Abstract
Breast cancer is asymptomatic with a poor prognosis at late stage. We used in-silico based proteomics data mining to identify immunoproteins in body fluids as potential indicators of onset and progression of breast cancer. We curated a list of differentially expressed proteins in tissue and body fluids (e.g. saliva and blood) on subtype and non-subtype specific breast cancer over the last 20 years, to show the extent of similarities in protein expression in tissue and bio-fluids. Based on specific selection criteria, significantly altered proteins were filtered to functionally annotate and analyze using different databases. Furthermore, the immunoproteins that showed cross-talks were further analyzed for amino-acid sequence-specific alterations associated with breast cancer to predict their potential impact on the disease. The curated datasets consolidated 4716 non-redundant proteins collectively in tissue, blood, and saliva from literature focused on subtype or non-subtype specific breast cancer. Of these immunoproteins, 39 (e.g., saliva) and 20 (e.g., blood) were found to cross-talk, of which 28 and 6 from saliva and blood, respectively, showed amino acid variations and were associated with breast cancer. Similarly, a total of 92 and 10 driver mutants were identified in saliva and blood, respectively, with ‘deleterious’ or ‘damaging’ impact on the biological function of a protein. The results of the study established correlations between expression profile and variation of immunoproteins with breast cancer to assess the cumulative effect of mutational hotspots and identify proteome-scale alterations that could trigger abnormal cell behavior.
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Abbreviations
- BC:
-
Breast cancer
- MS:
-
Mass spectrometry
- TCGA:
-
The Cancer Genome Atlas
- ER:
-
Estrogen receptor
- PR:
-
Progesterone receptor
- Her2:
-
Human epidermal growth factor receptor 2
- TN:
-
Triple negative
- TCIA:
-
The Cancer Immunome Atlas
- DEPs:
-
Differentially expressed proteins
- 2-DIGE:
-
2-Dimension gel electrophoresis
- iTRAQ:
-
Isobaric tag for relative and absolute quantitation
- TMT:
-
Tandem mass tag
- PTMs:
-
Post-translational modifications
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Acknowledgements
We acknowledge Dr. Aadil Hussain Bhat for assisting in data analysis and discussions during the course of this study. This work was supported by the Intramural Research Program of Indian Institute of Technology Roorkee, Roorkee, India (FIG100642; to KA). KG is thankful to Council of Scientific and Industrial Research (CSIR), India and SM is supported by the Ministry of Human Resource Development, Govt. of India.
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This work was supported by the Intramural Research Program of Indian Institute of Technology Roorkee, Roorkee, India (FIG100642; to KA).
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KG designed, searched, curate, analyzed and interpreted the data. SM analyzed and interpreted the data. KA conceived and supervised the study. KG, SM, and KA wrote the manuscript.
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Giri, K., Maity, S. & Ambatipudi, K. In silico data mining of human body fluids to unravel the immunomes in breast cancer. J Proteins Proteom 12, 45–62 (2021). https://doi.org/10.1007/s42485-021-00056-z
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DOI: https://doi.org/10.1007/s42485-021-00056-z