SNPs in lncRNA Regions and Breast Cancer Risk
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) play crucial roles in human physiology, and have been found to be associated to various cancers. Transcribed ultraconserved regions (T-UCRs) are a subgroup of lncRNAs conserved in several species and are often located in cancer-related regions. Breast cancer is the most common cancer in women worldwide and the leading cause of female cancer deaths. We investigated the association of genetic variants in lncRNA and T-UCR regions with breast cancer risk to uncover candidate loci for further analysis. Our focus was on low-penetrance variants that can be discovered in a large data set. We selected 565 regions of lncRNAs and T-UCRs that are expressed in breast or breast cancer tissue, or show expression correlation to major breast cancer associated genes. We studied the association of SNPs in these regions with breast cancer risk in the 122970 case samples and 105974 controls of the Breast Cancer Association Consortium’s genome wide data and also by in silico functional analyses using Integrated Expression Quantitative trait and in silico prediction of GWAS targets (INQUISIT) and expression quantitative trait loci (eQTL) analysis. The eQTL analysis was carried out using METABRIC dataset and analyses from GTEx and ncRNA eQTL databases. We found putative breast cancer risk variants (p<1x10-5) targeting the lncRNA GABPB1-AS1 in INQUISIT and eQTL analysis. In addition, putative breast cancer risk associated SNPs (p<1x10-5) in the region of two T-UCR:s, uc.184 and uc.313 located in protein coding genes CPEB4 and TIAL1, respectively, targeted these genes in INQUISIT and in eQTL analysis. Other noncoding regions containing SNPs with the defined p-value and highly significant FDR for breast cancer risk association were discovered that may warrant further studies. These results suggest candidate lncRNA loci for further research on breast cancer risk and the molecular mechanisms.
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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) | 0.000 | 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".