Human papilloma virus infection drives unique metabolic and immune profiles in head and neck and cervical cancers: implications for targeted therapies and prognostic markers
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
Human papillomavirus (HPV) is a key driver of head and neck squamous cell carcinoma (HNSCC) and cervical squamous cell carcinoma (CESC). Yet, these cancers exhibit distinct molecular and clinical features influenced by HPV status. This study utilizes RNA sequencing data from The Cancer Genome Atlas (TCGA). It employs bioinformatics tools, including DESeq2 for differential gene expression, CIBERSORT for immune profiling, and Kaplan-Meier survival analysis to investigate these differences. Differential expression analysis revealed distinct molecular signatures, with HPV-positive tumors enriched in immune-related pathways such as cytokine-cytokine receptor interactions. In contrast, HPV-negative tumors exhibited upregulation of metabolic pathways, including PPAR signaling. Metaflux analysis further demonstrated contrasting metabolic profiles: HPV-positive tumors showed increased glycolysis and oxidative stress regulation, whereas HPV-negative tumors were characterized by elevated amino acid and nucleotide metabolism. Immune profiling highlighted more significant CD8 + T-cell infiltration in HPV-positive tumors, while HPV-negative tumors were predominantly associated with macrophages, suggesting differing tumor immune environments. Survival analysis identified CXCL11 and STAT1 as potential prognostic biomarkers, with lower expression correlating with poorer survival in both cancers. These findings provide an integrated perspective on the molecular, metabolic, and immune differences associated with HPV status, offering insights into potential therapeutic strategies.
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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