Upregulated E26 Transformation-Specific Variant Transcription Factor 7 in Oral Squamous Cell Carcinoma: Clinicopathological Correlations and Immune Regulatory Mechanisms
Bibliographic record
Abstract
Background: E26 transformation-specific variant transcription factor 7 (ETV7) is implicated in various cancers, but its role in oral squamous cell carcinoma (OSCC) remains undefined. This study explores the clinicopathological significance and molecular mechanisms of ETV7 upregulation in OSCC. Methods: ETV7 protein expression was assessed via immunohistochemistry (IHC) in 173 OSCC and 60 non-OSCC tissues. ETV7 mRNA levels were analyzed using bulk RNA sequencing and single-cell RNA sequencing, supplemented by immune infiltration, enrichment and cell communication analysis. Results: IHC revealed significantly higher ETV7 protein expression in OSCC than in non-OSCC tissues (P < 0.001), correlating with advanced T (r = 0.380, P < 0.001) and N stages (r = 0.592, P < 0.001). High-throughput data confirmed ETV7 mRNA upregulation (standardized mean difference (SMD) = 0.35, 95% confidence interval (CI): 0.15 - 0.56; summary receiver operating characteristic (s receiver operating characteristic) area under the curve (AUC) = 0.78, 95% CI: 0.74 - 0.81), with levels decreasing twofold post-nivolumab treatment (P < 0.001). Enrichment analysis pinpointed the immune response-regulating signaling pathway as a key mechanism, supported by elevated immune cell infiltration (e.g., CD8+ T cells) in high-ETV7 samples. SLC15A4 and DAB2IP emerged as potentially overexpressed ETV7 targets. Cell communication analysis showed ETV7 enhancing myeloid cell interactions via the midkine (MK) pathway. Conclusions: ETV7 upregulation drives OSCC progression, potentially through immune microenvironment modulation, positioning it as a candidate biomarker and therapeutic target. Its association with clinical stage and immunotherapy response underscores its prognostic relevance in OSCC management.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".