Evidence map›Paper›PMID 31949664›Full record

ArticleInternational journal of clinical and experimental pathology2018

LncRNA CERNA2 is an independent predictor for clinical prognosis and is related to tumor development in gastric cancer.

Jingjing Zhang, Xiao Han, Lei Jiang, Zhengquan Han, Zian Wang

Open access · greenAbstract read
In one paragraph

Article in International journal of clinical and experimental pathology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.1field-weighted citation impact, top 55% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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  5. 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

5 authors at 2 institutions in 1 country.

Jingjing ZhangDepartment of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College Bengbu, People's Republic of China.
Xiao HanDepartment of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College Bengbu, People's Republic of China.
Lei JiangDepartment of General Surgery, The Second Affiliated Hospital of Bengbu Medical College Bengbu, People's Republic of China.
Zhengquan HanDepartment of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College Bengbu, People's Republic of China.
Zian WangDepartment of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College Bengbu, People's Republic of China.
Bengbu Medical College · CNThe Second Affiliated Hospital of Bengbu Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer (GC) is the second most common cancer-related mortality worldwide. Mounting evidence has demonstrated that dysregulated long noncoding RNAs (lncRNAs) are involved in the development of GC. LncRNA CERNA2 (competing endogenous lncRNA 2 for microRNA let-7b) was previously found to be upregulated and play an oncogenic role in hepatocellular carcinoma, osteosarcoma and breast cancer. However, the clinical significance and biological functions of CERNA2 in GC remain largely unknown. In this study, we found that CERNA2 expression was significantly increased in GC tissues and cell lines compared to adjacent non-cancerous tissues and normal gastric epithelial cells, respectively. High levels of CERNA2 were correlated with poor clinical parameters and an unfavorable prognosis of GC patients. Moreover, silencing CERNA2 expression effectively inhibited gastric cancer cell growth and induced cell apoptosis

Indexed as

CERNA2expressionGClncRNAsprognosis

Identifiers

PMID31949664
PMCPMC6963070
OpenAlexW3007311248

What OpenQuestion holds

Textmetadata
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.