Evidence map›Paper›PMID 39104623›Full record

ReviewBioImpacts : BI2024

Cancer treatment comes to age: from one-size-fits-all to next-generation sequencing (NGS) technologies.

Sepideh Parvizpour, Hanieh Beyrampour-Basmenj, Jafar Razmara, Farhad Farhadi, Mohd Shahir Shamsir

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BioImpacts : BI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Sepideh ParvizpourResearch Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical ‎Sciences, Tabriz, Iran.ORCID https://orcid.org/0000-0003-1865-5040
Hanieh Beyrampour-BasmenjDepartment of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical ‎Sciences, Tabriz, Iran.
Jafar RazmaraDepartment of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, ‎Tabriz, Iran.ORCID https://orcid.org/0000-0002-6320-8517
Farhad FarhadiFood and Drug Administration, Tabriz University of Medical Sciences, Tabriz, Iran.
Mohd Shahir ShamsirBioinformatics Research Group, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is one of the leading causes of death worldwide and one of the greatest challenges in extending life expectancy. The paradigm of one-size-fits-all medicine has already given way to the stratification of patients by disease subtypes, clinical characteristics, and biomarkers (stratified medicine). The introduction of next-generation sequencing (NGS) in clinical oncology has made it possible to tailor cancer patient therapy to their molecular profiles. NGS is expected to lead the transition to precision medicine (PM), where the right therapeutic approach is chosen for each patient based on their characteristics and mutations. Here, we highlight how the NGS technology facilitates cancer treatment. In this regard, first, precision medicine and NGS technology are reviewed, and then, the NGS revolution in precision medicine is described. In the sequel, the role of NGS in oncology and the existing limitations are discussed. The available databases and bioinformatics tools and online servers used in NGS data analysis are also reviewed. The review ends with concluding remarks.

Indexed as

CancerNext-generation sequencingOne-size-fits-all medicinePersonalized medicinePrecision medicineStratified medicine

Identifiers

PMID39104623
PMCPMC11298019

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.