MITO-OC: Ovarian Cancer Prognosis Model Based on Mitochondrial-Related Genes

TCGA, ICGC, and GEO data were used to build a prognostic model for ovarian cancer. The MITO-OC model was constructed using ten machine-learning algorithms and is based on 241 differentially expressed mitochondria-related genes. The dataset, last updated in April 2026, was created by Ke Xu and includes a model with a reported C-index of 0.73.

Use Cases

• Predicting overall survival for ovarian cancer patients based on the MITO-OC model.
• Analyzing correlations between prognosis and tumor immune infiltration based on the model's features.
• Investigating the role of the CHCHD2 gene in ovarian and other cancers based on single-cell and pan-cancer analyses.
• Developing personalized prognostic tools based on mitochondrial-related gene expression.

Strengths

• Model built using ten machine-learning algorithms on data from three major sources (TCGA, ICGC, GEO).
• Model performance reported with a C-index of 0.73.
• Includes single-cell and pan-cancer analyses for the CHCHD2 gene.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

TCGA, ICGC, and GEO data sources.

Collection Method

Constructed using ten machine-learning algorithms, including StepCox[forward] and RSF.

Time Range

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Freshness

Last updated 2026-04-15 09:20:35; freshness should be verified.

Geography

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