Background Breast cancer (BC) is a common malignant tumor and its incidence and mortality rates are ranked first among female cancers. So far, there has been no effective biomarkers for BC prognosis. Methods The DNA methylation data of BC was downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus, and Functional ANnoTation of The Mammalian Genome databases. The RNA‐Seq data and clinical information of patients were downloaded from TCGA. R packages edgeR and minfi were used for differentially methylated genes (DMGs) screening. Then, the DMGs were collected for gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis by the online tool database for annotation, visualization and integrated discovery (DAVID) and Reactome. Cox regression analysis was used to screen candidate differentially methylated sites (DMSs) for BC prognosis. Logrank test was used to explore the correlation between DMSs and survival time. Correlation analysis was used to investigate the correlation between DNA methylation and gene expression. Results We identified 276 DMGs which contained 1454 DMSs in those three datasets. Also, six DMGs that contained seven DMSs were identified by Cox regression analysis. Interestingly, their expression levels were negatively correlated with the DNA methylation level and not affected by age, subtypes, or tumor stages. Conclusions We proposed that these seven differentially DNA methylation sites can be used as a novel prognostic biomarker for BC area under curve (AUC) = 0.74), which may facilitate research and benefit the clinical treatment of BC.

Epigenetic alterations are crucial to oncogenesis and regulation of gene expression in non–small-cell lung carcinoma (NSCLC). DNA methylation (DNAm) biomarkers may provide molecular-level prediction of relapse risk in cancer. Identification of optimal treatment is warranted for improving clinical management of NSCLC patients. Using machine learning algorithm we identified 4 recurrence predictive CpG methylation markers (cg00253681/ART4, cg00111503/KCNK9, cg02715629/FAM83A, cg03282991/C6orf10) and constructed a risk score model that potently predicted recurrence-free survival and prognosis for patients with NSCLC (P = 0.0002). Integrating genomic, transcriptomic, proteomic and clinical data, the DNAm-based risk score was observed to significantly associate with clinical stage, cell proliferation markers, somatic alterations, tumor mutation burden (TMB) as well as DNA damage response (DDR) genes, and potentially predict the efficacy of immunotherapy. In general, our identified DNAm signature shows a significant correlation to TMB and DDR pathways, and serves as an effective biomarker for predicting NSCLC recurrence and response to immunotherapy. These findings demonstrate the utility of 4-DNAm-marker panel in the prognosis, treatment decision-making and evaluation of therapeutic responses for NSCLC.

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Identification of novel clinical biomarker in clear cell renal carcinoma (ccRCC) is warranted. Integrating transcriptome (n=1669), DNA methylation (n=577) and copy number data (n=832), we developed a method to identify driver biomarkers by analyzing the omics-level dynamics of Epithelial-Mesenchymal Transition (EMT)related genes in ccRCC. We first identified 504 expression dynamic changed genes involved in ccRCC-associated key pathways such as EMT, cell cycle, EGFR and PI3K/AKT signaling. Further analysis identified 229 (90 gene promoters) aberrant expression quantitative trait methylation (eQTM) and 256 genes with expression quantitative trait copy number (eQTCN) alterations. Among them, FOXM1 was affected by both eQTM and eQTCN. FOXM1 copy number amplification (115/500, 23% of patients), occurred in an amplified peak in chromosome 12q13.3, was enriched in late-stage ccRCC samples and was associated with worse survival. FOXM1-overexpressed pT3 patients with distant metastasis showed ~25% shorter overall survival in both training (log-rank P=0.006) and validation (log-rank P=0.018) cohorts. The eQTM-gene hybrid signature (cg00044170 and FOXM1), superior to either gene expression or DNA methylation alone, showed great potential in diagnosing localized ccRCC in training (area under curve = 0.958) and validation datasets. FOXM1 could be a novel prognostic biomarker and shed light for early diagnosis at molecular level in ccRCC.

Background RNA-sequencing (RNA-seq) has been widely used to study the dynamic expression patterns of transcribed genes, which can lead to new biological insights. However, processing and analyzing these huge amounts of histological data remains a great challenge for wet labs and field researchers who lack bioinformatics experience and computational resources. Results We present BarleyExpDB, an easy-to-operate, free, and web-accessible database that integrates transcriptional profiles of barley at different growth and developmental stages, tissues, and stress conditions, as well as differential expression of mutants and populations to build a platform for barley expression and visualization. The expression of a gene of interest can be easily queried by searching by known gene ID or sequence similarity. Expression data can be displayed as a heat map, along with functional descriptions as well as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Proteins Families Database, and Simple Modular Architecture Research Tool annotations. Conclusions BarleyExpDB will serve as a valuable resource for the barley research community to leverage the vast publicly available RNA-seq datasets for functional genomics research and crop molecular breeding.