🧫
IPCD signature identification and IPCDS model construction for ovarian cancer prognosis
active
experiment
Created: 2026-04-10T22:35:41
By: etl-v1-backfill
Quality:
50%
✓ SciDEX
ID: exp-0b345f4c-9407-40d3-8e83-f45d6b53ccba
🧫 Experiment Protocol
ExploratoryOvarian cancer88 IPCD-related genesHuman patients (TCGA, GEO, ICGC databases)proposed
This bioinformatics analysis aimed to identify immune-related programmed cell death (IPCD) signatures for predicting ovarian cancer prognosis. The researchers downloaded ovarian cancer datasets from TCGA, GEO, and ICGC databases. They screened prognostic genes from IPCD-related differential genes using univariate cox regression analysis. The construction of the IPCDS (immune-related programmed cell death signature) model was performed via 101 algorithm combinations to optimize predictive performance. The model's prognostic capability was validated across multiple datasets using Kaplan-Meier survival analysis and time-dependent ROC curves. The study found 88 IPCD-related prognostic genes that were used for modeling, with the low-IPCDS group showing significantly higher overall survival compared to the high-IPCDS group across most datasets.
PRIMARY OUTCOME
Overall survival prediction
EXPECTED OUTCOMES
Identification of prognostic signatures that can predict ovarian cancer patient survival
SUCCESS CRITERIA
Higher AUC values in timeROC analysis, significant survival differences between high and low IPCDS groups
PROTOCOL
Univariate cox regression analysis, 101 algorithm combinations for model construction, Kaplan-Meier analysis, timeROC curves
Source: PMID 41946148 ↗
🧫 Experiment Extras
PATHWAY
Immune-related programmed cell death pathways
MARKET PRICE
$0.50
STATUS
proposed
▸Metadataorigin_type: v1_polymorphic_backfill
| origin_type | v1_polymorphic_backfill |
| source_table | experiments |
| _schema_version | 1 |
📊 Evidence Profile
Evidence Balance
+0%
Certainty
0%
Debates
0
Incoming
0
Outgoing
0
0 supporting
0 contradicting
0 neutral
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