TTSurv

TTSurv correlates coding and noncoding genes with thoracic cancers (including lung cancer, esophageal cancer, and breast cancer) to identify gene–outcome associations using high-throughput genomic data integrated with clinical prognosis.

Key Features:

• Integration of High-Throughput Genomic Data: Analyzes public expression profile data from high-throughput studies to assess coding and noncoding gene expression.
• Detection of Noncoding RNAs: Identifies noncoding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs, from genomic expression data.
• Comprehensive Analysis with Clinical Data: Integrates expression profiles with clinical follow-up information to correlate gene expression patterns with patient outcomes.
• Minimum p-value Algorithm: Employs the Minimum p-value Algorithm to detect statistically significant associations between genes and cancer prognosis.
• Unsupervised Clustering Methods: Applies unsupervised clustering to classify thoracic cancer samples into distinct risk groups based on gene expression profiles.

Scientific Applications:

• Biomarker Identification: Identifies potential prognostic and diagnostic biomarkers for thoracic malignancies.
• ncRNA Functional Studies: Elucidates the prognostic and diagnostic relevance of lncRNAs and microRNAs in cancer biology.
• Patient Stratification: Correlates genomic and clinical data to support stratification of patients into risk groups.
• Support for Targeted Therapy Development: Provides gene–outcome associations that can inform development of targeted therapies.

Methodology:

Integrates public expression profile data with clinical follow-up information, detects ncRNAs (lncRNAs and microRNAs) from high-throughput genomic data, applies the Minimum p-value Algorithm for association testing, and uses unsupervised clustering to classify samples into risk groups.