IL-4-STAT6-induced high Siglec-G/10 expression aggravates the severe immune suppressive tumor microenvironment and impedes the efficacy of immunotherapy in head and neck squamous cell carcinoma
Bibliographic record
Abstract
Background Immune checkpoint blockade therapy has shown limited efficacy in head and neck squamous cell carcinoma (HNSCC). Sialic acid binding immunoglobulin-like lectin (Siglec)-15 has been identified as a novel immune evasion biomarker, while the role of Siglec-10 in the specific immune suppressive tumor microenvironment remains largely unknown. Methods Immunohistochemical assays were employed to investigate the correlation of the expressions of Siglec-10 and Siglec-15 with the clinicopathological features as well as the prognosis of immunotherapy in patients with HNSCC. The Gene Expression Omnibus datasets were used to identify the upstream transcriptional regulators of SIGLEC10 in tumor-associated macrophages (TAMs) and the downstream biological functions it mediates. These findings were then validated through in vitro and in vivo experiments. The impact of Siglecg deficiency on the efficacy of immunotherapy and the activation of CD8+T cells was analyzed in mouse HNSCC tumor-bearing models. Results The expression of Siglec-G/10, rather than that of Siglec-15, was positively correlated with immune suppressive marker programmed death-ligand 1 (PD-L1) expression and was associated with cervical lymph node metastasis, poorer pathologic stage, and lower sensitivity to immunotherapy. Siglecg deficiency rescued the immune suppressive tumor microenvironment, as evidenced by decreased TAM-associated phenotype and increased CD8+T cell infiltration and activation, which inhibited tumor growth significantly. Single-cell sequence and transcription factor prediction revealed that signal transducer and activator of transcription 6 (STAT6) could induce Siglec-G/10 transcription. Interleukin (IL)-4 could upregulate Siglec-G/10 expression significantly via STAT6 activation, as proved by overexpression and inhibition of STAT6. Signal transduction mechanism revealed that Siglec-G/10 could promote TAM differentiation and activation via increasing HIF1α (hypoxia-inducible factor 1α) expression. Furthermore, Siglecg deficiency could enhance the efficacy of immune checkpoint inhibitor, and increase the infiltration and cytotoxic functions of CD8+T cells. Conclusions Our results suggest that high Siglec-G/10 expression aggravates the immune suppressive tumor microenvironment and impedes the immunotherapy efficacy in HNSCC, which indicates that targeting Siglec-G/10 may represent a promising therapeutic option for improving the immunotherapy efficacy in HNSCC.
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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) | 0.001 | 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".