Integrative Transcriptomic Meta-Analysis Reveals Risk Signatures and Immune Infiltration Patterns in High-Grade Serous Ovarian Cancer
Bibliographic record
Abstract
Background: High-grade serous ovarian cancer (HGSOC) is a highly aggressive malignancy with poor prognosis due to late-stage diagnosis and limited treatments. Identifying differentially expressed genes (DEGs), and immune cell infiltration patterns may improve prognostic assessment and therapeutic strategies. Methods: We conducted a meta-analysis of gene expression data from the GEO (Gene Expression Omnibus, NCBI). DEGs were identified, functionally enriched, and analyzed for protein-protein interactions. Overlaps with oncogenes and tumor suppressor genes were examined. Cox survival analysis and a gene expression-based risk stratification model were developed. Immune infiltration differences were assessed using deconvolution methods. Results: A total of 11 studies (291 HGSOC, 96 controls) identified 892 DEGs, mainly involved in mitochondrial function, vesicle trafficking, and immune regulation. Key oncogenes (EZH2, PDK1, ERBB2) and tumor suppressor genes (BRCA1, DUSP22) were identified. Survival analysis associated the expression of SEC24B, TGOLN2, TRAK1, and CAST with poor prognosis. Low-risk patients had higher activated dendritic cells and CD4+ memory T cells while high-risk patients were enriched in common lymphoid progenitors and megakaryocyte-erythroid progenitors. Conclusions: This study identifies key DEGs in HGSOC progression and presents a risk stratification model predicting patient outcomes.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".