Computational Advances in the Label-free Quantification of Cancer Proteomics Data

Area of Science:

• Proteomics
• Cancer Research
• Computational Biology

Background:

• Proteomics offers quantitative insights into tumor development and drug responses.
• It aids in identifying cancer targets and developing novel anticancer drugs.
• Characterizing protein expression is crucial for understanding malignancy.

Purpose of the Study:

• To systematically review and analyze computational advances in label-free quantification (LFQ) for cancer proteomics.
• To address the key technical challenges of low precision, poor reproducibility, and inaccuracy in LFQ.
• To enhance the discovery of anticancer targets and drugs.

Main Methods:

• Searched PubMed and Web of Science databases.
• Focused on label-free quantification approaches, cancer proteomics, and computational advances.
• Systematically reviewed and critically assessed acquisition techniques and quantification tools.

Main Results:

• Discussed various popular acquisition techniques and state-of-the-art quantification tools.
• Evaluated processing approaches (transformation, normalization, filtering, imputation) for improving LFQ performance.
• Proposed future directions for enhancing computation-based quantification in cancer proteomics.

Conclusions:

• There has been a dramatic increase in LFQ approaches in recent years.
• These advances significantly enhance quantification strategies for cancer proteomics.
• Improved computational methods are vital for advancing cancer drug discovery.