Evidence map›Paper›PMID 36435816›Full record

ArticleCell death & disease2022

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders.

Garik V Mkrtchyan, Alexander Veviorskiy, Evgeny Izumchenko, Anastasia Shneyderman, Frank W Pun, Ivan V Ozerov, Alex Aliper, Alex Zhavoronkov, Morten Scheibye-Knudsen

Open access · goldAbstract read
In one paragraph

Article in Cell death & disease, 2022. 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
1.5field-weighted citation impact, top 17% 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

15 citing papers in PubMed, 17 citations in OpenAlex.

  1. Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026
    Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024
    Review
  12. Review
  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

9 authors at 2 institutions in 2 countries.

Garik V Mkrtchyan *Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark.
Alexander Veviorskiy *Insilico Medicine, Hong Kong, China.
Evgeny IzumchenkoDepartment of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, IL, USA.
Anastasia ShneydermanInsilico Medicine, Hong Kong, China.
Frank W PunInsilico Medicine, Hong Kong, China.
Ivan V OzerovInsilico Medicine, Hong Kong, China.
Alex AliperInsilico Medicine, Hong Kong, China.
Alex ZhavoronkovInsilico Medicine, Hong Kong, China.ORCID 0000-0001-7067-8966
Morten Scheibye-KnudsenCenter for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark. mscheibye@sund.ku.dk.ORCID 0000-0002-6637-1280
University of Copenhagen · DKUniversity of Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform ( https://pandaomics.com/ ) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization.

Indexed as

Artificial IntelligenceSarcomaCarcinogenesisCentriolesDNA RepairHumans

Identifiers

PMID36435816
PMCPMC9701218
OpenAlexW4310009880

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.