Examining protein expression patterns in breast cancer compared to healthy group by bioinformatics

Breast cancer is a multifactorial disease and various factors contribute to its occurrence. There is a significant di↵erence between the occurrence of this disease and mortality rates . Despite improvements in the genetic classification, protein level is frequently used in clinical diagnostics and treatment decisions . In the molecular sciences, proteomics has become an important field. Proteomics revealed critical details in the characterization of molecular mechanisms, development, and metastasis cancer, identifying specific treatment targets as well as useful biomarkers . The aim of this research is to determine the key proteins and central molecules involved in the pathogenesis of breast cancer using protein data analysis. MaxQuant software was used to convert the raw data (ID.number PXD۰۱۲۴۳۱) into the protein table according to the target-decoy identification algorithm. The results were analyzed with Perseus software and their quality verified by component analysis so that infected and identified-reverse sequence proteins removed. T-test and Permutation-based FDR method were used to correct the P-values and identified expressed proteins in study groups. Cytoscape v and CluePedia V software created a comprehensive view of interactions between proteins and other obtained layers. The confidence threshold for network edges was ۰.۶ and considered for Inhibition, Binding, Activation and PTM.The quality control shows appropriate separation of healthy and disease samples in study groups. The results indicated presence of ۱۱۴۹ altered proteins in patient sample, including ۱۹۱ low expression and ۹۵۸ high expression in the comparison with healthy group with confidence threshold of FDR < ۰.۰۵. Also, identified-central proteins in the network were included NID۱ , LAMC۱, COL۴A۱, COL۱A۲, COL۴A۲, TNS۱. It is expected that these proteins will be suitable as a marker for early diagnosis or for therapeutic purposes.

کلیدواژه ها:

proteomics ، breast cancer ، protein expression ، bioinformatics