Novel regulatory and transcriptional networks associated with resistance to platinum-based chemotherapy in ovarian cancer
Bibliographic record
Abstract
Abstract Background 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 elucidate the biological mechanisms underlying chemotherapy resistance, which remain poorly understood. Methods Sequencing data (mRNA and microRNA) from HGSOC patients were analyzed to identify differentially expressed genes and co-expressed transcript networks associated with chemotherapy response. Initial analyses used datasets from The Cancer Genome Atlas and then replicated in two independent cancer cohorts. Moreover, transcript expression datasets and genomics data (i.e. single nucleotide polymorphisms) were integrated to determine potential regulation of the associated mRNA networks by microRNAs and expression quantitative trait loci (eQTLs). Results In total, 196 differentially expressed mRNAs were enriched for adaptive immunity and translation, and 21 differentially expressed microRNAs were associated with angiogenesis. Moreover, co-expression network analysis identified two mRNA networks associated with chemotherapy response, which were enriched for ubiquitination and lipid metabolism, as well as three associated microRNA networks enriched for lipoprotein transport and oncogenic pathways. In addition, integrative analyses revealed potential regulation of the mRNA networks by the associated microRNAs and eQTLs. Conclusion We report novel transcriptional networks and pathways associated with resistance to platinum-based chemotherapy among HGSOC patients. These results aid our understanding of the effector networks and regulators of chemotherapy response, which will improve drug efficacy and identify novel therapeutic targets for 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 | 0.000 |
| 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) | 0.001 | 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".