Evidence map›Paper›PMID 32318351›Full record

ArticleFrontiers in oncology2020

Identifying Long Non-coding RNA of Prostate Cancer Associated With Radioresponse by Comprehensive Bioinformatics Analysis.

Meng Xu, Shiqi Gong, Yue Li, Jun Zhou, Junhua Du, Cheng Yang, Mingwei Yang, Fan Zhang, Chaozhao Liang, Zhuting Tong

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
0.6field-weighted citation impact, top 42% 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, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. JUNB and JUND in Urological Cancers: A Literature Review.Current issues in molecular biology · 2025
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. 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

10 authors at 3 institutions in 1 country.

Meng XuDepartment of Radiation Oncology, The First Affiliated Hospital, Anhui Medical University, Hefei, China.
Shiqi GongDepartment of Otolaryngology, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
Yue LiDepartment of Radiation Oncology, The First Affiliated Hospital, Anhui Medical University, Hefei, China.
Jun ZhouDepartment of Urology, The First Affiliated Hospital, Anhui Medical University, Hefei, China.
Junhua DuDepartment of Urology, The First Affiliated Hospital, Anhui Medical University, Hefei, China.
Cheng YangDepartment of Urology, The First Affiliated Hospital, Anhui Medical University, Hefei, China.
Mingwei YangDepartment of Radiation Oncology, The First Affiliated Hospital, Anhui Medical University, Hefei, China.
Fan ZhangDepartment of Radiation Oncology, The First Affiliated Hospital, Anhui Medical University, Hefei, China.
Chaozhao LiangDepartment of Urology, The First Affiliated Hospital, Anhui Medical University, Hefei, China.
Zhuting TongDepartment of Radiation Oncology, The First Affiliated Hospital, Anhui Medical University, Hefei, China.
First Affiliated Hospital of Anhui Medical University · CNAnhui Medical University · CNRuijin Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although radiotherapy is greatly successful in the treatment of prostate cancer (PCa), radioresistance is still a major challenge in the treatment. To our knowledge, this study is the first to screen long non-coding RNAs (lncRNAs) associated with radioresponse in PCa by The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify the differentially expressed lncRNAs and protein-coding genes (PCGs) between complete response (CR) and non-complete response (non-CR) groups in radiotherapy. Statistical methods were applied to identify the correlation between lncRNAs and radioresponse as well as lncRNAs and PCGs. The correlation between PCGs and radioresponse was analyzed using weighted gene co-expression network analysis (WGCNA). The three online databases were used to predict the potential target miRNAs of lncRNAs and the miRNAs that might regulate PCGs. RT-qPCR was utilized to detect the expression of lncRNAs and PCGs in our PCa patients. A total of 65 differentially expressed lncRNAs and 468 differentially expressed PCGs were found between the two groups of PCa. After the chi-square test, LINC01600 was selected to be highly correlated with radioresponse from the 65 differentially expressed lncRNAs. Pearson correlation analysis found 558 PCGs co-expressed with LINC01600. WGCNA identified the darkred module associated with radioresponse in PCa. After taking the intersection of the darkred module of WGCNA, differentially expressed PCGs between the two groups of PCa, and the PCGs co-expressed with LINC01600, three PCGs, that is, JUND, ZFP36, and ATF3 were identified as the potential target PCGs of LINC01600. More importantly, we detected the expression of LINC01600 and three PCGs using our PCa patients, and finally verified that LINC01600 and JUND were differentially expressed between CR and non-CR groups, excluding ZFP36 and ATF3. Meantime, the potential regulation ability of LINC01600 for JUND in PCa cell lines was initially explored. In addition, we constructed the competing endogenous RNA (ceRNA) network of LINC01600-miRNA-JUND. In conclusion, we initially reveal the association of LINC01600 with radioresponse in PCa and identify its potential target PCGs for further basic and clinical research.

Indexed as

bioinformatics analysislong non-coding RNAprostate cancerradioresponseWGCNA

Identifiers

PMID32318351
PMCPMC7154134
OpenAlexW3014170703

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.