Evidence map›Paper›PMID 34150139›Full record

ArticleAmerican journal of translational research2021

The value of LncRNA SNHG5 as a marker for the diagnosis and prognosis of gastric cancer.

Xiangying Li, Yaqing Du, Yunfang Wang

Open access · greenAbstract read
In one paragraph

Article in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
1.2field-weighted citation impact, top 24% 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.

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  6. Review
  7. The Functions and Mechanisms of Long Non-coding RNA SNHGs in Gastric Cancer.Combinatorial chemistry & high throughput screening · 2024
    Review
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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

3 authors at 1 institution in 1 country.

Xiangying LiThe Internal Medicine of TCM, Xingtai People's Hospital Xingtai, Hebei, China.
Yaqing DuThe Internal Medicine of TCM, Xingtai People's Hospital Xingtai, Hebei, China.
Yunfang WangThe Internal Medicine of TCM, Xingtai People's Hospital Xingtai, Hebei, China.
Hebei Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo elucidate the value of serum lncRNA SNHG5 as a marker for the diagnosis and prognosis in gastric cancer.

methodsFrom January 2017 to January 2018, serum samples were collected from 50 cases of gastric cancer patients and 50 cases of benign gastrosia who underwent operations in our hospital, and 50 cases of healthy person. We detected the expression level of serum lncRNA SNHG5 in all research targets and the expression levels of LncRNA SNHG5 in the cancer adjacent tissues and cancer tissues of gastric cancer patients to analyze the relationship between serum LncRNA SNHG5 level and clinicopathological parameters. ROC curve was used to analyze its prognostic value of patients with gastric cancer, while Cox regression model was used to analyze the survival predictors of short-term adverse events.

resultsThe expression of lncRNA SNHG5 in the serum of gastric cancer was down-regulated, lower than that in the benign gastrosia group and healthy group (

conclusionThe expression of lncRNA SNHG5 in gastric cancer patients before operation and those with poor prognosis decreased. Therefore, it is of high diagnostic value in prognosis prediction and is expected to become a new molecular marker for early diagnosis of gastric cancer.

Indexed as

diagnosisgastric cancerLncRNA SNHG5prognosis

Identifiers

PMID34150139
PMCPMC8205807
OpenAlexW3175527008

What OpenQuestion holds

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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.