Advanced Expression Survival Analysis (AESA)

Advanced Expression Survival Analysis (AESA) performs composite gene-set survival analyses to derive prognostic signatures from TCGA transcriptome and clinical datasets.

Key Features:

• Composite Gene Expression Scoring: Calculates a composite score for sets of genes to enhance predictive power in survival analysis.
• Novel Statistical Approaches: Applies permutation tests and cross-validation to assess the significance of log-rank statistics and evaluate potential over-fitting.
• Survival Feature Selection with Post-Selection Inference: Implements feature selection methods with post-selection statistical inference for identified survival-related genes.
• Expanded Clinical Data Utilization: Integrates TCGA clinical endpoints including overall survival, disease-specific survival, disease-free survival, and progression-free survival.
• Versatile Transcriptome Analysis: Analyzes both protein-coding and non-coding regions of the transcriptome.
• Proven Effectiveness with Non-Coding RNAs: Empirical results indicate non-coding RNAs perform comparably to mRNAs in predicting cancer patient survival.

Scientific Applications:

• Prognostic biomarker identification: Identify expression-based prognostic biomarkers across TCGA cancer types, including ACC, BLCA, and BRCA.
• Coding versus non-coding RNA comparison: Evaluate and compare the prognostic value of protein-coding RNAs and non-coding RNAs.
• Model validation and robustness assessment: Validate survival signatures and assess overfitting using permutation tests and cross-validation of log-rank statistics.

Methodology:

Utilizes TCGA gene expression datasets; computes composite gene-set scores; evaluates significance with log-rank statistics, permutation tests, and cross-validation; performs feature selection with post-selection inference; and incorporates overall survival, disease-specific survival, disease-free survival, and progression-free survival while analyzing protein-coding and non-coding transcriptome regions.