Abstract 827: Transcriptome-wide association study of endometrial cancer risk
Bibliographic record
Abstract
Abstract Background Endometrial cancer is the most common malignancy of the female reproductive system, and the third leading cause of cancer death among women. In contrast to other common cancers, endometrial cancer is one of the few caners with a rapidly increasing incidence rate but a decreasing survival rate. To date, genome-wide association studies (GWAS) have identified 17 risk loci for endometrial cancer among European descendants, however, much of its heritability remains unexplained. For many of the GWAS identified loci, the target genes remain elusive. Methods We performed a large transcriptome-wide association study to discover novel endometrial cancer risk loci and possible target genes by studying 12,906 endometrial cases and 108,979 controls of European ancestry included in the Endometrial Cancer Association Consortium, the Epidemiology of Endometrial Cancer Consortium, and UK Biobank. We applied gene expression predicting models established using data of mostly European descent in the Genotype-Tissue Expression Project (version 8) in uterus, vagina, and whole blood tissues with the newly developed joint-tissue imputation approach. We systematically assessed the associations between genetically predicted gene expression levels and endometrial cancer risk. For genes that were associated with endometrial cancer risk, we performed an enrichment analysis by using Ingenuity Pathway Analysis. Results Overall, we identified 26 genes whose genetically predicted expression was significantly associated with endometrial cancer risk (false discovery rate (FDR) < 0.05), including 15 genes at 9 novel loci (3q22.1: AC022296.4; 3q21.3: EEFSEC; 3q25.31: LEKR1; 6p21.32: HLA-DMA; 6p21.33: C4A, C4B, APOM, and PPP1R18; 16p11.2: AC109460.2, EIF3C, and NFATC2IP; 16p12.1: NPIPB6; 16p13.2: ATF7IP2; 20q11.22: GDF5 and UQCC1) and 11 genes at 5 known risk loci (14q32.33: SIVA1 and AL583810.1; 15q15.1: AC021755.2, AC021755.3 and EIF2AK4; 15q21.2: CYP19A1 and TNFAIP8L3; 17q11.2: EVI2A; 17q21.32: SKAP1, SNX11 and AC004477.3). Of them, AC021755.3 (P-value = 1.77 × 10-6), EIF2AK4 (P-value = 1.07 × 10-7), and CYP19A1 (P-value = 1.61 × 10-9) showed an association even at the stringent Bonferroni threshold (P-value < 2 × 10-6). Pathway enrichment analysis showed that the identified genes were significantly enriched in cancer-related pathways, such as FXR/RXR activation, EIF2 signaling and LXR/RXR activation. Conclusion By integrating gene expression and genotype data, our comprehensive transcriptome-wide association study identified novel endometrial cancer risk loci and candidate target genes that warrant further investigation. Citation Format: Jingjing Zhu, Tracy A. O'Mara, Veronica W. Setiawan, Mengmeng M. Du, the ECAC consortium, Christine Friedenreich, Linda S. Cook, James Lacey, Chu Chen, Lisa Johnson, Immaculata De Vivo, Nicolas Wentzensen, Xiao-Ou Shu, Jirong Long, Wei Zheng, Lang Wu, Herbert Yu. Transcriptome-wide association study of endometrial cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 827.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".