Evidence map›Paper›PMID 37489460›Full record

ArticleMedical sciences (Basel, Switzerland)2023

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.

Ezgi Demir Karaman, Zerrin Işık

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical sciences (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
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  3. CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026
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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

2 authors.

Ezgi Demir KaramanDepartment of Computer Engineering, Institute of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey.
Zerrin IşıkDepartment of Computer Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir 35390, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for

Indexed as

MultiomicsNeoplasmsBiomarkers, TumorData AnalysisHumansPrognosisSEC Translocation ChannelsBiomarkers, TumorSEC61G protein, humanSEC Translocation Channelscancer biomarkercommunity detectionmulti-omics datanetwork-based integrationsurvival analysis

Identifiers

PMID37489460
PMCPMC10366886

What OpenQuestion holds

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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.