Evidence map›Paper›PMID 34782661›Full record

ArticleScientific reports2021

Immune classification and identification of prognostic genes for uveal melanoma based on six immune cell signatures.

Guohong Gao, Zhilong Yu, Xiaoyan Zhao, Xinyi Fu, Shengsheng Liu, Shan Liang, Aijun Deng

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. 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
1.9field-weighted citation impact, top 15% 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, 11 citations in OpenAlex.

  1. Article
  2. Review
  3. Machine Learning Methods for Gene Selection in Uveal Melanoma.International journal of molecular sciences · 2024
    Article
  4. Article
  5. Review
  6. Advances in immunotyping of colorectal cancer.Frontiers in immunology · 2023
    Review
  7. Article
  8. 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

7 authors at 1 institution in 1 country.

Guohong Gao *Department of Ophthalmology, Affiliated Hospital of Weifang Medical University, Clinical Medical Institute, Weifang Medical University, Weifang, 261000, Shandong, China. eyeggh@163.com.
Zhilong Yu *Department of Ophthalmology, Affiliated Hospital of Weifang Medical University, Clinical Medical Institute, Weifang Medical University, Weifang, 261000, Shandong, China.
Xiaoyan ZhaoDepartment of Ophthalmology, Affiliated Hospital of Weifang Medical University, Clinical Medical Institute, Weifang Medical University, Weifang, 261000, Shandong, China.
Xinyi FuDepartment of Ophthalmology, Affiliated Hospital of Weifang Medical University, Clinical Medical Institute, Weifang Medical University, Weifang, 261000, Shandong, China.
Shengsheng LiuDepartment of Ophthalmology, Affiliated Hospital of Weifang Medical University, Clinical Medical Institute, Weifang Medical University, Weifang, 261000, Shandong, China.
Shan LiangDepartment of Ophthalmology, Affiliated Hospital of Weifang Medical University, Clinical Medical Institute, Weifang Medical University, Weifang, 261000, Shandong, China.
Aijun DengDepartment of Ophthalmology, Affiliated Hospital of Weifang Medical University, Clinical Medical Institute, Weifang Medical University, Weifang, 261000, Shandong, China.
Weifang Medical University · CN

Funding

China Disabled Persons' Federation CJFJRRB19-2019Shandong Medical and Health Science and Technology Development Program 2017WS245
6 · The paper itself

Abstract

Cutaneous melanoma could be treated by immunotherapy, which only has limited efficacy on uveal melanoma (UM). UM immunotyping for predicting immunotherapeutic responses and guiding immunotherapy should be better understood. This study identified molecular subtypes and key genetic markers associated with immunotherapy through immunosignature analysis. We screened a 6-immune cell signature simultaneously correlated with UM prognosis. Three immune subtypes (IS) were determined based on the 6-immune cell signature. Overall survival (OS) of IS3 was the longest. Significant differences of linear discriminant analysis (LDA) score were detected among the three IS types. IS3 with the highest LDA score showed a low immunosuppression. IS1 with the lowest LDA score was more immunosuppressive. LDA score was significantly negatively correlated with most immune checkpoint-related genes, and could reflect UM patients' response to anti-PD1 immunotherapy. Weighted correlation network analysis (WGCNA) identified that salmon, purple, yellow modules were related to IS and screened 6 prognostic genes. Patients with high-expressed NME1 and TMEM255A developed poor prognosis, while those with high-expressed BEX5 and ROPN1 had better prognosis. There was no notable difference in OS between patients with high-expressed LRRN1 and ST13 and those with low-expressed LRRN1 and ST13. NME1, TMEM255A, Bex5 and ROPN1 showed potential prognostic significance in UM.

Indexed as

TranscriptomeBiomarkers, TumorCluster AnalysisComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansImmune Checkpoint ProteinsImmunomodulationMelanomaMolecular Sequence AnnotationMolecular Targeted TherapyProportional Hazards ModelsROC CurveSignal TransductionBiomarkers, TumorImmune Checkpoint Proteins

Identifiers

PMID34782661
PMCPMC8593069
OpenAlexW3213111046

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.