E2F5 Overexpression in Laryngeal Squamous Cell Carcinoma: Associations With Neutrophil Extracellular Traps in the Tumor Microenvironment
Bibliographic record
Abstract
Background: Laryngeal squamous cell carcinoma (LSCC) is a common malignant tumor of the head and neck, associated with smoking and excessive alcohol consumption. The objective was to investigate the expression pattern of E2F transcription factor 5 (E2F5) in LSCC and its association with neutrophil extracellular traps (NETs), elucidating its role in the tumor microenvironment. Methods: At the cellular level, single-cell RNA sequencing (scRNA-seq) was employed to analyze the expression of E2F5 and NETs-related genes (S100A8, S100A9, LCN2, etc.). At the tissue level, spatial transcriptomics (ST) was used to examine the E2F5 expression pattern. At the mRNA level, E2F5 expression was assessed through mRNA expression profiling, and at the protein level, expression was validated using immunohistochemistry (IHC) on tissue specimens, including 10 LSCC cases (laryngeal, hypopharyngeal, and oropharyngeal squamous cell carcinomas) and 10 non-LSCC controls (benign lesions such as mucoceles, hemangiomas, and polyps). Clustered regularly interspaced short palindromic repeats (CRISPR) knockout screening combined with the CERES algorithm was utilized to evaluate the impact of E2F5 on LSCC cell line proliferation, with negative/positive dependency scores indicating suppression/promotion of growth, respectively. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the correlation between E2F5 and immune cells, and chromatin immunoprecipitation sequencing (ChIP-seq) was performed to validate the transcriptional regulation of NETs-related genes by E2F5. Statistical analyses included Wilcoxon, standardized mean difference (SMD), receiver operating characteristic (ROC), and summary receiver operating characteristic (sROC). Results: , ROC AUC = 1). Knockdown of E2F5 significantly inhibited proliferation in LSCC cell lines (e.g., BICR31, BICR16) (inhibition score < 0). High E2F5 expression was positively correlated with T-helper cells and natural killer (NK) CD56bright cells (R = 0.251, 0.175, P < 0.05) and negatively correlated with neutrophils and Th17 cells (R = -0.293, -0.260, P < 0.05). Cellular and tissue-level analyses revealed high NETs expression in LSCC, with E2F5 also highly expressed in NETs-related cells and regions. ChIP-seq analysis confirmed that E2F5 regulates NETs-related genes. Functional enrichment analysis indicated that E2F5-related genes are involved in transcriptional regulation, chromatin organization, and immune regulation. Conclusion: E2F5 is highly expressed in LSCC and is associated with the regulation of NETs-related genes. It may contribute to tumor proliferation and immune evasion by reshaping the tumor microenvironment, highlighting E2F5 as a potential therapeutic target that warrants further functional validation.
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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 | 0.001 | 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) | 0.001 | 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".