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 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 | 0.001 | 0.000 |
| 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".