Oxidative Phosphorylation Gene Signature for Ovarian Cancer Prognosis and Drug Response

An oxidative phosphorylation-related gene signature (OPRGS) constructed using random survival forest and supervised principal components methods from TCGA, GTEx, and GEO data. The 407 KB Excel file, authored by Luyao Kang and last updated in May 2026, presents a risk score associated with prognosis, immune infiltration, and chemotherapy sensitivity in ovarian cancer. This signature was derived from gene set enrichment analysis of 84 metabolic pathways from KEGG.

Use Cases

• Predicting patient prognosis based on the oxidative phosphorylation-related gene signature (OPRGS) risk score.
• Analyzing correlations between tumor immune microenvironment features, such as ESTIMATE stromal scores and immune checkpoint markers, and the OPRGS.
• Investigating associations between cancer stemness indices and the oxidative phosphorylation signature.
• Modeling drug sensitivity, specifically to paclitaxel and carboplatin, using the OPRGS risk stratification.
• Identifying key genes like KIF1A for further biological validation in ovarian cancer cell proliferation and migration.

Strengths

• Integrates data from multiple authoritative genomic sources: TCGA, GTEx, and GEO datasets.
• Applies specific machine learning methodologies: robust rank aggregation (RRA), random survival forest (RSF), and supervised principal components (SuperPC).
• Links the gene signature to multiple clinical and biological endpoints: prognosis, immune infiltration, drug sensitivity, and cancer stemness.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect bias inherent to the specific genomic cohorts used from figshare.

Provenance

Source

Cancer Genome Atlas Program (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) datasets.

Collection Method

Gene set enrichment analysis (GSEA) on 84 KEGG metabolic pathways, followed by robust rank aggregation and machine learning model construction.

Freshness

Last updated 2026-05-29 06:07:16; freshness should be verified.