Upregulation of AKR1C1 and AKR1C3 expression in OPSCC with integrated HPV16 and HPV‐negative tumors is an indicator of poor prognosis
Bibliographic record
Abstract
Different studies have shown that HPV16-positive OPSCC can be subdivided based on integration status (integrated, episomal and mixed forms). Because we showed that integration neither affects the levels of viral genes, nor those of virally disrupted human genes, a genome-wide screen was performed to identify human genes which expression is influenced by viral integration and have clinical relevance. Thirty-three fresh-frozen HPV-16 positive OPSCC samples with known integration status were analyzed by mRNA expression profiling. Among the genes of interest, Aldo-keto-reductases 1C1 and 1C3 (AKR1C1, AKR1C3) were upregulated in tumors with viral integration. Additionally, 141 OPSCC, including 48 HPV-positive cases, were used to validate protein expression by immunohistochemistry. Results were correlated with clinical and histopathological data. Non-hierarchical clustering resulted in two main groups differing in mRNA expression patterns, which interestingly corresponded with viral integration status. In OPSCC with integrated viral DNA, often metabolic pathways were deregulated with frequent upregulation of AKR1C1 and AKR1C3 transcripts. Survival analysis of 141 additionally immunostained OPSCC showed unfavorable survival rates for tumors with upregulation of AKR1C1 or AKR1C3 (both p <0.0001), both in HPV-positive (p ≤0.001) and -negative (p ≤0.017) tumors. OPSCC with integrated HPV16 show upregulation of AKR1C1 and AKR1C3 expression, which strongly correlates with poor survival rates. Also in HPV-negative tumors, upregulation of these proteins correlates with unfavorable outcome. Deregulated AKR1C expression has also been observed in other tumors, making these genes promising candidates to indicate prognosis. In addition, the availability of inhibitors of these gene products may be utilized for drug treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".