ArticleOncology letters2018
Screening of differentially methylated genes in breast cancer and risk model construction based on TCGA database.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 16 citations in OpenAlex.
- Design and Validation of a Predictive Model for Hepatocellular Carcinoma Based on Genes With Differential Expression Driven by DNA Methylation.International journal of genomics · 2026Article
- Targeting racial disparities in breast cancer: mechanistic insights and therapeutic potential of African medicinal plants.Medical oncology (Northwood, London, England) · 2025Review
- Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.Scientific reports · 2025Article
- Survival Rate and Chronic Diseases of TCGA Cancer and KoGES Normal Samples by Clustering for DNA Methylation.Life (Basel, Switzerland) · 2024Article
- Curcumin alters distinct molecular pathways in breast cancer subtypes revealed by integrated miRNA/mRNA expression analysis.Cancer reports (Hoboken, N.J.) · 2022Article
- Epigenetic Determinants of Racial Disparity in Breast Cancer: Looking beyond Genetic Alterations.Cancers · 2022Review
- A Risk Model Based on Sorafenib-Response Target Genes Predicts the Prognosis of Patients with HCC.Journal of oncology · 2022Article
- Screening for Core Genes Related to Pathogenesis of Alzheimer's Disease.Frontiers in cell and developmental biology · 2021Article
- Article
- Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival.Frontiers in oncology · 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
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differentially methylated genes in breast cancer were screened out and a prognostic risk model of breast cancer was constructed. RNA-seq data and methylation data for breast cancer-related level 3 were downloaded from The Cancer Genome Atlas (TCGA), and MethylMix R package was used to screen out differentially methylated genes in cancer tissues and normal tissues. DAVID was used to analyze the GO enrichment of differentially methylated genes, ConsensusPathDB to analyze the PATHWAY pathways of differentially methylated genes, the single factor, multivariate Cox analysis and Akaike Information Criterion (AIC) to construct the prognostic risk model of breast cancer, and the ROC curve to judge the clinical application value of the risk model. Two hundred and fifty-seven differentially methylated genes were successfully screened out in cancer tissues and normal tissues; 39 related to GO enrichments and 19 related to PATHWAY pathways were found; the best prognostic risk model was obtained, risk score =
Indexed as
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.