Evidence map›Paper›PMID 37516753›Full record

ArticleScientific reports2023

Encapsulation and adhesion of nanoparticles as a potential biomarker for TNBC cells metastatic propensity.

Yulia Merkher, Elizaveta Kontareva, Elizaveta Bogdan, Konstantin Achkasov, Ksenia Maximova, Joshua M Grolman, Sergey Leonov

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yulia MerkherLaboratory of Innovative Medicine and Agrobiotechnology, Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Moscow Region, Russia. syuliam@gmail.com.
Elizaveta KontarevaLaboratory of Innovative Medicine and Agrobiotechnology, Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Moscow Region, Russia.
Elizaveta BogdanLaboratory of Innovative Medicine and Agrobiotechnology, Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Moscow Region, Russia.
Konstantin AchkasovLaboratory of Innovative Medicine and Agrobiotechnology, Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Moscow Region, Russia.
Ksenia MaximovaLaboratory of Innovative Medicine and Agrobiotechnology, Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Moscow Region, Russia.
Joshua M GrolmanThe Biomechanic Materials Lab, Technion Israel Institute of Technology, Haifa, Israel.
Sergey LeonovLaboratory of Innovative Medicine and Agrobiotechnology, Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Moscow Region, Russia. leonov.sv@mipt.ru.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metastasis is the main cause of cancer-related mortality; therefore, the ability to predict its propensity can remarkably affect survival rate. Metastasis development is predicted nowadays by lymph-node status, tumor size, histopathology, and genetic testing. However, all these methods may have inaccuracies, and some require weeks to complete. Identifying novel prognostic markers will open an essential source for risk prediction, possibly guiding to elevated patient treatment by personalized strategies. Cancer cell invasion is a critical step in metastasis. The cytoskeletal mechanisms used by metastatic cells for the invasion process are very similar to the utilization of actin cytoskeleton in the endocytosis process. In the current study, the adhesion and encapsulation efficiency of low-cost carboxylate-modified fluorescent nanoparticles by breast cancer cells with high (HM) and low metastatic potential (LM) have been evaluated; benign cells were used as control. Using high-content fluorescence imaging and analysis, we have revealed (within a short time of 1 h), that efficiency of nanoparticles adherence and encapsulation is sufficiently higher in HM cells compared to LM cells, while benign cells are not encapsulating or adhering the particles during experiment time at all. We have utilized custom-made automatic image analysis algorithms to find quantitative co-localization (Pearson's coefficients) of the nanoparticles with the imaged cells. The method proposed here is straightforward; it does not require especial equipment or expensive materials nor complicated cell manipulations, it may be potentially applicable for various cells, including patient-derived cells. Effortless and quantitative determination of the metastatic likelihood has the potential to be performed using patient-specific biopsy/surgery sample, which will directly influence the choice of protocols for cancer patient's treatment and, as a result, increase their life expectancy.

Indexed as

NanoparticlesTriple Negative Breast NeoplasmsBiomarkersHumansPhysical PhenomenaTissue AdhesionsBiomarkers

Identifiers

PMID37516753
PMCPMC10387085

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