ArticleJournal of proteome research2025
Deciphering Proteoform Landscape of Mammary Carcinoma by Top-Down Proteomics.
Article in Journal of proteome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Defining the proteoform landscape of breast cancer can provide unique insights into the signaling pathways driving disease progression. While bottom-up proteomics has been utilized to profile breast cancer, it lacks the ability to capture intact proteoforms that may underpin the disease. Top-down proteomics is ideally suited to characterize intact proteoforms; however, most top-down proteomics studies have been limited to low molecular weight (MW) proteins (<50 kDa). Herein, we employed a two-dimensional (2D) liquid chromatography combining size exclusion chromatography (SEC) with reverse phase chromatography (RPC) followed by high-resolution mass spectrometry (MS) to expand the coverage for high MW proteoforms. Using this 2D-SEC-RPC-MS approach, we observed a 5-fold increase in the detection of high MW proteoforms (>50 kDa) compared to the conventional 1D-RPC-MS. SEC separation significantly enhanced the detection of high MW proteoforms (>104 kDa), including intermediate filament proteins, vimentin and keratins. Based on accurate mass measurements and MS/MS data, we identified 775 proteoforms from both TFA and HEPES extracts and detected PTMs, such as acetylation, glutathionylation, and myristoylation. Pathway analysis uncovered many proteoforms involved in processes dysregulated in cancer progression. Overall, our findings illustrate the power of top-down proteomics in defining the proteoform landscape of breast carcinoma.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.