ATAD2 silencing decreases VEGFA secretion through targeting has-miR-520a to inhibit angiogenesis in colorectal cancer
Bibliographic record
Abstract
ATPase family AAA domain-containing protein 2 (ATAD2) is involved in various types of cancers, including colorectal cancer. This study aimed to determine the role of ATAD2 in angiogenesis in colorectal cancer. Here, we downregulated ATAD2 expression in HCT116 and SW480 cells, and collected the conditioned medium (CM) from control and ATAD2-silenced cells. The effect of CM on human umbilical vein endothelial cells (HUVEC) was evaluated by using CCK-8, wound healing, tube formation, Western blot, and dual-luciferase reporter assays. Our results showed that the proliferation, migration, and tube formation of HUVEC were reduced in presence of ATAD2-silenced CM, and the levels of phosphorylated vascular endothelial growth factor receptor 2 (P-VEGFR2), CD31, and CD34 were downregulated. Mechanism studies showed that ATAD2 silencing regulated the expression of vascular endothelial growth factor A (VEGFA) and miR-520a. Moreover, we found that miR-520a could bind to ATAD2, and its inhibitor partly reversed the alterations in HUVEC induced by CM from ATAD2-silenced cells. In addition, we demonstrated that miR-520a directly bound to 3'-UTR of VEGFA and inhibited its expression. Collectively, our results indicate that ATAD2 inhibition suppresses VEGFA secretion by increasing miR-520a levels. Our study suggests ATAD2 as a potential therapeutic target for angiogenesis in colorectal cancer.
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 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".