CD47 Promotes Tumor Invasion and Metastasis in Non-small Cell Lung Cancer
Bibliographic record
Abstract
CD47 is overexpressed in many human cancers, its level positively correlates with tumor invasion and metastasis. However, it is largely unknown whether CD47 overexpression drives metastasis and how CD47 lead to tumor metastasis in non-small cell lung cancer (NSCLC). In this study, we analyzed NSCLC specimens and cell lines, and revealed that CD47 is expressed at a higher level than in tumor-free control samples. Furthermore, increased CD47 expression correlated with clinical staging, lymph node metastasis and distant metastasis. In order to understand the molecular mechanisms underlying CD47 functions, we applied both gain-of-function and loss-of-function approaches in cell lines. The siRNA-mediated downregulation of CD47 inhibited cell invasion and metastasis in vitro, while the overexpression of CD47 by plasmid transfection generated opposite effects. In vivo, CD47-specific shRNA significantly reduced tumor growth and metastasis. On the molecular level, the expression of CD47 correlated with that of Cdc42, both in cell lines and NSCLC specimens. The inhibition of Cdc42 attenuates the invasion and metastasis of CD47-overexpressing cells. These results indicate that Cdc42 is a downstream mediator of CD47-promoted metastasis. Our findings provide first evidence that CD47 is an adverse prognostic factor for disease progression and metastasis, and a promising therapeutic target for NSCLC.
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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".