Evidence map›Paper›PMID 40998459›Full record

ReviewWiley interdisciplinary reviews. RNA

MicroRNAs and Cancer Racial Disparities.

Dan Zhao, Yifei Wang

Abstract readReview
In one paragraph

Review in Wiley interdisciplinary reviews. RNA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
  2. Molecules (Basel, Switzerland) · 2026
    Pooled it
  3. Article
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

2 authors.

Dan ZhaoSection of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.ORCID https://orcid.org/0000-0002-5120-6725
Yifei WangSection of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer remains one of the leading causes of death worldwide. Despite various efforts to reduce cancer mortality, such as decreasing tobacco use, improving early detection and prevention methods, and enhancing cancer care and treatments, certain racial and ethnic groups continue to experience higher cancer incidence and mortality rates, along with shorter survival compared to other groups. Several factors, including socioeconomic status, environmental influences, diet, and behavior, contribute to these racial disparities. More importantly, scientists have identified a genetic basis for these observations, with a growing body of research highlighting microRNAs as significant players in cancer racial disparities. This review focuses on various types of microRNAs (such as epigenetically regulated, copy number altered, circulating, and exosomal) and microRNA single-nucleotide variations in the context of cancer-related racial disparities. Additionally, we have summarized the existing resources, including racial-specific model cell lines and cancer cohorts that include patients from diverse racial and ethnic backgrounds. Moreover, we provide here several key things to consider for future investigations. While many challenges remain, we aim to offer a balanced overview of this field to help scientists with varying expertise address these issues. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Indexed as

Health Status DisparitiesMicroRNAsNeoplasmsHumansRacial GroupsMicroRNAscancer racial disparitiesmicroRNAsmiR genetic variations

Identifiers

PMID40998459
PMCPMC12463549

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.