ReviewBiology2024
The Protein Corona on Nanoparticles for Tumor Targeting in Prostate Cancer-A Review of the Literature and Experimental Trial Protocol.
Review in Biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Advancements in Inorganic Chiral Nanomaterials for Anti-Tumor Immunotherapy.Cancer science · 2026Review
- Nanomedicine in Ovarian Cancer: Advances in Imaging, Targeted Delivery, and Theranostic Therapeutic Platforms.Cancers · 2025Review
- Nanomaterials for Theranostic Management of Oral Cancer: Advances in Imaging, Biosensing, Targeted Delivery, and Multimodal Synergistic Therapy.International journal of nanomedicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The National Cancer Institute (NCI) recognizes the potential of technologies based on the use of nanoparticles (NPs) in revolutionizing clinical approaches to the diagnosis and prognosis of cancer. Recent research suggests that once NPs come into contact with the biological fluid of cancer patients, they are covered by proteins, forming a "protein corona" composed of hundreds of plasma proteins. The concept of a personalized, disease-specific protein corona, demonstrating substantial differences in NP corona profiles between patients with and without cancer, has been introduced. We developed the design of an experimental prospective single-center study with patients allocated in a 1:1:1 ratio of one of three arms: untreated patients with benign prostatic hyperplasia (BPH), untreated patients with non-metastatic prostate cancer (PCa), and metastatic prostate cancer patients starting systemic therapies with new androgen-targeted agents or taxanes. The protocol aims to develop and implement sensitive nanotools with two distinct objectives: First, to design NPs capable of selectively binding and detecting biomarkers in order to build a predictive diagnostic model to effectively discriminate between patient sera affected by BPH and PCa. Secondly, within the population with PCa, in the case of initial advanced metastatic diagnosis, the objective is to find biomarkers capable of predicting the response to systemic treatments to improve the precision and efficiency of monitoring treatment outcomes. For protein and metabolite corona experiments, we developed a cross-reactive sensor array platform with cancer detection capacity made of three liposomal formulations with different surface charges. For proteomic-NP studies, proteins were identified and quantified using nano-high-performance LC (nanoHPLC) coupled with MS/MS (nanoHPLC-MS/MS). Metabolites were instead analyzed using an untargeted metabolomic approach. Compared with previous review articles, the novelty of this review is represented by the analysis of the possible clinical applications of protein corona NPs focused on PCa and the presentation of a new clinical protocol in the metastatic phase of PCa.
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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.