MassIVE MSV000091469

Partial Public PXD040776

Proteomic Fingerprinting of Prostate Cancer Progression Through Library-Free DIA-MS Reveals Systematic and Conserved Pathway Dysregulation

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Description

Prostate cancer is the second-most prevalent cancer in men and the cancer with the highest age-adjusted incidence overall. With rates of diagnoses positively correlating with advanced age, the aging populations in many high cancer rate countries mandate the development of facile strategies to accurately identify and stratify this disease. Protein biomarkers are used prominently in prostate cancer diagnosis and therapeutic monitoring but are often criticized for inaccuracy that leads to under or overdiagnosis, incorrect treatment, or false indication of severity. Taken together, there has never been a more pressing need to uncover biomolecular fingerprints or protein panels that enable minimally invasive cancer diagnoses and the ability to confidently stratify disease states. Research towards this goal has traditionally been limited by the lack of model organisms that mimic the unique genotypic and phenotypic characteristics associated with discrete prostate cancer severities. The recently developed benign prostate hypertrophy to prostate cancer (BCaP) cell model removes this limitation, enabling confident association of protein expression changes with cancer phenotype. Herein, we investigate three progressive prostate cancer phenotypes using library-free data-independent acquisition mass spectrometry. Identifying 91,785 peptides and quantifying 6,614 proteins, we reveal 1,242 biomolecular signatures dysregulated in accordance with cancer progression. Highlighting 7 distinct diagnostic expression patterns within this protein cohort, we reveal the progressive reorganization of critical biological processes such as kinetochore formation, cytoskeletal organization, metabolic processing, and interferon signaling. We also provide a topical comparison of transcript and protein level analyses, articulating the importance of proteomic measurements and the need for regular, multimodal analysis. Together, this study presents a primary investigation of the protein-level perturbations observed in a novel progressive cell model, pinpointing a collection of proteins that demonstrate potential for biomarker validation and utility within protein-centric prostate cancer diagnosis. [doi:10.25345/C5QN5ZN2J] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Prostate Cancer ; Data-Independent Analysis ; Library-Free ; Proteomics ; Cancer Progression

Contact

Principal Investigators:
(in alphabetical order) 		Lingjun Li, University of Wisconsin-Madison, United States
Submitting User: grahamdelafield
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