Differential expression of host oncogenes in human papillomavirus‐associated nasopharyngeal and cervical epithelial cancers
Bibliographic record
Abstract
Human papillomavirus (HPV)-related cervical and nasopharyngeal cancers differ in molecular mechanisms underlying the oncogenic processes. The disparity may be attributed to differential expression of oncoproteins. The current study investigated the host oncogenes expression pattern in HPV-associated cervical and nasopharyngeal cancer. Formalin-fixed paraffin-embedded tissues originating from the nasopharyngeal and cervical regions were screened using Hematoxylin and Eosin staining. Genomic DNA and total RNA were extracted from confirmed cancer biopsies and non-cancer tissues (NC). HPV was detected by PCR using MY09/GP5+/6+ primers. Protein expression levels of AKT, IQGAP1, and MMP16 in HPV-infected cancers and controls were determined by immunohistochemistry. RT-qPCR was used to profile mRNAs of the oncogenes. AKT and IQGAP1 proteins were highly expressed in the epithelial cancers compared with the non-cancer tissues (p < 0.05). IQGAP1 and MMP16 mRNAs level was significantly higher in the cancers than in the NC (p < 0.05), but not AKT mRNA levels. MMP16 protein was ubiquitously expressed in all tissues. AKT mRNA level was significantly elevated in CC compared with NPC (p < 0.001). However, the difference in AKT, IQGAP1 and MMP16 proteins level between CC and NPC was not significant (p > 0.05). The oncoproteins expression level between the HPV-positive and HPV-negative cancer biopsies showed no significant difference (p < 0.05). Current study reports AKT but not IQGAP1 and MMP16 mRNAs differentially expression in cervical and nasopharyngeal cancers, independent of HPV infection status.
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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 | 0.001 | 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) | 0.001 | 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".