SmulTCan

SmulTCan performs multivariable survival analysis using Cox proportional hazards models to identify prognostic gene-set signatures from TCGA gene expression and survival data.

Key Features:

• Multivariable Cox proportional hazards models: Enables construction and evaluation of multivariable Cox proportional hazards (CPH) models for gene sets.
• Gene set analysis: Analyzes specific gene sets, such as netrins and their receptors (netrins-receptors), to identify survival gene signatures (GSs) predictive of patient prognosis.
• Best subset selection: Applies best subset selection methods to choose optimal subsets of genes, microRNAs, methylation levels, and copy number variations for prognostic modeling.
• Integration with TCGA and UCSC Xena: Uses TCGA survival data and gene expression files accessible via UCSC Xena for model input and validation.
• Analytical R packages: Utilizes R packages "survminer", "riskRegression", "rms", "glmnet", and "BeSS" for CPH modeling and best subset selection.
• Input formats: Accepts input files in TSV format for analysis.

Scientific Applications:

• Prognostic biomarker discovery: Identify gene-set signatures associated with hazard ratios and patient survival across cancers.
• Patient stratification: Stratify patients into risk groups using multivariable CPH models derived from gene sets.
• Cancer-specific signature detection: Detect prognostic gene signatures in specific cancers, including examples such as renal and neural cancers with the netrins-receptors gene set.

Methodology:

Accepts TSV input files and processes them using built-in R functions from survminer, riskRegression, rms, glmnet, and BeSS to build multivariable Cox proportional hazards models and perform best subset selection on TCGA survival and UCSC Xena gene expression data.