Evidence map›Paper›PMID 28203090›Full record

ArticleOncoTargets and therapy2017

Exploring targeted therapy of osteosarcoma using proteomics data.

Parunya Chaiyawat, Jongkolnee Settakorn, Apiruk Sangsin, Pimpisa Teeyakasem, Jeerawan Klangjorhor, Aungsumalee Soongkhaw, Dumnoensun Pruksakorn

Abstract read
In one paragraph

Article in OncoTargets and therapy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Trial
  2. Article
  3. Cellular and Genetic Background of Osteosarcoma.Current issues in molecular biology · 2023
    Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
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.

Parunya ChaiyawatOrthopedic Laboratory and Research Netting Center, Department of Orthopedics.
Jongkolnee SettakornDepartment of Pathology, Faculty of Medicine.
Apiruk SangsinOrthopedic Laboratory and Research Netting Center, Department of Orthopedics.
Pimpisa TeeyakasemOrthopedic Laboratory and Research Netting Center, Department of Orthopedics.
Jeerawan KlangjorhorOrthopedic Laboratory and Research Netting Center, Department of Orthopedics.
Aungsumalee SoongkhawDepartment of Pathology, Faculty of Medicine.
Dumnoensun PruksakornOrthopedic Laboratory and Research Netting Center, Department of Orthopedics; Excellence Center in Osteology Research and Training Center, Chiang Mai University, Chiang Mai, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite multimodal therapeutic treatments of osteosarcoma (OS), some patients develop resistance to currently available regimens and eventually end up with recurrent or metastatic outcomes. Many attempts have been made to discover effective drugs for improving outcome; however, due to the heterogeneity of the disease, new therapeutic options have not yet been identified. This study aims to explore potential targeted therapy related to protein profiles of OS. In this review of proteomics studies, we extracted data on differentially expressed proteins (DEPs) from archived literature in PubMed and our in-house repository. The data were divided into three experimental groups, DEPs in 1) OS/OB: OS vs osteoblastic (OB) cells, 2) metastasis: metastatic vs non-metastatic sublines plus fresh tissues from primary OS with and without pulmonary metastasis, and 3) chemoresistance: spheroid (higher chemoresistance) vs monolayer cells plus fresh tissues from biopsies from good and poor responders. All up-regulated protein entities in the list of DEPs were sorted and cross-referenced with identifiers of targets of US Food and Drug Administration (FDA)-approved agents and chemical inhibitors. We found that many targets of FDA-approved antineoplastic agents, mainly a group of epigenetic regulators, kinases, and proteasomes, were highly expressed in OS cells. Additionally, some overexpressed proteins were targets of FDA-approved non-cancer drugs, including immunosuppressive and antiarrhythmic drugs. The resulting list of chemical agents showed that some transferase enzyme inhibitors might have anticancer activity. We also explored common targets of OS/OB and metastasis groups, including amidophosphoribosyltransferase (PPAT), l-lactate dehydrogenase B chain (LDHB), and pyruvate kinase M2 (PKM2) as well as the common target of all categories, cathepsin D (CTSD). This study demonstrates the benefits of a text mining approach to exploring therapeutic targets related to protein expression patterns. These results suggest possible repurposing of some FDA-approved medicines for the treatment of OS and using chemical inhibitors in drug screening tests.

Indexed as

FDA-approved drugsosteosarcomaproteomicstargeted therapytext mining

Identifiers

PMID28203090
PMCPMC5295800

What OpenQuestion holds

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Registered trials

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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.