Elevated Siglec-7 expression correlates with adverse clinicopathological, immunological, and therapeutic response signatures in breast cancer patients
Bibliographic record
Abstract
Introduction: Breast cancer is a highly heterogeneous malignancy, characterized by an intricate, hypersialylated tumor microenvironment that actively inhibits immune functions. Although immune checkpoint inhibitors have marked clinical advantages in various tumor types, their efficacy in Triple-Negative Breast Cancer (TNBC) and Human Epidermal Growth Factor Receptor 2 positive (HER2+) patients remains limited. Siglec-7 has emerged as a novel and promising candidate for advancing cancer immunotherapy. Methods: Herein, we explored the potential role of Siglec-7, a key inhibitory glycoimmune checkpoint, in human breast tumor microenvironment in a cohort of 45 Moroccan patients. Subsequently, data were corroborated using large-scale data from independent cohorts of American and British-Canadian patients, the TCGA and METABRIC. Results: We report that Siglec-7 transcripts were significantly upregulated in breast tumor tissues compared to matched adjacent non-invaded tissues. Furthermore, higher Siglec-7 expression correlated with poor clinicopathological features, including negative Estrogen Receptor (ER) and Progesterone Receptor (PR) status, advanced tumor grades, and unfavorable patient prognosis. Siglec-7 overexpression was linked to gene expression patterns, known to promote breast tumor progression through pathways involved in proliferation, invasion, and immune escape. Notably, high Siglec-7 expression was associated with increased infiltration of immunosuppressive cells and T-cells with an exhausted phenotype. Interestingly, a positive correlation between siglec-7 and A2AR, another emerging and potent inhibitory checkpoint, was revealed, along with the co-upregulation of other inhibitory immune checkpoint genes; and this was associated with signatures of impaired immune effector functions. Finally, our data also pinpointed an interesting role of high Siglec-7 expression in predicting resistance to conventional therapies, including chemotherapy, endocrine treatments, and immunotherapy. Discussion: These findings strongly suggest a central role for Siglec-7 as a promising therapeutic target and prognostic biomarker for human breast cancer, warranting further investigation into its clinical potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".