rodent data to human Rx trials. No significant financial relationships to disclose.
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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 | 0.000 |
| 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) | 0.002 | 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".