Abstract B07: Long noncoding RNAs underlying genetic predispositions to prostate cancer
Bibliographic record
Abstract
Abstract Trait-associated SNPs identified through Genome-Wide Association Studies are enriched in regulatory regions. However, the functional link between these SNPs and their target genes remains elusive. Due to their involvement in fundamental biological processes, long noncoding RNAs (lncRNAs) represent an attractive class of molecules mediating cancer risk. Through integrative analysis of the lncRNA transcriptome with genomic and prostate cancer risk SNP data, we identified 60 candidate lncRNAs associated with risk to prostate cancer. The mechanism underlying the top hit, PCAT1, was evaluated further. The risk variant at rs7463708 decreases HOXB13 and increases AR binding at a distal enhancer that loops to PCAT1 promoter, resulting in upregulation of PCAT1 upon prolonged androgen treatment. In addition, PCAT1 interacts with AR and LSD1 and is required for their recruitment to the enhancers of GNMT and DHCR24, two androgen late response genes implicated in prostate cancer development and progression. These findings suggest that modulating lncRNA expression is an important mechanism for risk SNPs in promoting prostate transformation. Note: This abstract was not presented at the conference. Citation Format: Haiyang Guo, Musaddeque Ahmed, Junjie Tony Hua, Yi Liang, Housheng Hansen He. Long noncoding RNAs underlying genetic predispositions to prostate cancer. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr B07.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".