MiR-19a enhances cell proliferation, migration, and invasiveness through enhancing lymphangiogenesis by targeting thrombospondin-1 in colorectal cancer
Bibliographic record
Abstract
Colorectal cancer (CRC) is a devastating disease with high mortality and morbidity, and the underlying mechanisms of miR-19a in CRC are poorly understood. In our study, dual-luciferase reporter assays were used to evaluate the binding of miR-19a with thrombospondin-1 (THBS1). Cell viability, migration, and invasiveness were assessed using MTT, wound healing, and Transwell assays, respectively. Tube-formation assays with human lymphatic endothelial cells (HLECs) were used to evaluate lymphangiogenesis, and tumor xenograft assays were used to measure tumor growth. The results showed that miR-19a was up-regulated and THBS1 was down-regulated in CRC tissues and cells. Applying an inhibitor of miR-19a suppressed survival, migration, and invasiveness, and inhibited the expression of matrix metallopeptidase 9 (MMP-9) and vascular endothelial growth factor C (VEGFC). Further mechanistic study identified that THBS1 is a direct target of miR-19a. THBS1 silencing attenuated the above-mentioned suppressive effects induced with the miR-19a inhibitor. Furthermore, the miR-19a inhibitor suppressed the migration and tube-formation abilities of HLECs via targeting the THBS1-MMP-9/VEGFC signaling pathway. And the inhibition of miR-19a also suppressed tumor growth and lymphatic tube formation in vivo. In conclusion, miR-19a inhibition suppresses the viability, migration, and invasiveness of CRC cells, and suppresses the migration and tube-formation abilities of HLECs, and further, inhibits tumor growth and lymphatic tube formation in vivo via targeting THBS1.
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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 | 0.001 |
| 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".