Evidence map›Paper›PMID 33061850›Full record

ArticleCancer cell international2020

Identified

Donglan Huang, Qianqian Liu, Weijun Zhang, Chunyue Huang, Ronghui Zheng, Guofeng Xie, Hongmei Wang, Baochang Jia, Jianjun Shi, Yawei Yuan and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cancer cell international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.6field-weighted citation impact, top 40% 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

9 citing papers in PubMed, 16 citations in OpenAlex.

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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

11 authors at 2 institutions in 1 country.

Donglan Huang *Department of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.ORCID 0000-0002-2585-0568
Qianqian Liu *Department of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Weijun Zhang *Department of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Chunyue HuangDepartment of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Ronghui ZhengDepartment of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Guofeng XieDepartment of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Hongmei WangDepartment of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Baochang JiaDepartment of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Jianjun ShiDepartment of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Yawei YuanDepartment of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Min DengDepartment of Radiation Oncology, Institute of Cancer Research, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Guangzhou Medical University · CNGuangzhou Medical University Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite improvements in nasopharyngeal carcinoma (NPC) treatment, patients with recurrence and metastasis still have a poor prognosis. Thus, the identification of novel biomarkers is urgently needed to predict outcomes and tailor treatment for NPC.

methodsFour data sets were downloaded from Gene Expression Omnibus, and one data set GSE68799 of which was applied to filtrate key modules and hub genes by construction of a co-expression network. Other data sets (GSE12452 and GSE53819) were used to verify hub genes. The data set GSE102349 was devoted to identify prognostic hub genes by survival analysis. To explored whether prognostic hub genes are related to hypoxia signatures in NPC, correlation analysis was carried out, and followed by functional verification experiments of those genes in vitro.

resultsBy co-expression network analysis, blue module was regarded as a key module in the benign and malignant group, and

conclusion

Indexed as

Bioinformatics analysisHypoxiaIGSF9Nasopharyngeal carcinomaPrognosis

Identifiers

PMID33061850
PMCPMC7552377
OpenAlexW3092078323

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.