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Over the past two decades, quantitative proteomics has emerged as an important tool for deciphering the complex molecular events involved in cancers. The number of references involving studies on the cancer metastatic process has doubled since 2010, while the last 5 years have seen the development of novel technologies combining deep proteome coverage capabilities with quantitative consistency and accuracy. To highlight key findings within this huge amount of information, the present review identified a list of tumor invasive biomarkers based on both the literature and data collected on a biocollection of experimental cell lines, tumor models of increasing invasiveness and tumor samples from patients with colorectal or breast cancer. Crossing these different data sources led to 76 proteins of interest out of 1,245 mentioned in the literature. Information on these proteins can potentially be translated into clinical prospects, since they represent potential targets for the development and evaluation of innovative therapies, alone or in combination. Herein, a systematical review of the biology of each of these proteins, including their specific subcellular/extracellular or multiple localizations is presented. Finally, as an important advantage of quantitative proteomics is the ability to provide data on all these molecules simultaneously in cell pellets, body fluids or paraffin‑embedded sections of tumors/invaded tissues, the significance of some of their interconnections is discussed.
Proteomics, [SDV.CAN]Life Sciences [q-bio]/Cancer, Articles, Disease Models, Animal, [SDV.CAN] Life Sciences [q-bio]/Cancer, Cell Line, Tumor, Neoplasms, Biomarkers, Tumor, Animals, Humans, Neoplasm Invasiveness
Proteomics, [SDV.CAN]Life Sciences [q-bio]/Cancer, Articles, Disease Models, Animal, [SDV.CAN] Life Sciences [q-bio]/Cancer, Cell Line, Tumor, Neoplasms, Biomarkers, Tumor, Animals, Humans, Neoplasm Invasiveness
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 23 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |