ArticleJournal of extracellular vesicles2026
Leveraging Quantitative Proteomics and Extracellular Vesicle Data to Uncover Druggable Receptor Kinases Across Cancers.
Article in Journal of extracellular vesicles, 2026. 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
7 authors.
Funding
Abstract
Extracellular vesicles (EVs) play an important role in cancer progression and metastasis. The increasing clinical proteomics data provides an opportunity to uncover new biomarkers and therapeutic targets in cancer specific EVs. Here, we present an integrated data analysis approach that leverages a comprehensive protein catalog from EVs, blood plasma, and the cancer cell surface to identify EV-associated proteins and evaluate their potential as therapeutic targets in cancer cells. Quantitative proteomics data from 12 cancer types (n = 2,272) identified 3,250 proteins that are commonly found in EV studies and EV quantity data. Receptor kinases (RKs) are crucial in cancer signalling pathways and have been widely studied targets. Our analysis focused on identifying RKs with significant high expression in cancer types that were also detected in EVs. Notably, PTK7 emerged as a significant target with high expression or survival association in seven independent cancer cohorts. We then explored the druggability of the RKs. Our analysis of drug repurposing network and RNAi screening suggested PTK7 as a potential therapeutic target with no FDA approved drugs in cancer. Overall, this integrative analysis proposes a framework to prioritize EV-associated candidates for downstream therapeutic investigation in cancer.
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.