Evidence map›Paper›PMID 35379783›Full record

ReviewCell death discovery2022

Targeting lncRNAs in programmed cell death as a therapeutic strategy for non-small cell lung cancer.

Yanqin Luo, Jingyang Li, Peng Yu, Jiayi Sun, Yingfan Hu, Xianli Meng, Li Xiang

Open access · goldAbstract readReview
In one paragraph

Review in Cell death discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
2.8field-weighted citation impact, top 8% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. LncRNA FEZF1-AS1 promotes the tumorigenesis and suppresses ferroptosis in non-small cell lung cancer through the TNF-α/NF-κB pathway.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  4. Review
  5. Review
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Role of autophagy and its regulation by noncoding RNAs in ovarian cancer.Experimental biology and medicine (Maywood, N.J.) · 2023
    Review
  14. Article
  15. lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs.Database : the journal of biological databases and curation · 2023
    Article
  16. 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

7 authors at 2 institutions in 1 country.

Yanqin LuoState Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, P. R. China.ORCID http://orcid.org/0000-0002-1595-9367
Jingyang LiState Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, P. R. China.
Peng YuState Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, P. R. China.
Jiayi SunState Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, P. R. China.
Yingfan HuSchool of Preclinical Medicine, Chengdu University, Chengdu, 610106, P. R. China.
Xianli MengState Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, P. R. China. xlm999@cdutcm.edu.cn.ORCID http://orcid.org/0000-0002-7529-5240
Li XiangState Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, P. R. China. xianglydr@cdutcm.edu.cn.ORCID http://orcid.org/0000-0002-7439-9435
Chengdu University of Traditional Chinese Medicine · CNChengdu University · CN

Funding

China Postdoctoral Science Foundation 2019M663457
6 · The paper itself

Abstract

Lung cancer is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) being the most common histological type. Owing to the limited therapeutic efficacy and side effects of currently available therapies for NSCLC, it is necessary to identify novel therapeutic targets for NSCLC. Long non-coding RNAs (lncRNAs) are non-protein-coding RNAs with a transcript length of more than 200 nucleotides, which play a vital role in the tumorigenesis and progression of multiple cancers, including NSCLC. Induction of programmed cell death (PCD) is the main mechanism leading to tumour cell death in most cancer treatments. Recent studies have demonstrated that lncRNAs are closely correlated with PCD including apoptosis, pyroptosis, autophagy and ferroptosis, which can regulate PCD and relevant death pathways to affect NSCLC progression and the efficacy of clinical therapy. Therefore, in this review, we focused on the function of lncRNAs in PCD of NSCLC and summarized the therapeutic role of targeting lncRNAs in PCD for NSCLC treatment, aiming to provide new sights into the underlying pathogenic mechanisms and propose a potential new strategy for NSCLC therapy so as to improve therapeutic outcomes with the ultimate goal to benefit the patients.

Identifiers

PMID35379783
PMCPMC8980082
OpenAlexW4224212363

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.