Evidence map›Paper›PMID 38468244›Full record

ReviewCancer cell international2024

Decoding contextual crosstalk: revealing distinct interactions between non-coding RNAs and unfolded protein response in breast cancer.

Negin Karamali, Arshia Daraei, Arman Rostamlou, Roya Mahdavi, Zahra Akbari Jonoush, Nooshin Ghadiri, Zahra Mahmoudi, Amirhossein Mardi, Moslem Javidan, Sepideh Sohrabi and 1 more

Open access · goldAbstract readReview
In one paragraph

Review in Cancer cell international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
4.6field-weighted citation impact, top 5% 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. Integrative machine learning and neural networks for identifying PANoptosis-related lncRNA molecular subtypes and constructing a predictive model for head and neck squamous cell carcinoma.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024
    Article
  10. 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

11 authors at 5 institutions in 2 countries.

Negin KaramaliDepartment of Immunology, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Arshia DaraeiDepartment of Immunology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Arman RostamlouDepartment of Medical Biology, School of Medicine, University of EGE, Bornova, Izmir, Turkey.
Roya MahdaviStudent Research Committee, Ahvaz Jundishapur University of Medical Science, Ahvaz, Iran.
Zahra Akbari JonoushStudent Research Committee, Ahvaz Jundishapur University of Medical Science, Ahvaz, Iran.
Nooshin GhadiriStudent Research Committee, Ahvaz Jundishapur University of Medical Science, Ahvaz, Iran.
Zahra MahmoudiDepartment of Immunology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Amirhossein MardiDepartment of Immunology, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Moslem JavidanStudent Research Committee, Ahvaz Jundishapur University of Medical Science, Ahvaz, Iran.
Sepideh SohrabiDepartment of Immunology, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Behzad BaradaranDepartment of Immunology, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran. behzad_im@yahoo.com.
Ahvaz Jundishapur University of Medical Sciences · IRTabriz University of Medical Sciences · IREge University · TRKermanshah University of Medical Sciences · IRShiraz University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is significantly influenced by endoplasmic reticulum (ER) stress, impacting both its initiation and progression. When cells experience an accumulation of misfolded or unfolded proteins, they activate the unfolded protein response (UPR) to restore cellular balance. In breast cancer, the UPR is frequently triggered due to challenging conditions within tumors. The UPR has a dual impact on breast cancer. On one hand, it can contribute to tumor growth by enhancing cell survival and resistance to programmed cell death in unfavorable environments. On the other hand, prolonged and severe ER stress can trigger cell death mechanisms, limiting tumor progression. Furthermore, ER stress has been linked to the regulation of non-coding RNAs (ncRNAs) in breast cancer cells. These ncRNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), play essential roles in cancer development by influencing gene expression and cellular processes. An improved understanding of how ER stress and ncRNAs interact in breast cancer can potentially lead to new treatment approaches. Modifying specific ncRNAs involved in the ER stress response might interfere with cancer cell survival and induce cell death. Additionally, focusing on UPR-associated proteins that interact with ncRNAs could offer novel therapeutic possibilities. Therefore, this review provides a concise overview of the interconnection between ER stress and ncRNAs in breast cancer, elucidating the nuanced effects of the UPR on cell fate and emphasizing the regulatory roles of ncRNAs in breast cancer progression.

Indexed as

Breast cancerER stressNon-coding RNAsTherapyUPR

Identifiers

PMID38468244
PMCPMC10926595
OpenAlexW4392657856

What Socratic 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 Socratic graph.