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Updated: Nov 8, 2025

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Secretome Profiling and Computational Biology of Human Leiomyoma Samples Unravel Molecular Signatures with Potential
Tahreem Sahar1, Aruna Nigam2, Shadab Anjum1
1Department of Biotechnology, School of Chemical and Life Sciences, Jamia Hamdard, New Delhi, 110062, India.
Abstract:
In recent years, significant advancements have been made in the way the complex proteome samples are compared but the ultimate goal of routine biomarker discovery has yet to be achieved. Based on reverse genetic strategy, our study involved the spotting of genes showing expressional variability in uterine leiomyoma females. Serum samples were taken from uterine leiomyomas subjects (n=6) and healthy control subjects (n=6) for proteomic studies. Additionally, leiomyoma tissue samples (n=25) and normal myometrium samples (n=25) were taken for validation studies. In this study, we profiled the proteomes of uterine leiomyoma patient's serum and healthy control, along with relative quantification using Nano LC-MS/MS analysis. A total of 146 proteins were reported to be significantly differentially expressed (P value less than 0.05) in case and control sample. Statistical analysis identified a number of molecular signatures distinguishing healthy from diseased serum. Among these, five proteins lumican, ficolin, MASP2, EMSY, and kallistatin were further chosen according to their function for validation. Kallistatin was downregulated while ficolin, MASP2, lumican, and EMSY were found to be upregulated in the diseased sample. The expression modulations in the identified genes were further validated in twenty-five cases. Interactions among the differentially expressed proteins were identified followed with network analysis. Network analysis emphasized important pathways that are highly deregulated in myoma, and functional significance of these pathways in the pathology of the disease was discussed. Comparative expression analysis reveals distinct molecular signatures and their probable role in diagnosis of the disease.
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