Background: Searching the biomarker from complex heterogeneous material for early detection of disease is a challenging task in the field of biomedical sciences. Objective: The study has been arranged to explore the proteomics serum derived profiling of the differential expressed and low molecular weight protein in breast cancer patient. Method: Quantitative proteome was analyzed using the Nano LC/Mass and Bioinformatics tool. Results: This quantification yields 239 total protein constituting 29% of differentially expressed protein, with 82% downregulated differential protein and 18% up-regulated differential protein. While 12% of total protein were found to be cancer inducing proteins. Gene Ontology (GO) described that the altered proteins with 0-60 kDa mass in nucleus, cytosol, ER, and mitochondria were abundant that chiefly controlled the RNA, DNA, ATP, Ca ion and receptor bindings Conclusion: The study demonstrate that the organelle specific, low molecular weighted proteins are significantly important biomarker. That act as strong agents in the prognosis.

Protein corona composition and precise physiological understanding of differentially expressed proteins are key for identifying disease biomarkers. In this report, we presented a distinctive quantitative proteomics table of molecular cell signaling differentially expressed proteins of corona that formed on iron carbide nanoparticles (NPs). High-performance liquid chromatography/electrospray ionization coupled with ion trap mass analyzer (HPLC/ESI-Orbitrap) and MASCOT helped quantify 142 differentially expressed proteins. Among these proteins, 104 proteins showed upregulated behavior and 38 proteins were downregulated with respect to the control, whereas 48, 32 and 24 proteins were upregulated and 8, 9 and 21 were downregulated CW (control with unmodified NPs), CY (control with modified NPs) and WY (modified and unmodified NPs), respectively. These proteins were further categorized on behalf of their regularity, locality, molecular functionality and molecular masses using gene ontology (GO). A STRING analysis was used to target the specific range of proteins involved in metabolic pathways and molecular processing in different kinds of binding functionalities, such as RNA, DNA, ATP, ADP, GTP, GDP and calcium ion bindings. Thus, this study will help develop efficient protocols for the identification of latent biomarkers in early disease detection using protein fingerprints.