Cancer Cells Promote Phenotypic Alterations in Hepatocytes at the Edge of Cancer Cell Nests to Facilitate Vessel Co-Option Establishment in Colorectal Cancer Liver Metastases
Bibliographic record
Abstract
Vessel co-option is correlated with resistance against anti-angiogenic therapy in colorectal cancer liver metastases (CRCLM). Vessel co-opting lesions are characterized by highly motile cancer cells that move toward and along the pre-existing vessels in the surrounding nonmalignant tissue and co-opt them to gain access to nutrients. To access the sinusoidal vessels, the cancer cells in vessel co-opting lesions must displace the hepatocytes and occupy their space. However, the mechanisms underlying this displacement are unknown. Herein, we examined the involvement of apoptosis, autophagy, motility, and epithelial-mesenchymal transition (EMT) pathways in hepatocyte displacement by cancer cells. We demonstrate that cancer cells induce the expression of the proteins that are associated with the upregulation of apoptosis, motility, and EMT in adjacent hepatocytes in vitro and in vivo. Accordingly, we observe the upregulation of cleaved caspase-3, cleaved poly (ADP-ribose) polymerase-1 (PARP-1) and actin-related protein 2/3 (ARP2/3) in adjacent hepatocytes to cancer cell nests, while we notice a downregulation of E-cadherin. Importantly, the knockdown of runt-related transcription factor 1 (RUNX1) in cancer cells attenuates the function of cancer cells in hepatocytes alterations in vitro and in vivo. Altogether, our data suggest that cancer cells exploit various mechanisms to displace hepatocytes and access the sinusoidal vessels to establish vessel co-option.
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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) | 0.001 | 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 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".