Revealing molecular mechanisms of early-onset tongue cancer by spatial transcriptomics
Bibliographic record
Abstract
Tongue cancer at a young age demonstrates an increase in incidence, aggressiveness, and poor response to therapy. Classic etiological factors for head and neck tumors such as tobacco, alcohol, and human papillomavirus are not related to early-onset tongue cancer. Mechanisms of development and progression of this cancer remain unclear. In this study, we performed spatial whole-transcriptome profiling of tongue cancer in young adults compared with older patients. Nine patients with tongue squamous cell carcinoma (T2-3N0-1M0) were included and divided into two groups: younger (n = 5) and older than 45 years (n = 4). Formalin-fixed paraffin-embedded (FFPE) and fresh frozen (FF) samples of tumor tissue from 4 young and 5 older patients, respectively, were used for spatial transcriptomic profiling using the 10 × Genomics Visium. The findings were validated using SeekGene single cell full-length RNA sequencing (1 young vs 1 older patient) and TCGA data (15 young vs 70 older patients). As a result, we performed the first successful integration of spatial transcriptomics data from FF and FFPE samples and revealed distinctive features of tongue cancer in young adults. Oxidative stress, vascular mimicry, and MAPK and JAK-STAT pathways were enriched in early-onset tongue cancer. Tumor microenvironment demonstrated increased gene signatures corresponding to myeloid-derived suppressor cells, tumor-associated macrophages, and plasma cells. The invasive front was accompanied by vascular mimicry with arrangement of tumor-associated macrophages and aggregations of plasma cells and lymphocytes organized into tertiary lymphoid structures. Taken together, these results indicate that early-onset tongue cancer has distinct transcriptomic features and molecular mechanisms compared to older patients.
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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) | 0.000 | 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 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".