Cat-E: A comprehensive web tool for exploring cancer targeting strategies.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Analyzing diverse omics data for cancer gene and drug target identification is complex, time-consuming, and requires specialized expertise.
• Existing methods often lack integration across different data types and analytical approaches.

Purpose of the Study:

• To develop a user-friendly web tool, Cat-E (Cancer Target Explorer), for comprehensive analysis and evaluation of cancer-related omics data.
• To facilitate the identification of potential cancer-associated genes and therapeutic targets for researchers with varying computational backgrounds.

Main Methods:

• Developed Cat-E as an R/Shiny web tool integrating data from OvirusTB, TCGA, DrugBANK, and PubChem.
• Implemented diverse analytical functionalities including differential gene expression, pathway analysis, survival analysis, and network analysis.
• Enabled users to analyze integrated datasets or upload their own data for personalized investigations.

Main Results:

• Cat-E provides a unified platform for analyzing multiple omics datasets, including gene expression, mutations, and clinical data.
• Demonstrated the tool's utility through an evaluation on lung adenocarcinoma (LUAD) datasets, identifying potential cancer targets.
• The tool successfully integrates various analyses, from gene expression dynamics to protein structure prediction.

Conclusions:

• Cat-E significantly lowers the computational barrier for experimental biologists, students, and clinicians to explore cancer omics data.
• The tool serves as a valuable resource for discovering novel diagnostic markers and therapeutic strategies across various cancer types.
• Cat-E enhances the accessibility and efficiency of cancer target discovery from complex molecular datasets.