Identifying the common denominator: Dectin-1 as a promising marker for macrophage modulation in lung cancer and fibrosis
Bibliographic record
Abstract
Background: Tumor-associated macrophages (TAMs) promote tumorigenesis and tumor growth. They share features with profibrotic macrophages found in lung fibrosis. Identifying specific markers to target these cells in both animal and human models is complex but necessary. This proposal aimed to identify macrophage membrane receptors to link human diseases and animal/cellular models. Methods and Results: To determine a shared gene signature unique to TAMs and profibrotic macrophages, we used transcriptomic datasets of macrophage phenotypes from both human (PBMCs) and murine (bone marrow-derived macrophages) systems obtained from the Gene Expression Omnibus database. To validate the in silico results, we conducted our own macrophage polarizations studies in vitro and utilized nanoString® technology. Through this process, 6 genes were significantly upregulated in profibrotic macrophages and were consistent in both human and murine systems. CLEC7A was one of the identified genes encoding the membrane receptor Dectin-1. In single-cell RNA-seq idiopathic pulmonary fibrosis (IPF) datasets, CLEC7A was observed to be significantly upregulated in monocytes and macrophages of IPF patients. Similarly, in a lung cancer CosMX spatial dataset, elevated expression of CLEC7A was found in TAMs. Immunohistochemical examination of lung specimens from patients with lung adenocarcinoma and IPF revealed that macrophages infiltrating fibrotic lesions and tumors had increased Dectin-1 levels. Conclusion: Dectin-1 is a macrophage receptor that is upregulated in both murine and human models of lung cancer and IPF serving as a potential therapeutic target to reprogram pathological macrophages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".