Evidence mapPaperPMID 42490792Full record

ReviewFrontiers in oncology2026

Early multi-cancer detection using liquid biopsy: emerging biomarkers and clinical strategies.

Sowhanur Rahman Nirob, Md Kishor Morol, Diya Rahman, Liew Tze Hui, Dip Nandi, Mashiour Rahman, Abdullah Al Jubair

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Sowhanur Rahman NirobDepartment of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh.
Md Kishor MorolELITE Research Lab, Queens, NY, United States.
Diya RahmanDepartment of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh.
Liew Tze HuiFaculty of Information Science and Technology, Multimedia University, Melaka, Malaysia.
Dip NandiDepartment of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh.
Mashiour RahmanDepartment of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh.
Abdullah Al JubairDepartment of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liquid biopsy has become a revolutionary method for the early detection of cancer as a non-invasive technology that can assess circulating tumor material in biofluids. Liquid biopsy allows dynamic monitoring of tumor evolution, genetic changes and treatment responses, which is different from traditional tissue biopsy which offers a static and potentially narrow view of tumor biology. This mini-review will summarize the rapidly evolving future of circulating biomarkers (circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs, proteins, exosomes and epigenetic fingerprints), with their potential for early multi-cancer biomarkers, and their integration into early detection. The analytical sensitivity of liquid biopsy has expanded dramatically through technologies such as next-generation sequencing (NGS), digital PCR, and advanced proteomics. In addition, data collection has been enhanced through machine learning for increased predictive performance and the identification of new biomarkers. This review discusses clinical performance between liquid and tissue biopsy and the value of combined biomarker methods to improve accuracy for the detection of early disease. Finally, future directions will be presented to identify new methods of integration, improved costs and the establishment of early detection programs at the population scale.

Indexed as

biomarkersCTCsCtDNAliquid biopsymicroRNAmulti-cancer early detectionNGSproteomics

Identifiers

PMID42490792
PMCPMC13375447

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.