Seldi-tof-ms profiling of metastatic phenotype in histopathological subtypes of breast cancer

Basit Öğe Kaydı
dc.authorid0000-0002-0675-1839
dc.contributor.authorYiğitbaşı, Türkan
dc.contributor.authorÇalıbaşı Kocal, Gizem
dc.contributor.authorBüyükuslu, Nihal
dc.contributor.authorAtahan, Murat Kemal
dc.contributor.authorKüpeli, Hakan
dc.contributor.authorYiğit, Seyran
dc.contributor.authorTarcan, Ercüment
dc.contributor.authorBaskın, Yasemin
dc.date.accessioned10.07.201910:49:13
dc.date.accessioned2019-07-10T19:51:45Z
dc.date.available10.07.201910:49:13
dc.date.available2019-07-10T19:51:45Z
dc.date.issued2018
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Tıbbi Biyokimya Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Sağlık Bilimleri Fakültesi, Beslenme ve Diyetetik Bölümü
dc.descriptionWOS: 000437989600007
dc.description.abstractBackground: Early detection of breast cancer is a key to the success of breast cancer management. Serum proteome analysis using Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (SELDI-TOF-MS) generates useful information that can be utilized to describe exclusive prognostic and diagnostic biomarkers. Objective: This study aimed to use proteomics and bioinformatics to identify new biomarkers during the metastatic process of breast cancers that were classified as invasive lobular cancer or invasive ductal cancer. Method: Blood samples from 64 breast cancer patients [36 with invasive ductal cancer (14 of whom were lymph node positive); 28 with invasive lobular cancer (8 of whom were lymph node positive] were analyzed using IMAC 30 protein chips. The data acquired from the spectra were processed with univariate statistical analysis (Protein Chip Data Manager Software). Results: One-hundred-eighteen clusters were generated from the individual serum samples. Thirty-six proteins of the metastatic phenotype were found to be diagnostically accurate in cluster analysis. In the breast cancer group, a single candidate peak (m/z 1090.8) that was able to discriminate the metastatic progression was identified as a metastatic phenotype marker. Fifteen protein peaks were identified as markers to separate the histopathological subtypes as either invasive ductal cancer or invasive lobular cancer. Conclusion: In recent years, proteomic methods have rapidly become widespread in breast cancer research. This study revealed the pattern of a group of proteins that were not previously identified and are recommended as candidate markers to diagnose metastatic progression.
dc.identifier.citationYiğitbaşı, T., Çalıbaşı Kocal, G., Büyükuslu, N., Atahan, M., Küpeli, H., Yiğit, S. ... Baskın, Y. (2018). Seldi-tof-ms profiling of metastatic phenotype in histopathological subtypes of breast cancer. Current Proteomics, 15(3), 214-220. https://dx.doi.org/10.2174/1570164615666180309154038
dc.identifier.doi10.2174/1570164615666180309154038
dc.identifier.endpage220
dc.identifier.issn1570-1646
dc.identifier.issn1875-6247
dc.identifier.issue3
dc.identifier.scopusqualityQ4
dc.identifier.startpage214
dc.identifier.urihttps://dx.doi.org/10.2174/1570164615666180309154038
dc.identifier.urihttps://hdl.handle.net/20.500.12511/2278
dc.identifier.volume15
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherBentham Science Publishers Ltd
dc.relation.ispartofCurrent Proteomicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBreast Cancer
dc.subjectHistopathological Subtypes
dc.subjectSELDI-TOF-MS
dc.subjectProfiling
dc.subjectSerum Proteome
dc.subjectMetastatic Phenotype
dc.titleSeldi-tof-ms profiling of metastatic phenotype in histopathological subtypes of breast cancer
dc.typeArticle

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Makale Koleksiyonu
Makale Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu