Evidence map›Paper›PMID 36357703›Full record

ReviewBritish journal of cancer2023

Integrative analysis of multi-omics data for liquid biopsy.

Geng Chen, Jing Zhang, Qiaoting Fu, Valerie Taly, Fei Tan

Erratum issuedAbstract readReview
In one paragraph

Review in British journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 49 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 2 pooled it
–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

49 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Geng ChenShanghai Skin Disease Hospital, School of Medicine, Tongji University, 200443, Shanghai, China. chengeng66666@outlook.com.ORCID http://orcid.org/0000-0002-3452-9434
Jing ZhangShanghai Skin Disease Hospital, School of Medicine, Tongji University, 200443, Shanghai, China.
Qiaoting FuShanghai Skin Disease Clinical College, The Fifth Clinical Medical College, Anhui Medical University, 200443, Shanghai, China.
Valerie TalyUniversité de Paris, UMR-S1138, CNRS SNC5096, Équipe labélisée Ligue Nationale contre le cancer, Centre de Recherche des Cordeliers, Paris, France. valerie.taly@parisdescartes.fr.ORCID http://orcid.org/0000-0002-3116-8602
Fei TanShanghai Skin Disease Hospital, School of Medicine, Tongji University, 200443, Shanghai, China. tanfeitrue@126.com.ORCID http://orcid.org/0000-0002-8136-0037

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The innovation of liquid biopsy holds great potential to revolutionise cancer management through early diagnosis and timely treatment of cancer. Integrative analysis of different tumour-derived omics data (such as genomics, epigenetics, fragmentomics, and proteomics) from body fluids for cancer detection and monitoring could outperform the analysis of single modality data alone. In this review, we focussed on the discussion of early cancer detection and molecular residual disease surveillance based on multi-omics data of blood. We summarised diverse types of tumour-derived components, current popular platforms for profiling cancer-associated signals, machine learning approaches for joint analysis of liquid biopsy data, as well as multi-omics-based early detection of cancers, molecular residual disease monitoring, and treatment response surveillance. We also discussed the challenges and future directions of multi-omics-based liquid biopsy. With the development of both experimental protocols and computational methods dedicated to liquid biopsy, the implementation of multi-omics strategies into the clinical workflow will likely benefit the clinical management of cancers including decision-making guidance and patient outcome improvement.

Indexed as

MultiomicsNeoplasmsComputational BiologyEpigenomicsGenomicsHumansProteomics

Identifiers

PMID36357703
PMCPMC9938261

What OpenQuestion holds

Textmetadata
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.