Evidence map›Paper›PMID 16341059›Full record

ReviewGene therapy2006

Prospects of RNA interference therapy for cancer.

S I Pai, Y-Y Lin, B Macaes, A Meneshian, C-F Hung, T-C Wu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Gene therapy, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.

0numbers the graph read from it
0cells of the map it votes in
98citing 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

98 citing papers in PubMed.

  1. Review
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  6. Transdermal delivery for gene therapy.Drug delivery and translational research · 2022
    Review
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38 more citing papers are in PubMed but not listed here.

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

6 authors.

S I PaiDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins Medical Institutions, Baltimore, MD 21205, USA.
Y-Y Lin
B Macaes
A Meneshian
C-F Hung
T-C Wu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RNA interference (RNAi) is a powerful gene-silencing process that holds great promise in the field of cancer therapy. The discovery of RNAi has generated enthusiasm within the scientific community, not only because it has been used to rapidly identify key molecules involved in many disease processes including cancer, but also because RNAi has the potential to be translated into a technology with major therapeutic applications. Our evolving understanding of the molecular pathways important for carcinogenesis has created opportunities for cancer therapy employing RNAi technology to target the key molecules within these pathways. Many gene products involved in carcinogenesis have already been explored as targets for RNAi intervention, and RNAi targeting of molecules crucial for tumor-host interactions and tumor resistance to chemo- or radiotherapy has also been investigated. In most of these studies, the silencing of critical gene products by RNAi technology has generated significant antiproliferative and/or proapoptotic effects in cell-culture systems or in preclinical animal models. Nevertheless, significant obstacles, such as in vivo delivery, incomplete suppression of target genes, nonspecific immune responses and the so-called off-target effects, need to be overcome before this technology can be successfully translated into the clinical arena. Significant progress has already been made in addressing some of these issues, and it is foreseen that early phase clinical trials will be initiated in the very near future.

Indexed as

RNA InterferenceDrug Resistance, NeoplasmForecastingGene TargetingGenetic TherapyHumansNeoplasmsOncogenesRNA, Small InterferingRNA, Small Interfering

Identifiers

PMID16341059

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.