Evidence map›Paper›PMID 31322504›Full record

ArticleAging2019

Better prognostic determination and feature characterization of cutaneous melanoma through integrative genomic analysis.

Xia Li, Yunpeng Cai

Abstract read
In one paragraph

Article in Aging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. miRNAs in the Diagnosis and Prognosis of Skin Cancer.Frontiers in cell and developmental biology · 2020
    Review
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

2 authors.

Xia LiResearch Center for Biomedical Information Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P.R. China.
Yunpeng CaiResearch Center for Biomedical Information Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Melanoma is the most dangerous type of skin cancer and has highly heterogeneous features. Despite progress in melanoma classification, interpatient heterogeneity remains difficult to predict, especially in terms of long-term survival. Here, based on mRNA-seq, miRNA-seq and DNA methylation data from 447 cutaneous melanoma patients in the Cancer Genome Atlas, we performed integrative and single-dataset clustering analyses. A novel group of patients was identified, including 301 with better, 55 with poorer and 91 with intermediate prognoses. Immune genes were upregulated in the better prognostic group, and higher immune scores (representing a greater extent of immune cell infiltration into tumor tissues) were associated with better prognoses. Higher expression of 115 genes was determined to predict better outcomes. The better prognostic group also exhibited DNA hypomethylation, and immune pathways were enriched among the hypomethylated genes. Using exome-seq data from the same patients, we observed that the better prognostic group harbored the highest number of mutations. The mutational signature in the better prognostic group was associated with ultraviolet light exposure. These integrated investigations have potential therapeutic significance, as they clarify the molecular heterogeneity of cutaneous melanoma and enhance its classification.

Indexed as

AdultAgedAged, 80 and overCutaneous Malignant MelanomaFemaleGene Expression ProfilingGenomicsHumansMaleMelanomaMiddle AgedPrognosisSkin NeoplasmsTranscriptomebetter outcomeclassificationheterogeneitymelanomamulti-omics

Identifiers

PMID31322504
PMCPMC6746212

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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.