KSCV

KSCV quantifies the stemness level of kidney renal clear cell carcinoma (KIRC) samples from RNA-Seq data by integrating gene expression and alternative splicing information.

Key Features:

• Stemness Quantification: Calculates a stemness index for KIRC patients by integrating gene expression with abnormal alternative splicing patterns identified in RNA-Seq datasets.
• One-Class Logistic Regression Model: Constructs a prediction model using one-class logistic regression that leverages both expression and alternative splicing data.
• Identification of Stemness-Associated Alternative Splicing Events (SASEs): Detects SASEs by comparing high- and low-stemness groups and correlates events with patient outcomes and splicing factors.
• Prognostic Analysis: Performs univariate Cox and multivariable logistic regression analyses to identify prognosis-related SASEs and assess their impact on overall survival.
• Predictive Performance: Evaluates model performance with receiver operating characteristic (ROC) analysis reporting an area under the curve (AUC) of 0.968.

Scientific Applications:

• Clinical Correlation: Associates stemness indices with clinical parameters including gender, smoking history, and metastasis in KIRC patients.
• Intratumor Heterogeneity: Reveals intratumor heterogeneity at the stemness level within KIRC samples.
• Immunotherapy Potential: Highlights differential alternative splicing events associated with poor prognosis that may inform immunotherapy target discovery.

Methodology:

Uses RNA-Seq data from The Cancer Genome Atlas (TCGA) and validation datasets from the Gene Expression Omnibus (GEO), integrating gene expression and alternative splicing data to build a one-class logistic regression model, identify SASEs, perform univariate Cox and multivariable logistic regression analyses, and evaluate performance by ROC AUC (0.968).