Evidence map›Paper›PMID 32695805›Full record

ArticleInternational journal of genomics2020

Identification of Prognostic Immune-Related Genes by Integrating mRNA Expression and Methylation in Lung Adenocarcinoma.

Jie Zhu, Min Wang, Daixing Hu

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Article in International journal of genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

  1. Article
  2. The Current Roadmap of Lung Cancer Biology, Genomics and Racial Disparity.International journal of molecular sciences · 2025
    Review
  3. Article
  4. Article
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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

3 authors.

Jie ZhuDepartment of Intensive Care Unit, The People's Hospital of Tongliang District, Chongqing, China.ORCID https://orcid.org/0000-0003-1610-4894
Min WangDepartment of Respiratory and Geriatrics, Chongqing Public Health Medical Center, Chongqing, China.ORCID https://orcid.org/0000-0002-7109-3203
Daixing HuDepartment of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.ORCID https://orcid.org/0000-0001-6175-6694

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is plenty of evidence showing that immune-related genes (IRGs) and epigenetic modifications play important roles in the biological process of cancer. The purpose of this study is to establish novel IRG prognostic markers by integrating mRNA expression and methylation in lung adenocarcinoma (LUAD). METHODS AND

resultsThe transcriptome profiling data and the RNA-seq data of LUAD with the corresponding clinical information of 543 LUAD cases were downloaded from The Cancer Genome Atlas (TCGA) database, which were analyzed by univariate Cox proportional regression and multivariate Cox proportional regression to develop an independent prognostic signature. On the basis of this signature, we could divide LUAD patients into the high-risk, medium-risk, and low-risk groups. Further survival analyses demonstrated that high-risk patients had significantly shorter overall survival (OS) than low-risk patients. The signature, which contains 8 IRGs (S100A16, FGF2, IGKV4-1, CX3CR1, INHA, ANGPTL4, TNFRSF11A, and VIPR1), was also validated by data from the Gene Expression Omnibus (GEO) database. We also conducted analyses of methylation levels of the relevant IRGs and their CpG sites. Meanwhile, their associations with prognosis were examined and validated by the GEO database, revealing that the methylation levels of INHA, S100A16, the CpG site cg23851011, and the CpG site cg06552037 may be used as the potential regulators for the treatment of LUAD.

conclusionCollectively, INHA, S100A16, the CpG site cg23851011, and the CpG site cg06552037 are promising biomarkers for monitoring the outcomes of LUAD.

Identifiers

PMID32695805
PMCPMC7368195

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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.