Blood serum is a source of cancer biomarkers. Tumor development is accompanied by metabolic malfunction that may result in altered serum composition: proteins that are up/downregulated and low molecular weight metabolites undergoing changes in concentration. Nowadays, researchers analyze blood serum through multifactorial techniques profiling to transform the current scenario in cancer therapy by: 1) determining patient prognosis; 2) monitoring tumor recurrence and therapeutic responses in real-time; 3) identifying new therapeutic targets; 4) elucidating drug resistance mechanisms; and 5) improving our current understanding of tumor progression and metastatic disease. One of the main advantages of using plasma samples is that only a minimally invasive assay such as a routine blood test analysis is required.
In this context Differential Scanning Calorimetry (DSC) has reveal its potential as a technique for a global analysis of serum samples. Traditionally, DSC has been employed for determining the partial heat capacity of a macromolecule as a function of temperature, from which the thermodynamic parameters associated with the structural stability of the macromolecule by thermal denaturation can be estimated1. Due to its high sensitivity, the precise determination of the thermally-induced conformational transitions of biomolecules that are present in plasma can be readily performed using DSC. The ten most abundant proteins in blood serum (albumin, IgG, fibrinogen, haptoglobin) account for 90% (w/w) of serum. Another twelve proteins represent the next 9% and 3000 proteins account for the final 1%. Interestingly, the normal serum thermogram can be reasonably reproduced with just the most abundant proteins. Therefore, serum thermograms do not provide direct information about the remaining, low-concentration serum proteins. However, the ability of those proteins, as well as disease-associated metabolites, to bind to and alter the unfolding temperatures of the most abundant proteins is responsible for the changes observed in the thermograms from disease states (that is, the interactome hypothesis).
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