Metadata Analysis to Get Insight into Drug Resistant Ovarian Cancer
Bibliographic record
Abstract
The most prevalent kind of ovarian cancer is high-grade serous ovarian cancer.Drug resistance is the major issue in this cancer.Transcriptional fusions involving SLC25A40-ABCB1 is a leading cause of this cancer.To understand the phenotypic consequences, transcriptional profile was studied using high throughput sequencing technologies.Here we have used that data to understand co-expressed genes and their functional role in two different cell types, fusion positive and fusion negative using WGCNA analysis.The major biological processes which are correlated with fusion positive cells are extracellular structure organization, external encapsulating structure organization, regulation of cell migration and axon guidance etc.In addition to these investigations, gene expression data of a PARPi-sensitive cell line and resistance was analyzed to determine the role and capabilities of PARP-inhibitors in controlling drug-resistant High-grade serous ovarian cancer.This investigation also shed light on the possible mechanism of PARPi resistant cases and concluded that the resistance comes from the dynamics of four biological processes like regulation of cell junction assembly, cell-cell adhesion, tissue morphogenesis, neuron projection development and negative regulation of cellular component organization.Further analysis with different Gene Set Enrichment analysis illustrates that four processes, negative regulation of lens fiber cell differentiation, sarcoplasmic reticulum lumen, presynaptic membrane assembly and nitrobenzene metabolic process are activated in PARPi resistance.These processes are connected to each other through an important kinase protein ERBB2 which is interpreted as a key protein in PARPi resistance.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".