ArticleInternational journal of genomics2020
Identification of Prognostic Immune-Related Genes by Integrating mRNA Expression and Methylation in Lung Adenocarcinoma.
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.
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.
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.
Who cites it
6 citing papers in PubMed.
- Aging-related signature stratifies LUAD prognosis and uncovers senescence-induced chemoresistance via NF-κB/RELA activation.Respiratory research · 2025Article
- The Current Roadmap of Lung Cancer Biology, Genomics and Racial Disparity.International journal of molecular sciences · 2025Review
- Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.Journal of cellular and molecular medicine · 2024Article
- Article
- Machine learning and bioinformatics analysis revealed classification and potential treatment strategy in stage 3-4 NSCLC patients.BMC medical genomics · 2022Article
- Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis.Frontiers in genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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
What OpenQuestion holds
Registered trials
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.