Multi-omics analysis to screen potential therapeutic biomarkers for anti-cancer compounds

Area of Science:

• Oncology
• Pharmacogenomics
• Bioinformatics

Background:

• Identifying biomarkers for anti-cancer therapy response, including traditional Chinese medicine (TCM), is crucial in cancer research.
• Multi-omics analysis offers a powerful strategy for discovering potential therapeutic biomarkers by integrating diverse biological data.

Purpose of the Study:

• To screen potential therapeutic biomarkers for anti-cancer compounds derived from TCM using multi-omics data analysis.
• To identify molecules that can serve as indicators for predicting patient response to TCM-based cancer treatments.

Main Methods:

• Collected TCM compounds from public databases and filtered their targeted molecules from drug bioactivity datasets.
• Performed comprehensive multi-omics analyses, including gene mutation, differential gene expression, copy number variation, and pan-cancer survival analysis.
• Utilized sophisticated computational methods to analyze integrated omics data for biomarker discovery.

Main Results:

• Identified 13 molecules (ERBB2, MYC, FLT4, TEK, GLI1, TOP2A, PDE10A, SLC6A3, GPR55, TERT, EGFR, KCNA3, and HDAC4) as potential therapeutic biomarkers for TCM compounds.
• These biomarkers exhibit differential expression, high mutation frequency, significant copy number variations, and a strong correlation with patient survival across various cancers.
• The identified molecules are implicated in key cellular pathways relevant to cancer progression and treatment response.

Conclusions:

• The 13 identified molecules are promising biomarkers for predicting the efficacy of TCM-based anti-cancer therapies.
• Multi-omics analysis provides a robust framework for discovering novel biomarkers in precision oncology.
• Further validation is warranted to translate these findings into clinical applications for personalized cancer treatment strategies.