Evidence map›Paper›PMID 28109319›Full record

ReviewChinese journal of cancer2017

For robust big data analyses: a collection of 150 important pro-metastatic genes.

Yan Mei, Jun-Ping Yang, Chao-Nan Qian

Abstract readReview
In one paragraph

Review in Chinese journal of cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

14 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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  13. Article
  14. Cancer metastasis: issues and challenges.Chinese journal of cancer · 2017
    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

3 authors.

Yan MeiState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Jun-Ping YangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Chao-Nan QianState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China. qianchn@sysucc.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metastasis is the greatest contributor to cancer-related death. In the era of precision medicine, it is essential to predict and to prevent the spread of cancer cells to significantly improve patient survival. Thanks to the application of a variety of high-throughput technologies, accumulating big data enables researchers and clinicians to identify aggressive tumors as well as patients with a high risk of cancer metastasis. However, there have been few large-scale gene collection studies to enable metastasis-related analyses. In the last several years, emerging efforts have identified pro-metastatic genes in a variety of cancers, providing us the ability to generate a pro-metastatic gene cluster for big data analyses. We carefully selected 285 genes with in vivo evidence of promoting metastasis reported in the literature. These genes have been investigated in different tumor types. We used two datasets downloaded from The Cancer Genome Atlas database, specifically, datasets of clear cell renal cell carcinoma and hepatocellular carcinoma, for validation tests, and excluded any genes for which elevated expression level correlated with longer overall survival in any of the datasets. Ultimately, 150 pro-metastatic genes remained in our analyses. We believe this collection of pro-metastatic genes will be helpful for big data analyses, and eventually will accelerate anti-metastasis research and clinical intervention.

Indexed as

Gene Expression Regulation, NeoplasticAnimalsData MiningHumansNeoplasm MetastasisNeoplasmsBig data analysisLiver cancerPro-metastatic geneRenal cancer

Identifiers

PMID28109319
PMCPMC5251273

What OpenQuestion holds

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LicenceCC BY
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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.