Significant association of caveolin-1 (CAV1) genotypes with breast cancer in Taiwan.
Bibliographic record
Abstract
AIM: Japanese and American groups reported that single nucleotide variation of caveolin-1 gene (CAV1) plays an important role in breast cancer risk. The aim of this study was to evaluate the association of six polymorphic genotypes of CAV1, which is reported to be overexpressed in tumors, with breast cancer within a Taiwanese population. PATIENTS AND METHODS: A total of 1232 patients with breast cancer and equal number of healthy controls in central Taiwan were genotyped via polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) and six polymorphic variants of CAV1 were analyzed for their association with breast cancer susceptibility. RESULTS: The distribution of genotypes of CAV1 G14713A and T29107A were significantly different between breast cancer and control groups (p=5.6×10(-5) and 1.9×10(-4), respectively), while those for CAV1 C239A, G21985A, T28608A and G32124A were not significant (p>0.05). The percentages of AG genotype of G14713A and TT genotype of T29107A are higher in the cancer group than in the control group. The two single nucleotide polymorphisms were chosen for haplotype analysis and the data showed that compared with GG/TT haplotype of CAV1 G14713A/T29107A, the GG/AT and GG/AA groups have a lower risk of breast cancer (odds ratio, OR=0.69, 95% confidence interval, CI=0.57-0.92). On the contrary, the AG/TT haplotype confers a higher risk of breast cancer (OR=1.50, 95% CI=1.14-2.12). CONCLUSION: Our results provide evidence for CAV1 genotypes being involved in predisposition to breast cancer. The association of the potential risk haplotype agrees well with a role of CAV1 genotype in breast cancer risk and the association with tumor progression needs further investigation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".