NRF2 DLG Domain Mutations Identified in Japanese Liver Cancer Patients Affect the Transcriptional Activity in HCC Cell Lines
Bibliographic record
Abstract
Geographically, East Asia had the highest liver cancer burden in 2017. Besides this, liver cancer-related deaths were high in Japan, accounting for 3.90% of total deaths. The development of liver cancer is influenced by several factors, and genetic alteration is one of the critical factors among them. Therefore, the detailed mechanism driving the oncogenic transformation of liver cells needs to be elucidated. Recently, many researchers have focused on investigating the liver cancer genome and identified somatic mutations (MTs) of several transcription factors. In this line, next-generation sequencing of the cancer genome identified that oxidative stress-related transcription factor NRF2 (NFE2L2) is mutated in different cancers, including hepatocellular carcinoma (HCC). Here, we demonstrated that NRF2 DLG motif mutations (NRF2 D29A and L30F), found in Japanese liver cancer patients, upregulate the transcriptional activity of NRF2 in HCC cell lines. Moreover, the transcriptional activity of NRF2 mutations is not suppressed by KEAP1, presumably because NRF2 MTs disturb proper NRF2-KEAP1 binding and block KEAP1-mediated degradation of NRF2. Additionally, we showed that both MTs upregulate the transcriptional activity of NRF2 on the MMP9 promoter in Hepa1-6 and Huh7 cells, suggesting that MT derived gain-of-function of NRF2 may be important for liver tumor progression. We also found that ectopic overexpression of oncogenic BRAF WT and V600E increases the transcriptional activity of NRF2 WT on both the 3xARE reporter and MMP9 promoter. Interestingly, NRF2 D29A and L30F MTs with oncogenic BRAF V600E MT synergistically upregulate the transcription activity of NRF2 on the 3xARE reporter and MMP9 promoter in Hepa1-6 and Huh7 cells. In summary, our findings suggest that MTs in NRF2 have pathogenic effects, and that NRF2 MTs together with oncogenic BRAF V600E MT synergistically cause more aberrant transcriptional activity. The high activity of NRF2 MTs in HCC with BRAF MT warrants further exploration of the potential diagnostic, prognostic, and therapeutic utility of this pathway in HCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".