A missense polymorphism in <i>ATF6</i> gene is associated with susceptibility to hepatocellular carcinoma probably by altering ATF6 level
Bibliographic record
Abstract
Accumulated evidences indicate that single nucleotide polymorphisms (SNP) are associated with risk of hepatocellular carcinoma (HCC). Activating transcription factor 6 (ATF6) is an important modulator of the unfolded protein response (UPR), which is regarded to be involved in carcinogenesis. So we speculate that SNPs in ATF6 may be associated with susceptibility to HCC. We carried out a two-stage association study in three independent case-control groups in a total of 1,082 chronic hepatitis B (CHB) patients and 816 hepatitis B virus (HBV) related HCC patients in Han Chinese. Four SNPs which can represent all potential functional SNPs with MAF > 0.1 recorded in HapMap database in ATF6 gene were genotyped using TaqMan methods. Functional analyses were conducted to verify the biological significances of the associated SNP. We identified a missense SNP (rs2070150) was significantly associated with susceptibility to HCC (p = 0.008, 0.001 and 0.007 in Beijing_302, Beijing_You'an and Guangxi samples, respectively). This SNP was further validated in four independent groups of major HBV outcomes, indicating it may associate exclusively to HCC. ATF6 mRNA expression was significantly decreased as the disease progressed (p <0.001). Functional analyses show that the protective allele of rs2070150 could significantly increase the expression levels of ATF6 mRNA, as well as ATF6 regulated genes such as GRP78, XBP1 and CHOP. These findings indicate that a common missense SNP in ATF6 may contribute to susceptibility of HCC functionally.
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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 | 0.001 |
| 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 | 0.000 | 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".