Evidence map›Paper›PMID 31620197›Full record

ArticleOncology letters2019

Identification of prognostic biomarkers for malignant melanoma using microarray datasets.

Guanyu Lin, Guoqian Yin, Yuyong Yan, Bojie Lin

Abstract read
In one paragraph

Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Observational
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

4 authors.

Guanyu LinDepartment of Plastic and Aesthetic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Guoqian YinDepartment of Plastic and Aesthetic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Yuyong YanDepartment of Plastic and Aesthetic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Bojie LinDepartment of Plastic and Aesthetic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malignant melanoma is one of the most common types of cancer worldwide. Efforts have been made to elucidate the pathology of malignant melanoma. However, its molecular mechanisms remain unclear. Therefore, the microarray datasets GSE3189, GSE4570 and GSE4587 from the Gene Expression Omnibus database were used for the elucidation of candidate genes involved in the initiation and progression of melanoma. Assessment of the microarray datasets led to the identification of differentially expressed genes (DEGs), which were subsequently used for function enrichment analysis. These data were utilized in the construction of the protein-protein interaction network and module analysis was conducted using STRING and Cytoscape software. The results of these analyses led to the identification of a total of 182 DEGs, including 52 downregulated and 130 upregulated genes. The functions and pathways found to be enriched in the DEGs were GTPase activity, transcription from RNA polymerase II promoter, apoptotic processes, cell adhesion, membrane related pathways, calcium signaling cascade and the PI3K-Akt signaling pathway. The identified genes were demonstrated to belong to a set of 10 hub genes biologically involved in proliferation, apoptosis, cytokinesis, adhesion and migration. Survival analysis and Oncomine database analysis revealed that the calmodulin gene family,

Indexed as

BAXdifferentially expressed genesmalignant melanomamicroarrayprognosis

Identifiers

PMID31620197
PMCPMC6788168

What OpenQuestion holds

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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.