Global DNA methylation signatures associated with chemoresistance and poor prognosis of high grade serous ovarian cancer
Bibliographic record
Abstract
Ovarian cancer (OVCA) is third most lethal gynecologic cancers and acquired chemoresistance is the key link in the high mortality rate of OVCA patients. Currently, there are no reliable methods to predict chemoresistance in OVCA. In our study, we identify genes, pathways and networks altered by DNA methylation in high-grade serous ovarian carcinoma (HGSC) cells that are associated with chemoresistance and prognosis of HGSC patients. We performed methylome-wide profiling using Illumina Infinium MethylationEPIC BeadChip (HM850K) methylation array on a set of HGSC chemoresistant and chemosensitive cell lines. Differentially Methylated CpG Probes (DMPs) were identified between the resistant and sensitive groups in HGSC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) over-representation analyses were conducted to identify both common and unique pathways between resistant and sensitive cells. While the HM850K array was used for the discovery phase to identify differentially methylated probes and regions in HGSC cell lines, the publicly available The Cancer Genome Atlas ovarian cancer (TCGA-OV) dataset generated using the Illumina Infinium HumanMethylation27 BeadChip (27 K array) methylation array served as an independent validation cohort for downstream survival and drug sensitivity analyses. Machine learning methods were applied to our dataset to predict drug sensitivity in the TCGA-OV cohort and to investigate associations with overall survival and progression-free survival. Kaplan-Meier analysis was performed to assess the relationship between differentially methylated genes and patient survival outcomes. The overlapping CpG probes shared between the two Illumina platforms were used for machine learning and survival analyses. Data visualization was performed using various R/Bioconductor packages. Our analysis identified a total of 3,641 DMPs spanning 1,617 differentially methylated genes between chemoresistant and sensitive HGSC cells, whereas 80% of them were hypermethylated CpG sites associated with HGSC resistant cells. Approximately half of the DMPs were distributed on chromosomes 1-3, 6, 11-12 and 17 and top identified hypermethylated CpGs were cg21226224 (SOX17, ∆β = 79%, adj.P = 7.73E-03), cg02538901 (ATP1A1, ∆β = 75%, adj.P = 7.6E-03), and cg17032184 (CD58, ∆β = 64%, adj.P = 4.39E-02). Machine learning analysis identified significant association of global hypermethylation in the HGSC chemoresistant cells with poor overall and progression-free survival of HGSC patients. Further analysis identified four differentially methylated genes (CD58, SOX17, FOXA1, ETV1) that were also positively associated with poor prognosis of HGSC OC patients. Functional enrichment analysis showed enrichment of several cancer-related pathways, including phosphatidylinositol signaling, homologous recombination and ECM-receptor interaction pathways. This study supplements the current knowledge of the underlying mechanism behind acquired chemoresistance in OVCA. Four differentially methylated genes identified in this study may have the potential to serve as promising epigenetic clinical biomarkers for HGSC chemotherapy 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 | 0.000 | 0.001 |
| 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".