Evidence map›Paper›PMID 30925905›Full record

ArticleBMC medical genetics2019

Weighted correlation network and differential expression analyses identify candidate genes associated with BRAF gene in melanoma.

Bin Zhao, Yanqiu You, Zheng Wan, Yunhan Ma, Yani Huo, Hongyi Liu, Yuanyuan Zhou, Wei Quan, Weibin Chen, Xiaohong Zhang and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

8 citing papers in PubMed, 23 citations in OpenAlex.

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  7. Powerful quantifiers for cancer transcriptomics.World journal of clinical oncology · 2020
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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

12 authors at 2 institutions in 1 country.

Bin ZhaoSchool of Medicine, Xiamen University, Xiamen, Fujian, China.ORCID 0000-0002-8165-3315
Yanqiu YouThe Department of Clinical Laboratory, the Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Zheng WanSchool of Medicine, Xiamen University, Xiamen, Fujian, China.
Yunhan MaSchool of Medicine, Xiamen University, Xiamen, Fujian, China.
Yani HuoSchool of Medicine, Xiamen University, Xiamen, Fujian, China.
Hongyi LiuSchool of Medicine, Xiamen University, Xiamen, Fujian, China.
Yuanyuan ZhouSchool of Medicine, Xiamen University, Xiamen, Fujian, China.
Wei QuanSchool of Medicine, Xiamen University, Xiamen, Fujian, China.
Weibin ChenSchool of Medicine, Xiamen University, Xiamen, Fujian, China.
Xiaohong ZhangSchool of Medicine, Xiamen University, Xiamen, Fujian, China.
Fujun LiThe Department of Anesthesiology, the First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Yilin ZhaoSchool of Medicine, Xiamen University, Xiamen, Fujian, China. zyllbz@gmail.com.
Xiamen University · CNHarbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrimary cutaneous malignant melanoma is a cancer of the pigment cells of the skin, some of which are accompanied by BRAF mutation. Melanoma incidence and mortality rates have been rising around the world. As the current knowledge about pathogenesis, clinical and genetic features of cutaneous melanoma is not very clear, we aim to use bioinformatics to identify the potential key genes involved in the expression and mutation status of BRAF.

methodsFirstly, we used UCSC public hub datasets of melanoma (Lin et al., Cancer Res 68(3):664, 2008) to perform weighted genes co-expression network analysis (WGCNA) and differentially expressed genes analysis (DEGs), respectively. Secondly, overlapping genes between significant gene modules and DEGs were screened and validated at transcriptional levels and overall survival in TCGA and GTEx datasets. Lastly, the functional enrichment analysis was accomplished to find biological functions on the web-server database.

resultsWe performed weighted correlation network and differential expression analyses, using gene expression data in melanoma samples. We identified 20 genes whose expression was correlated with the mutation status of BRAF. For further validation, three of these genes (CYR61, DUSP1, and RNASE4) were found to have similar expression patterns in skin tumors from TCGA compared with normal skin samples from GTEx. We also found that weak expression of these three genes was associated with worse overall survival in the TCGA data. These three genes were involved in the nucleic acid metabolic process.

conclusionIn this study, CYR61, DUSP1, and RNASE4 were identified as potential genes of interest for future molecular studies in melanoma, which would improve our understanding of its causes and underlying molecular events. These candidate genes may provide a promising avenue of future research for therapeutic targets in melanoma.

Indexed as

Gene Regulatory NetworksMutationCutaneous Malignant MelanomaCysteine-Rich Protein 61Dual Specificity Phosphatase 1Gene Expression ProfilingGene Expression Regulation, NeoplasticGenetic Association StudiesHumansMelanomaOligonucleotide Array Sequence AnalysisProto-Oncogene Proteins B-rafRibonucleasesSkin NeoplasmsSurvival AnalysisBRAF protein, humanCCN1 protein, humanCysteine-Rich Protein 61Dual Specificity Phosphatase 1DUSP1 protein, humanProto-Oncogene Proteins B-rafribonuclease 4RibonucleasesBRAF geneDifferentially expressed genesMelanomaOverall survivalWeighted gene co-expression network analysis

Identifiers

PMID30925905
PMCPMC6441238
OpenAlexW2947821829

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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.