A microRNA binding site polymorphism in the 3′ UTR region of VEGF-A gene modifies colorectal cancer risk based on ethnicity: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Vascular endothelial growth factor A (VEGF-A) plays an integral role in angiogenesis by contributing to growth, development, and metastasis of solid tumors. Recently, a single-nucleotide polymorphism +936C/T located in the VEGF-A 3' untranslated region (UTR) facilitated the susceptibility of colorectal cancer. The association between VEGF-A gene polymorphism +936C/T and colorectal cancer risk has been widely studied in the last decade, but presently, the results furnished remain enigmatic. Hence, the study aimed to investigate the association between VEGF-A +936C/T miRNA binding site polymorphism and the risk of developing colorectal cancer. METHODS: This meta-analysis included 13 published case-control studies covering 3465 cases (colorectal cancer) and 3476 healthy controls. Publication bias was examined by means of Begg's funnel plots and Egger's regression tests. The quality of the studies included was evaluated using Newcastle-Ottawa scale. Subgroup analyses were performed in accordance to the various ethnicities of the study subjects and the study quality. RESULTS: From the data obtained, it is implied that VEGF-A +936C/T polymorphism did not correlate with elevated colorectal cancer risk in all genetic models. But the results acquired from the subgroup analysis in over dominant model (CT vs. CC + TT: OR = 1.5047, 95% CI = 1.19-1.90) suggest that VEGF-A +936C/T polymorphism leads to the raise in the risk of developing CRC among the East Asian population. No association was observed in Caucasian and South Asian population. CONCLUSIONS: Our results indicate that VEGF-A +936C/T polymorphism is not a risk factor for developing CRC in Caucasian and South Asian population. However, the East Asian population was related to an increased risk of developing colorectal cancer due to the presence of the minor allele.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.035 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".