Evidence map›Paper›PMID 33987091›Full record

ArticleFrontiers in oncology2021

Inferring Cell Subtypes and LncRNA Function by a Cell-Specific CeRNA Network in Breast Cancer.

Xin Chen, Jing Xu, Feng Zeng, Chao Yang, Weijun Sun, Tao Yu, Haokun Zhang, Yan Li

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 54% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Tumor immune microenvironment lncRNAs.Briefings in bioinformatics · 2022
    Review
  2. International journal of general medicine · 2021
    Article
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

8 authors at 3 institutions in 1 country.

Xin ChenSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Jing XuDepartment of Oncology, Changhai Hospital, The Naval Military Medical University, Shanghai, China.
Feng ZengSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Chao YangSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Weijun SunSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Tao YuDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Haokun ZhangSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Yan LiDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Guangdong University of Technology · CNNanjing Medical University · CNSecond Military Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell RNA sequencing is a powerful tool to explore the heterogeneity of breast cancer. The identification of the cell subtype that responds to estrogen has profound significance in breast cancer research and treatment. The transcriptional regulation of estrogen is an intricate network involving crosstalk between protein-coding and non-coding RNAs, which is still largely unknown, particularly at the single cell level. Therefore, we proposed a novel strategy to specify cell subtypes based on a cell-specific ceRNA network (CCN). The CCN was constructed by integrating a cell-specific RNA-RNA co-expression network (RCN) with an existing ceRNA network. The cell-specific RCN was built based on single cell expression profiles with predefined reference cells. Heterogeneous cell subtypes were inferred by enriching RNAs in CCN to the estrogen response hallmark. Edge biomarkers were identified in the early estrogen response subtype. Topological analysis revealed that NEAT1 was a hub lncRNA for the early response subtype, and its ceRNAs could predict patient survival. Another hub lncRNA, DLEU2, could potentially be involved in GPCR signaling, based on CCN. The CCN method that we proposed here facilitates the inference of cell subtypes from a network perspective and explores the function of hub lncRNAs, which are promising targets for RNA-based therapeutics.

Indexed as

cell-specific networkceRNAestrogen regulationlncRNAsubtype

Identifiers

PMID33987091
PMCPMC8111082
OpenAlexW3158194754

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.