Evidence map›Paper›PMID 33239893›Full record

ArticleOncoTargets and therapy2020

Identifying a Potential Key Gene, TIMP1, Associated with Liver Metastases of Uveal Melanoma by Weight Gene Co-Expression Network Analysis.

Ping Wang, Xuan Yang, Nan Zhou, Jinyuan Wang, Yang Li, Yueming Liu, Xiaolin Xu, Wenbin Wei

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in OncoTargets and therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-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

3 citing papers in PubMed, 6 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

8 authors at 1 institution in 1 country.

Ping WangBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, People's Republic of China.
Xuan YangBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, People's Republic of China.
Nan ZhouBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, People's Republic of China.
Jinyuan WangBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, People's Republic of China.ORCID 0000-0002-8378-1249
Yang LiBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, People's Republic of China.
Yueming LiuBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, People's Republic of China.
Xiaolin XuBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, People's Republic of China.
Wenbin WeiBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, People's Republic of China.
Beijing Tongren Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeUveal melanoma (UM) is a primary intraocular tumor in adults, with a high percentage of metastases to the liver. Identifying potential key genes may provide information for early detection and prognosis of UM metastasis. PATIENTS AND

methodsDifferentially expressed genes (DEGs) were identified using the GSE22138 dataset. Weighted gene co-expression network analysis was used to construct co-expression modules. Functional enrichment analysis was performed for DEGs and genes of key modules. Hub genes were screened by co-expression network and protein-protein interaction network (PPI), and validated by survival analysis in The Cancer Genome Atlas database. Gene set enrichment analysis (GSEA) was used to explore the potential metastasis mechanism of UM. Transient transfection was used to investigate the effect of TIMP1 on the proliferation, migration, and invasion of UM cells.

resultsIn total, 552 DEGs were identified between primary and metastatic UM and mainly enriched in extracellular matrix, cellular senescence and focal adhesion pathway. A weighted gene co‑expression network was built to identify key gene modules associated with UM metastasis (n=36). The turquoise module is positively correlated with metastasis and genes in this module were mainly enriched in peptidyl-tyrosine autophosphorylation and regulation of organ growth. The hub gene

conclusionOur study indicates that

Indexed as

GO analysisliver metastasespathway analysisuveal melanomaweighted gene co-expression network analysisWGCNA

Identifiers

PMID33239893
PMCPMC7682792
OpenAlexW3101464505

What OpenQuestion holds

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LicenceCC BY-NC
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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.