@article{MAKHILLIJTM202217120024,
    title = {The Role of Pertomix Approaches in Early Detection of Cancer},
    journal = {International Journal of Tropical Medicine},
    volume = {17},
    number = {1},
    pages = {10-19},
    year = {2022},
    issn = {1816-3319},
    doi = {ijtmed.2022.10.19},
    url = {https://makhillpublications.co/view-article.php?issn=1816-3319&doi=ijtmed.2022.10.19},
    author = {Saman and},
    keywords = {biomarkers,protein,early detection,cancer,Proteomics},
    abstract = {Although, advances in early stage detection of cancer have
come of great help to cancer treatment, most routine screening and
diagnosis tools lack sufficient sensitivity and specificity of molecular
approaches such as proteomics. With the proteomic technologies
emerging, classification and identification of body fluid proteins have
been a major focus of scientists. Proteomic analyses have opened a new
horizon in screening changes happening in cellular processes to become
cancerous; however, it is yet to be perfected using complementary
approaches for more accurate diagnosis of cancers. A combination of
proteomics approaches like Ciphergen Protein Chip Arrays and SELDITOF
MS with bioinformatics tools was proved to be effective in the
discovery of new biomarkers which further helps the early-stage
detection and diagnosis of cancer. In this study, the UMSA algorithm
provided an efficient model to rank a large number of peaks collectively
according to their contribution to the separation of two predefined
diagnostic groups. The Pro Peak, BootStrap module introduced random
perturbations in multiple runs to test the consistency of the top-ranked
peaks, measured by the SD of computed ranks from multiple runs. To
establish an upper cutoff value on a peak’s rank SD for its performance
not to be considered as purely by chance, the same bootstrap procedure
was applied to a randomly generated data set that simulated the
distribution of the real data. The minimum value of rank SDs from such
“simulated peaks” indicates the degree of consistency that a peak might
achieve by random chance. This minimum value was used as the cutoff
to help to reduce the original 147 peaks to a subset of 15 peaks for
further consideration. The performance of such peaks should be less
likely attributable to random artifacts in the data. For the three
biomarkers selected, no significant correlation was found between the
concentrations of the markers and tumor size or lymph node metastasis.
The discriminatory power of these markers therefore most likely reflects
the malignant nature of the tumor rather than its progression. The origin
and identity of BC1, BC2, and BC3 are currently under investigation.
Furthermore, it is not our intent at this stage to suggest a final diagnostic
algorithm based on nonlinear classification. In conclusion, we have
shown that using proteomics approaches such as Ciphergen ProteinChip
Arrays and SELDI-TOF MS in combination with bioinformatics tools
could facilitate the discovery of new biomarkers. Using the panel of
three selected biomarkers, we could achieve high sensitivity and
specificity for the detection of breast cancer.}
    }