Evidence map›Paper›PMID 34692733›Full record

ArticleFrontiers in medicine2021

Heat Shock Proteins in Urine as Cancer Biomarkers.

Zarema Albakova, Diogo Dubart Norinho, Yana Mangasarova, Alexander Sapozhnikov

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 25% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Article
  6. Evaluation of the Small Heat Shock Protein Family MembersInternational journal of molecular sciences · 2023
    Article
  7. Article
  8. Review
  9. Article
  10. The HSP Immune Network in Cancer.Frontiers in immunology · 2021
    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

4 authors at 2 institutions in 1 country.

Zarema AlbakovaDepartment of Biology, Lomonosov Moscow State University, Moscow, Russia.
Diogo Dubart NorinhoData Science Department, NOS SGPS, Porto, Portugal.
Yana MangasarovaNational Research Center for Hematology, Moscow, Russia.
Alexander SapozhnikovDepartment of Biology, Lomonosov Moscow State University, Moscow, Russia.
Lomonosov Moscow State University · RUFederal State Institution Hematology Research Center · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heat shock proteins (HSPs) are a large family of molecular chaperones, which have shown to be implicated in various hallmarks of cancer such as resistance to apoptosis, invasion, angiogenesis, induction of immune tolerance, and metastasis. Several studies reported aberrant expression of HSPs in liquid biopsies of cancer patients and this has opened new perspectives on the use of HSPs as biomarkers of cancer. However, no specific diagnostic, predictive, or prognostic HSP chaperone-based urine biomarker has been yet discovered. On the other hand, divergent expression of HSPs has also been observed in other pathologies, including neurodegenerative and cardiovascular diseases, suggesting that new approaches should be employed for the discovery of cancer-specific HSP biomarkers. In this study, we propose a new strategy in identifying cancer-specific HSP-based biomarkers, where HSP networks in urine can be used to predict cancer. By analyzing HSPs present in urine, we could predict cancer with approximately 90% precision by machine learning approach. We aim to show that coupling the machine learning approach and the understanding of how HSPs operate, including their functional cycles, collaboration with and within networks, is effective in defining patients with cancer, which may provide the basis for future discoveries of novel HSP-based biomarkers of cancer.

Indexed as

biomarkerscancerheat shock proteinsmachine learningurine

Identifiers

PMID34692733
PMCPMC8531591
OpenAlexW3206759763

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.