Evidence map›Paper›PMID 26314850›Full record

ArticleOncotarget2015

Association of CELF2 polymorphism and the prognosis of nasopharyngeal carcinoma in southern Chinese population.

Yun-Miao Guo, Ming-Xia Sun, Jing Li, Tong-Tong Liu, Hang-Zhen Huang, Jie-Rong Chen, Wen-Sheng Liu, Qi-Sheng Feng, Li-Zhen Chen, Jin-Xin Bei and 1 more

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 35% 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

6 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Identification of miRNA/mRNA-Negative Regulation Pairs in Nasopharyngeal Carcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2016
    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

11 authors at 4 institutions in 1 country.

Yun-Miao GuoSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Ming-Xia SunSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Jing LiDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P. R. China.
Tong-Tong LiuSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Hang-Zhen HuangSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Jie-Rong ChenSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Wen-Sheng LiuSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Qi-Sheng FengSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Li-Zhen ChenSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Jin-Xin BeiSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Yi-Xin ZengSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
Sun Yat-sen University Cancer Center · CNSun Yat-sen University · CNState Key Laboratory of Oncology in South ChinaFirst Affiliated Hospital of Zhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nasopharyngeal carcinoma (NPC) is a malignancy with high metastatic potential and loco-regional recurrence. The overall survival of NPC has been limited from further improvement partly due to the lack of effective biomarker for accurate prognosis prediction and precise treatments. Here, in light of the implication of CELF gene family in cancer prognosis, we selected 112 tagging single nucleotide polymorphisms (SNPs) located in six members of the family and tested their associations with the clinical outcomes in a discovery cohort of 717 NPC patients. Survival analyses under multivariate cox proportional hazards model and Kaplan-Meier curve revealed five promising SNPs, which were further validated in another independent sample of 1,520 cases. Combined analysis revealed that SNP rs3740194 in CELF2 was significantly associated with the decreased risk of death with a Hazard ratio (HR) of 0.69 (95% confidence interval [CI] = 0.58-0.82, codominant model). Moreover, rs3740194 also showed a significant association with superior metastasis-free survival (HR = 0.69, 95% CI = 0.57-0.83, codominant model). Taken together, our findings suggested that genetic variant of rs3740194 in CELF2 gene might be a valuable predictor for NPC prognosis, and potentially useful in the personalized treatment of NPC.

Indexed as

Polymorphism, Single NucleotideAdolescentAdultAgedAged, 80 and overCarcinomaCELF ProteinsChildChinaCohort StudiesDisease-Free SurvivalFemaleGene FrequencyHumansKaplan-Meier EstimateMaleCELF2 protein, humanCELF ProteinsNerve Tissue ProteinsCELF2nasopharyngeal carcinomaprognosisSNP

Identifiers

PMID26314850
PMCPMC4694981
OpenAlexW1894676311

What OpenQuestion holds

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Registered trials

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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.