Abstract 4839: SNP-SNP interactions in mitochondria-related pathways are associated with invasive Epithelial Ovarian Cancer (EOC) risk .
Bibliographic record
Abstract
Abstract Background: Variation in mitochondrial genes in cancer cells alters the mitochondrial bioenergetic and biosynthetic state, which mediates cross-talk between mitochondria and the nucleus to modulate many cellular processes. The objective of this study was to comprehensively evaluate mitochondria-nuclear cross-talk in terms of SNP-SNP interactions associated with invasive epithelial ovarian cancer (EOC) risk, which may play an important role in unveiling the underlying mechanism of this complex lethal disease. Methods: Our analyses were based on 1,938 invasive EOC cases and 2,009 controls with greater than 80% European ancestry from five case-control studies genotyped using the Illumina 610quad chip. We evaluated 2,488 SNPs in over 1,500 genes in eight mitochondria-related pathways (apoptosis, oxidation-reduction, epithelial-mesenchymal transition, immunomoduation, transmembrane transport, small GTPases, BRCA1-interactors and circadian rhythm). SNPs with strong linkage disequilibrium of r2 > 0.8 were excluded. We examined 2-way SNP-SNP interactions associated with invasive EOC risk using the two-stage Random Forests plus Multivariate Adaptive Regression Splines (TRM) approach. Bootstrapping was further performed to select important variables and reduce false positive findings. Based on the scree plot using 1,000 bootstrap samples, the factors with more than 4% of bootstrap frequency were defined as significant factors. We then included each identified factors separately in model adjusting for study site and first principal component representing European ancestry. Results: Five significant SNP-SNP interactions were observed: (rs2270799 in IQSEC3 (12p13.33) + rs933305 in CUX2 (12q24.3), (p= 6.3x10−5); rs6063251 in PREX1 (20q13.3) + rs9705 in SLC39A8 (4q22-q24), (p= 3.5x10−5); rs933305 in CUX2 (12q24.3) + rs1712957 in ARHGAP15 (2q22.2-q22.3), (p=4.2x10−7), rs2861828 in SLIT1 (10q23.3-q24) + rs6063251 in PREX1 (20q13.3), (p=2.5x10−7); rs10954593 in SEMA3C (7q21-q31) + rs933305 in CUX2 (12q24.3), (p=2.8x10−3). Four of the five interactions involved SNPs from genes in the small GTPases pathway (IQSEC3; PREX1 (twice) and ARHGAP15). In addition, three of the five interaction pairs involved the CUX2 gene. This gene encodes a protein which contains three CUT domains and a homeodomain, which are DNA-binding motifs. The main mitochondrial SNP-SNP interaction involved the nuclear mitochondria gene SLC39A8. Conclusions: Our results suggest that genetic variation in mitochondrial-related pathways may be associated with invasive EOC risk. These findings highlight important genes/pathways that may interact as a network to influence invasive EOC risk and may generate novel therapeutic targets. External validation will be done using other independent studies for future analyses. Citation Format: Hui-Yi Lin, Ya-Yu Tsai, Y. Ann Chen, Zhihua Chen, Xiaotao Qu, Ellen L. Goode, Joellen Schildkraut, Edwin Iversen, Steven A. Narod, Thomas A. Sellers, Catherine M. Phelan. SNP-SNP interactions in mitochondria-related pathways are associated with invasive Epithelial Ovarian Cancer (EOC) risk . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4839. doi:10.1158/1538-7445.AM2013-4839
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".