Radial Data Visualization-Based Step-by-Step Eliminative Algorithm to Predict Colorectal Cancer Patients' Response to...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Colorectal cancer (CRC) patients often develop chemo-resistance to FOLFOX treatment, leading to therapy failure.
• Predicting patient response to FOLFOX is crucial for effective treatment strategies.

Purpose of the Study:

• To develop a functional and user-friendly algorithm for predicting patient response to FOLFOX chemotherapy in CRC.
• To identify key gene expression markers associated with FOLFOX resistance.

Main Methods:

• Downloaded transcriptomic data from Gene Expression Omnibus (GEO) for CRC patients treated with FOLFOX.
• Analyzed gene expression differences between FOLFOX responders and non-responders to identify potential markers.
• Developed a step-by-step predictive algorithm using modified radial data visualization.

Main Results:

• Identified specific gene expression patterns in FOLFOX-resistant CRC samples, including upregulated TMEM182 and MCM9, and downregulated LRRFIP1.
• The developed algorithm, utilizing 14 gene markers, demonstrated capability in predicting FOLFOX therapy response.
• Highlighted the interplay of gene expression levels in predicting treatment outcomes.

Conclusions:

• The developed algorithm offers valuable insights for clinical decision-making in CRC treatment.
• Accurate prediction of FOLFOX response can potentially improve patient survival and reduce treatment-related futility.
• This approach aids in personalized therapy administration for colorectal cancer.