Evidence map›Paper›PMID 33791165›Full record

ArticleAmerican journal of cancer research2021

Aptamer-guided targeting of the intracellular long-noncoding RNA

Yuan-Liang Wang, Ling-Chu Chang, Kuen-Bao Chen, Shao-Chun Wang

Open access · greenAbstract read
In one paragraph

Article in American journal of cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 28% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Noncoding RNA as potential therapeutics to rescue mitochondrial dysfunction in cardiovascular diseases.American journal of physiology. Heart and circulatory physiology · 2025
    Pooled it
  2. Review
  3. Long non-coding RNAs: Key regulators of stemness in breast cancer.Frontiers in cell and developmental biology · 2025
    Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Making Sense of Antisense lncRNAs in Hepatocellular Carcinoma.International journal of molecular sciences · 2023
    Review
  9. Review
  10. Review
  11. Review
  12. 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

4 authors at 2 institutions in 2 countries.

Yuan-Liang WangCenter for Molecular Medicine, China Medical University Hospital Taichung 40402, Taiwan.
Ling-Chu ChangChinese Medicine Research and Development Center, China Medical University Hospital Taichung 40402, Taiwan.
Kuen-Bao ChenGraduate Institute of Biomedical Sciences, College of Medicine, China Medical University Taichung 40402, Taiwan.
Shao-Chun WangCenter for Molecular Medicine, China Medical University Hospital Taichung 40402, Taiwan.
China Medical University · TWAsia University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long non-coding RNAs (lncRNAs) are increasingly recognized as promising targets in cancer treatment. However, compared to targeting the ordinary protein-coding genes, suppressing non-coding RNAs expressed in cancer cells has been a more challenging task. The major hurdles lay on the requirement of a tumor-specific delivery system for the designated inhibitor to suppress the target transcripts within the cellular compartment. EGFR is a cancer driver gene which is frequently associated with the triple-negative phenotype of breast cancer. Prior studies have shown that expression of the tumor-promoting lncRNA

Indexed as

aptamerEGFRHOTAIRlong non-coding RNAreceptor tyrosine kinasesiRNATNBCtriple-negative breast cancer

Identifiers

PMID33791165
PMCPMC7994153
OpenAlexW3147538393

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.