Impact of COL11A1 Mutations on Tumor Mutational Signatures and Immune Microenvironment in Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Targeted immunotherapy can significantly improve the survival rates of head and neck squamous cell carcinoma (HNSCC) patients; however, only a minority of patients respond favorably to such treatments. In this study, we investigate the association between the tumor mutational burden (TMB) and clinical characteristics of HNSCC, as well as the impact of COL11A1 gene mutations on the immune microenvironment in head and neck cancer (HNC). In our analysis of HNSCC patient data from The Cancer Genome Atlas (TCGA) database, we found significant differences in TMB across various clinical features. Furthermore, a high TMB was associated with poorer survival outcomes. Despite its relatively high mutation frequency, the clinical significance of COL11A1 has not been fully explored. Our analysis of COL11A1 mutations in HNSCC and their functional impact found that COL11A1 mutations are associated with poor survival outcomes. Additionally, we observed a close correlation between COL11A1 mutations, reduced immune cell infiltration, and altered expression levels of chemokines. Further enrichment analysis suggested that COL11A1 mutations may alter the tumor immune microenvironment by affecting immune-related pathways, such as leukocyte activation and chemokine signaling. Finally, we evaluated the effect of the COL11A1 mutation on immune infiltration. Using a variety of immune infiltration algorithms, we found that the mutation of COL11A1 was associated with lower levels of immune cell infiltration. In summary, the present study explored the potential role of COL11A1 mutation in the progression of HNSCC, and our findings provide a potential therapeutic target for better therapeutic outcomes in HNSCC.
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 | 0.001 |
| 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".