Background: Due to its ability to provide quantitative and dynamic information on tumor genesis and
development by directly profiling protein expression, the proteomics has become intensely popular for characterizing
the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations
induced by anticancer drug, and discovering the innovative targets and first-in-class drugs for oncologic disorders.
Objective: To quantify cancer proteomics data, the label-free quantification (LFQ) is frequently employed. However,
low precision, poor reproducibility and inaccuracy of the LFQ of proteomics data have been recognized as
the key “technical challenge” in the discovery of anticancer targets and drugs. In this paper, the recent advances
and development in the computational perspective of LFQ in cancer proteomics were therefore systematically
reviewed and analyzed.
Methods: PubMed and Web of Science database were searched for label-free quantification approaches, cancer
proteomics and computational advances.
Results: First, a variety of popular acquisition techniques and state-of-the-art quantification tools are systematically
discussed and critically assessed. Then, many processing approaches including transformation, normalization,
filtering and imputation are subsequently discussed, and their impacts on improving LFQ performance of
cancer proteomics are evaluated. Finally, the future direction for enhancing the computation-based quantification
technique for cancer proteomics are also proposed.
Conclusion: There is a dramatic increase in LFQ approaches in recent year, which significantly enhance the
diversity of the possible quantification strategies for studying cancer proteomics.