Evidence map›Paper›PMID 35634537›Full record

ReviewCancer management and research2022

Long Noncoding RNAs in Lung Cancer: From Disease Markers to Treatment Roles.

Lin Chen, Yuxin Xie, Min Yu, Qiheng Gou

Open access · goldAbstract readReview
In one paragraph

Review in Cancer management and research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 34% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
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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 1 institution in 1 country.

Lin Chen *Department of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
Yuxin Xie *Department of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
Min YuDepartment of Thoracic Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
Qiheng GouDepartment of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is an urgent need to identify reliable biomarkers that can be used in early diagnosis, prognostication prediction and as possible therapeutic targets for lung cancer due to its current poor prognosis. Long noncoding RNAs (lncRNAs) have recently attracted additional attention due to their potential role in carcinogenesis, invasion and metastasis. Issues involved in the biofunctions and regulatory mechanisms of oncogenic and tumor-suppressive lncRNAs in lung cancer are discussed. Some lncRNAs have shown good diagnostic value, especially in combination with conventional serum protein markers. The use of antisense oligonucleotides, small molecules and RNA interference techniques have shown promise as direct therapeutic tools for targeting lncRNAs in preclinical studies. The biomarker function of lncRNAs may also indirectly involved in tumor therapy as a reference to conventional therapy. Overall, the concept of using lncRNAs as biomarkers for prognostication and intervention in lung cancer is still in its infancy, and only with more in-depth studies could they have a significant impact.

Indexed as

biomarkerdiagnosislong noncoding RNAslung cancertreatment

Identifiers

PMID35634537
PMCPMC9132104
OpenAlexW4281256895

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.