Evidence map›Paper›PMID 37204526›Full record

ReviewDiscover oncology2023

Non-coding RNAs in breast cancer: with a focus on glucose metabolism reprogramming.

Junjie Liang, Chun Ye, Kaiqin Chen, Zihan Gao, Fangguo Lu, Ke Wei

Open access · goldAbstract readReview
In one paragraph

Review in Discover oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 22% of its field
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, 4 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

6 authors at 1 institution in 1 country.

Junjie LiangMedical School, Hunan University of Chinese Medicine, Changsha, 410208, China.
Chun YeMedical School, Hunan University of Chinese Medicine, Changsha, 410208, China.
Kaiqin ChenMedical School, Hunan University of Chinese Medicine, Changsha, 410208, China.
Zihan GaoMedical School, Hunan University of Chinese Medicine, Changsha, 410208, China.
Fangguo LuMedical School, Hunan University of Chinese Medicine, Changsha, 410208, China.
Ke WeiMedical School, Hunan University of Chinese Medicine, Changsha, 410208, China. 004343@hnucm.edu.cn.
Hunan University of Traditional Chinese Medicine · CN

Funding

Hunan Provincial Natural Science Foundation of China 2021JJ30508Hunan Provincial Traditional Chinese Medicine Research Project 2021055National Natural Science Foundation of China 82074250The project supported by Hunan Provincial Education Department 21B0387Training Program for Excellent Young Innovators of Changsha kq2106063
6 · The paper itself

Abstract

Breast cancer is the tumor with the highest incidence in women worldwide. According to research, the poor prognosis of breast cancer is closely related to abnormal glucose metabolism in tumor cells. Changes in glucose metabolism in tumor cells are an important feature. When sufficient oxygen is available, cancer cells tend to undergo glycolysis rather than oxidative phosphorylation, which promotes rapid proliferation and invasion of tumor cells. As research deepens, targeting the glucose metabolism pathway of tumor cells is seen as a promising treatment. Non-coding RNAs (ncRNAs), a recent focus of research, are involved in the regulation of enzymes of glucose metabolism and related cancer signaling pathways in breast cancer cells. This article reviews the regulatory effect and mechanism of ncRNAs on glucose metabolism in breast cancer cells and provides new ideas for the treatment of breast cancer.

Indexed as

Breast cancer cellscircRNAGlucose metabolismlncRNAmiRNA

Identifiers

PMID37204526
PMCPMC10199155
OpenAlexW4377093943

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.