Dynamic change in Siglec-15 expression in peritumoral macrophages confers an immunosuppressive microenvironment and poor outcome in glioma
Bibliographic record
Abstract
Background Sialic acid-binding immunoglobulin-like lectin-15 (Siglec-15) was reported to be a novel immune checkpoint molecule comparable to programmed cell death 1 ligand 1 (PD-L1). However, its expression profile and immunosuppressive mechanisms in the glioma tumor microenvironment have not yet been fully explored. Objectives To identify the expression profile and potential function of Siglec-15 in glioma tumor microenvironment. Methods We investigated Siglec-15 and PD-L1 expression in tumor tissues from 60 human glioma patients and GL261 tumor models. Next, Siglec-15 knockout macrophages and mice were used to elucidate the immunosuppressive mechanism of Siglec-15 impacting macrophage function. Results Our results demonstrated that high levels of Siglec-15 in tumor tissues was positively correlated with poor survival in glioma patients. Siglec-15 was predominantly expressed on peritumoral CD68+ tumor-associated macrophages, which accumulated to the highest level in grade II glioma and then declined as grade increased. The Siglec-15 expression pattern was mutually exclusive with that of PD-L1 in glioma tissues, and the number of Siglec-15+PD-L1- samples (n = 45) was greater than the number of Siglec-15-PD-L1+ samples (n = 4). The dynamic change in and tissue localization of Siglec-15 expression were confirmed in GL261 tumor models. Importantly, after Siglec15 gene knockout, macrophages exhibited enhanced capacities for phagocytosis, antigen cross-presentation and initiation of antigen-specific CD8+ T-lymphocyte responses. Conclusion Our findings suggested that Siglec-15 could be a valuable prognostic factor and potential target for glioma patients. In addition, our data first identified dynamic changes in Siglec-15 expression and distribution in human glioma tissues, indicating that the timing of Siglec-15 blockade is critical to achieve an effective combination with other immune checkpoint inhibitors in clinical practice.
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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".