Evidence map›Paper›PMID 39741289›Full record

ReviewJournal of translational medicine2024

Recent progress in prompt molecular detection of liquid biopsy using Cas enzymes: innovative approaches for cancer diagnosis and analysis.

Zahra Farshchian Yazdi, Shahrzad Roshannezhad, Samaneh Sharif, Mohammad Reza Abbaszadegan

Abstract readReview
In one paragraph

Review in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

4 authors.

Zahra Farshchian Yazdi *Department of Medical Genetics, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Shahrzad Roshannezhad *Department of Science, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Samaneh SharifMedical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. sharifMSM2@mums.ac.ir.
Mohammad Reza AbbaszadeganDepartment of Medical Genetics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. abbaszadeganmr@mums.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Creating fast, non-invasive, precise, and specific diagnostic tests is crucial for enhancing cancer treatment outcomes. Among diagnostic methods, those relying on nucleic acid detection are highly sensitive and specific. Recent developments in diagnostic technologies, particularly those leveraging Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), are revolutionizing cancer detection, providing accurate and timely results. In clinical oncology, liquid biopsy has become a noninvasive and early-detectable alternative to traditional biopsies over the last two decades. Analyzing the nucleic acid content of liquid biopsy samples, which include Circulating Tumor Cells (CTCs), Circulating Tumor DNA (ctDNA), Circulating Cell-Free RNA (cfRNA), and tumor extracellular vesicles, provides a noninvasive method for cancer detection and monitoring. In this review, we explore how the characteristics of various Cas (CRISPR-associated) enzymes have been utilized in diagnostic assays for cancer liquid biopsy and highlight their main applications of innovative approaches in monitoring, as well as early and rapid detection of cancers.

Indexed as

NeoplasmsNeoplastic Cells, CirculatingCell-Free Nucleic AcidsCirculating Tumor DNACRISPR-Cas SystemsHumansLiquid BiopsyCell-Free Nucleic AcidsCirculating Tumor DNACancerCRISPR-CasDiagnosisLiquid biopsy

Identifiers

PMID39741289
PMCPMC11686959

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.