CD47 promotes MAPK and epithelial-to-mesenchymal transition molecular programs to drive pro-metastatic phenotypes in non-small cell lung cancer
Bibliographic record
Abstract
Abstract CD47 is best known for its role in tumor immune evasion; however, studies in diverse cell models indicate that it also has cell-autonomous, tumor-promoting functions which are cell type- and context-specific. Motivated by the prognostic and therapeutic significance of CD47 and the limited knowledge regarding its roles beyond immune evasion in non-small cell lung cancer (NSCLC), we sought to define the cellular and molecular processes driven by intrinsic CD47 signaling in NSCLC. Transcriptome profiling of CD47 wildtype and knockout NSCLC cells implicated its regulation of genes enriched for signatures of MAPK signaling and epithelial-to-mesenchymal transition (EMT). A significant positive association between CD47 and MAPK/EMT expression signatures was also evident in large cohorts of NSCLC cell lines and tumor tissues. Functional studies indicated that CD47 does not regulate cell proliferation in NSCLC cells like it does in other cancer types. Instead, CD47 regulates cell adhesion and migration through an ERK and EMT axis, validating our transcriptomic findings. Moreover, CD47 loss-of-function significantly diminished the ability of NSCLC cells to metastasize in vivo , demonstrating the physiological relevance of cell-intrinsic CD47 signaling in lung cancer cells. Our data reveal a novel role for CD47 in relaying signals through ERK to promote EMT expression programs and pro-metastatic phenotypes in NSCLC. Although additional mechanistic studies are needed to further decipher the CD47-ERK-EMT signaling pathway, our findings reinforce the therapeutic potential of CD47, rationalizing further research to develop CD47 blockade as a multimodal therapy 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) | 0.002 | 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".