Extracellular Vesicles from Lung Adenocarcinoma Cells Induce Activation of Different Cancer-Associated Fibroblast Subtypes
Bibliographic record
Abstract
Background: Lung cancer, including the major subtype lung adenocarcinoma (LUAD), is the leading cause of cancer deaths worldwide, largely due to metastasis. Improving survival rates requires new treatment strategies and a deeper understanding of the mechanisms that drive tumor progression within the tumor microenvironment (TME). This study investigated the impact of extracellular vesicles (EVs) derived from LUAD cells on lung fibroblasts. Methods: EVs were isolated from LUAD cell lines via ultracentrifugation and characterized using nanoparticle tracking analysis and Western blotting. Lung fibroblasts were treated with PBS, TGFβ, or EVs, and their activation was assessed through protein (Western blotting) and RNA analyses (RNA seq and RT-qPCR). Results: The results confirmed the TGFβ induced activation and showed that LUAD EVs could also activate fibroblasts, increasing cancer-associated fibroblast (CAF) markers. While EV-induced CAF activation displayed unique features, like an increase in proliferation-related genes, the EV and TGFβ treatments also shared some differentially expressed genes. The EV groups induced a higher expression of ECM remodeling and EMT-associated genes, but some of those genes were also upregulated in the TGFβ group. Mesenchymal genes POSTN and SPOCK1 were significantly upregulated in TGFβ- and EV-treated fibroblasts. Their secretion as proteins from the TGFβ- and EV-induced CAFs was not significant, confirmed through ELISA. Conclusions: These findings suggest that LUAD EVs play a role in CAF activation through both shared and distinct pathways compared to canonical TGFβ activation, potentially identifying novel gene expressions involved in CAF activation. Additionally, optimal protein secretion conditions of confirmed CAF-upregulated genes need to be established to determine their contribution to the TME.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".