Pioneering Proteomics in Oncology

Understanding peptides is central to pushing the boundaries of proteomics research. Here’s why they matter:

• Insights into Protein Dynamics: Proteins often operate through their peptide fragments, which offer a window into how proteins fold, interact, and function within complex cellular environments.

• Biomarker Discovery: Peptide profiles can unveil unique disease signatures, making them invaluable for diagnosing or prognosing conditions like cancer.

• Post-translational Modifications (PTMs): Mapping PTMs such as phosphorylation or glycosylation is vital for studying oncogenic pathways and identifying drug targets.

• Therapeutic Targets: Identifying peptide sequences and their alterations exposes potential therapeutic targets, offering new strategies for intervention in disease processes.

Experimental results correspond to the input .csv file which was produced as a result by our MS Viewer, It contains important information about quality and quantity of peptides. This data can then be further analyzed using bioinformatics tools to identify patterns and relationships between proteins. By integrating multiple sources of data, a comprehensive understanding of protein function and regulation within biological systems can be obtained.

As for the theoretical results, these are the expected behaviour of peptides once their respective protein is digested with trypsin. This provides valuable insights into expected protein structure and its behavior. Additionally, this data can be used to validate experimental results and guide future research directions in the field of proteomics.

Most viable use case of this module is to check which peptides are missing or in higher concentration that expected in experimental results.

Now that the actual and expected peptide positions are obtained, a detailed overview can be found automatically in the analysis tab. Most importantly the comparison of this type of result can further be compared for two different files, for example, healthy vs cancerous tissues.

In summary, the MS Analyser model has shown a capability to differentiate between normal and tumor tissue at a molecular level, with variations in protein and peptide counts being noted. Some proteins manifest with higher peptide counts, although this may not be consistent across all cases of CRC. Lastly, graphical representations suggest certain peptides’ absence in tumor tissues compared to normal tissues, pointing towards specific molecular changes associated with cancerous transformations.

The abundance of peptides alone cannot reveal much information about their behavior unless it is studied with their intensity. Theoretically, the peptides belonging to the same protein must have the same stoichiometry i.e. they must be present in equal molar amounts. However, their detection in Mass Spectrometers is influenced by the presence of other precursor ions.

During one of our recent studies, we found some differences for MCM proteins in tumor samples when compared to normal ones. Specifically, MCM3 (Minichromosome Maintenance Complex Component 3) protein, an essential player in DNA replication and cell cycle regulation.

The objective was to explore the differential peptide intensities of the MCM3 protein in normal versus tumor tissues using various data transformation methods. By comparing raw, normalized, and log-transformed intensities, we aimed to identify specific peptides whose expression levels differ significantly between these conditions.

Examining the differences in peptide intensities and their positions in normal and tumor tissues uncovers vital aspects of protein regulation and function that are pivotal in cancer development. This analysis not only advances our understanding of tumor biology but also opens up new avenues for diagnostic and therapeutic innovations.