Novel MicroRNA-Regulated Transcript Networks Are Associated with Chemotherapy Response in Ovarian Cancer
Bibliographic record
Abstract
High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecologic cancer, in part due to resistance to platinum-based chemotherapy reported among 20% of patients. This study aims to generate novel hypotheses of the biological mechanisms underlying chemotherapy resistance, which remain poorly understood. Differential expression analyses of mRNA- and microRNA-sequencing data from HGSOC patients of The Cancer Genome Atlas identified 21 microRNAs associated with angiogenesis and 196 mRNAs enriched for adaptive immunity and translation. Coexpression network analysis identified three microRNA networks associated with chemotherapy response enriched for lipoprotein transport and oncogenic pathways, as well as two mRNA networks enriched for ubiquitination and lipid metabolism. These network modules were replicated in two independent ovarian cancer cohorts. Moreover, integrative analyses of the mRNA/microRNA sequencing and single-nucleotide polymorphisms (SNPs) revealed potential regulation of significant mRNA transcripts by microRNAs and SNPs (expression quantitative trait loci). Thus, we report novel transcriptional networks and biological pathways associated with resistance to platinum-based chemotherapy in HGSOC patients. These results expand our understanding of the effector networks and regulators of chemotherapy response, which will help to improve the management of ovarian cancer.
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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 | 0.000 | 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) | 0.001 | 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".