Targeting Ovarian Cancer Stem Cells by Dual Inhibition of the Long Noncoding RNA HOTAIR and Lysine Methyltransferase EZH2
Bibliographic record
Abstract
The persistence of cancer stem cells (CSC) is believed to contribute to resistance to platinum-based chemotherapy and disease relapse in ovarian cancer, the fifth leading cause of cancer-related death among US women. HOXC transcript antisense RNA (HOTAIR) is a long, noncoding RNA (lncRNA) overexpressed in high-grade serous ovarian cancer and linked to chemoresistance. However, HOTAIR impacts chromatin dynamics in ovarian CSCs. Oncogenic lncRNA's contributions to drug-resistant disease are incompletely understood. Here, we generated HOTAIR knockout (KO) high-grade serous ovarian cancer cell lines using paired CRISPR guide RNA design to investigate the function of HOTAIR. We show the loss of HOTAIR function resensitized ovarian cancer cells to platinum treatment and decreased the population of ovarian CSCs. Furthermore, HOTAIR KO inhibited the development of stemness-related phenotypes, including spheroid formation ability and expression of key stemness-associated genes ALDH1A1, NOTCH3, SOX9, and PROM1. HOTAIR KO altered the cellular transcriptome and chromatin accessibility landscape of multiple oncogenic-associated genes and pathways, including the NF-kB pathway. HOTAIR functions as an oncogene by recruiting enhancer of zeste homolog 2 (EZH2) to catalyze H3K27 trimethylation to suppress downstream tumor suppressor genes, and it was of interest to inhibit both HOTAIR and EZH2. In vivo, combining a HOTAIR inhibitor with an EZH2 inhibitor and platinum chemotherapy decreased tumor formation and increased survival. These results suggest a key role for HOTAIR in ovarian CSCs and malignant potential. Targeting HOTAIR in combination with epigenetic therapies may represent a therapeutic strategy to ameliorate ovarian cancer progression and resistance to platinum-based chemotherapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".