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High grade serous ovarian cancer differentiation and invasion post chemotherapy: Role of NRF2.

2025· article· en· W4410811898 on OpenAlexaff
Chelsea Katz, Helen Toma, Yaas Azmoudeh, Nasrine Bendjilali, Huseyin Karaduman, Hadi Shojaei, Gord Guo Zhu, Lauren Krill, Christina Chu, David Warshal, Yemin Wang, Samera H. Hamad

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSerous ovarian cancerMedicineOvarian cancerChemotherapySerous fluidOncologyCancerInternal medicineCancer research

Abstract

fetched live from OpenAlex

e17611 Background: Epithelial ovarian cancer accounts for 90% of ovarian cancer diagnoses, about 70% of which are high grade serous ovarian cancer (HGSOC). HGSOC is the most aggressive type of ovarian cancer with limited therapeutic options. The Cancer Genome Atlas (TCGA) and other studies have shown that several molecular subtypes of HGSOC exist such as immunoreactive, differentiated, proliferative, and mesenchymal, which further complicates the effort to find therapies for this deadly cancer. HGSOC is still treated as a single disease with a combination of surgery and systemic platinum-based chemotherapy being standard initial therapy. The expression of NRF2, an antioxidant gene and anti-inflammatory pathway, is altered in about 50% of HGSOC, but its significance is unclear. In this project, we aimed to investigate the NRF2-chemotherapy interaction and the impact on treatment. Methods: We used two NRF2 Low human HGSOC cell lines, OVCAR8, and ES-2. We induced the expression of NRF2 pathway through the activation of E79Q mutation, one of the most common mutations of NRF2 found in human tumors, using pInducer20 system. Cells were treated with carboplatin or paclitaxel. Doxycycline induced the expression of NRF2 E79Q which resulted in high levels of all down stream targets of NRF2. Both cell lines with and without NRF2 expression were also treated with carboplatin or paclitaxel and differentiation markers and downstream targets were evaluated using western blot protein quantification. Results: NRF2 activation changed the differentiation markers that represent squamous (P63), growth (C-MYC), immune checkpoint (PDL-1), and epithelial to mesenchymal transition (EMT) pathways in both cell lines. Interestingly, treatment with carboplatin or paclitaxel changed NRF2 protein levels and impacted differentiation markers. Further, scratch testing showed that NRF2 Low (OVCAR8 and ES-2) cell lines treated with paclitaxel demonstrated more rapid invasion, compared to those exposed to carboplatin and no treatment (which showed similar activity). These results were different in the NRF2 High versions of OVCAR8 and ES-2, where we observed slower invasion with both carboplatin and paclitaxel treated cells compared to untreated cells. Conclusions: Our results suggest that NRF2-chemotherapy interaction may result in pathways that impact the differentiation and invasion of HGSOC.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.027
GPT teacher head0.369
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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