The integrative analysis of DNA methylation and mRNA expression profiles confirmed the role of selenocompound metabolism pathway in Kashin-Beck disease

Staphylococcus aureus- induced mastitis is one of the most intractable problems for the dairy industry, which causes loss of milk yield and early slaughter of cows worldwide. Few studies have used a comprehensive approach based on the integrative analysis of miRNA and mRNA expression profiles to explore molecular mechanism in bovine mastitis caused by S. aureus. In this study, S. aureus (A1, B1 and C1) and sterile phosphate buffered saline (PBS) (A2, B2 and C2) were introduced to different udder quarters of three individual cows, and transcriptome sequencing and microarrays were utilized to detected miRNA and gene expression in mammary glands from the challenged and control groups. A total of 77 differentially expressed microRNAs (DE miRNAs) and 1625 differentially expressed genes (DEGs) were identified. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that multiple DEGs were enriched in significant terms and pathways associated with immunity and inflammation. Integrative analysis between DE miRNAs and DEGs proved that miR-664b, miR-23b-3p, miR-331-5p, miR-19b and miR-2431-3p were potential factors regulating the expression levels of CD14 Molecule (CD14), G protein subunit gamma 2 (GNG2), interleukin 17A (IL17A), collagen type IV alpha 1 chain (COL4A1), microtubule associated protein RP/EB family member 2 (MAPRE2), member of RAS oncogene family (RAP1B), LDOC1 regulator of NFKB signaling (LDOC1), low-density lipoprotein receptor (LDLR) and S100 calcium binding protein A9 (S100A9) in bovine mastitis caused by S. aureus. These findings could enhance the understanding of the underlying immune response in bovine mammary glands against S. aureus infection and provide a useful foundation for future application of the miRNA–mRNA-based genetic regulatory network in the breeding cows resistant to S. aureus.

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DDR1 methylation is associated with bipolar disorder and the isoform expression and methylation of myelin genes

Epigenomics ◽

10.2217/epi-2021-0006 ◽

2021 ◽

Author(s):

Beatriz Garcia-Ruiz ◽

Manuel Castro de Moura ◽

Gerard Muntané ◽

Lourdes Martorell ◽

Elena Bosch ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Bipolar Disorder ◽

Mrna Expression ◽

Gene Expression Analysis ◽

Mrna Levels ◽

Healthy Controls ◽

Genome Wide ◽

Isoform Expression

Aim: To investigate DDR1 methylation in the brains of bipolar disorder (BD) patients and its association with DDR1 mRNA levels and comethylation with myelin genes. Materials & methods: Genome-wide profiling of DNA methylation (Infinium MethylationEPIC BeadChip) corrected for glial composition and DDR1 gene expression analysis in the occipital cortices of individuals with BD (n = 15) and healthy controls (n = 15) were conducted. Results: DDR1 5-methylcytosine levels were increased and directly associated with DDR1b mRNA expression in the brains of BD patients. We also observed that DDR1 was comethylated with a group of myelin genes. Conclusion: DDR1 is hypermethylated in BD brain tissue and is associated with isoform expression. Additionally, DDR1 comethylation with myelin genes supports the role of this receptor in myelination.

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Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.675197 ◽

2021 ◽

Vol 12 ◽

Author(s):

Min Zhou ◽

Shasha Hong ◽

Bingshu Li ◽

Cheng Liu ◽

Ming Hu ◽

...

Keyword(s):

Ovarian Cancer ◽

Dna Methylation ◽

Mrna Expression ◽

Risk Score ◽

Cox Regression ◽

Expression Profiles ◽

Figo Stage ◽

Prognostic Indicator ◽

Tissue Expression ◽

The Cancer Genome Atlas

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

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A Prognostic Nomogram Model Based on mRNA Expression of DNA Methylation-Driven Genes for Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.584733 ◽

2020 ◽

Vol 10 ◽

Author(s):

Zuhua Chen ◽

Bo Liu ◽

Minxiao Yi ◽

Hong Qiu ◽

Xianglin Yuan

Keyword(s):

Gastric Cancer ◽

Dna Methylation ◽

Mrna Expression ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Dna Hypomethylation

PurposeThe exploration and interpretation of DNA methylation-driven genes might contribute to molecular classification, prognostic prediction and therapeutic choice. In this study, we built a prognostic risk model via integrating analysis of the transcriptome and methylation profile for patients with gastric cancer (GC).MethodsThe mRNA expression profiles, DNA methylation profiles and corresponding clinicopathological information of 415 GC patients were downloaded from The Cancer Genome Atlas (TCGA). Differential expression and correlation analysis were performed to identify DNA methylation-driven genes. The candidate genes were selected by univariate Cox regression analyses followed by the least absolute shrinkage and selection operator (LASSO) regression. A prognostic risk nomogram model was then built together with clinicopathological parameters.Results5 DNA methylation-driven genes (CXCL3, F5, GNAI1, GAMT and GHR) were identified by integrated analyses and selected to construct the prognostic risk model with clinicopathological parameters. High expression and low DNA hypermethylation of F5, GNAI1, GAMT and GHR, as well as low expression and high DNA hypomethylation of CXCL3 were significantly associated with poor prognosis rates, respectively. The high-risk group showed a significantly shorter prognosis than the low-risk group in the TCGA dataset (HR = 0.212, 95% CI = 0.139–0.322, P = 2e-15). The final nomogram model showed high predictive efficiency and consistency in the training and validation group.ConclusionWe construct and validate a prognostic nomogram model for GC based on five DNA methylation-driven genes with high performance and stability. This nomogram model might be a powerful tool for prognosis evaluation in the clinic and also provided novel insights into the epigenetics in GC.

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Unique MicroRNA and mRNA Interactions in EGFR-Mutated Lung Adenocarcinoma

Journal of Clinical Medicine ◽

10.3390/jcm7110419 ◽

2018 ◽

Vol 7 (11) ◽

pp. 419 ◽

Cited By ~ 3

Author(s):

Sophia Subat ◽

Kentaro Inamura ◽

Hironori Ninomiya ◽

Hiroko Nagano ◽

Sakae Okumura ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Mrna Expression ◽

Regulatory Network ◽

Egfr Mutation ◽

Expression Profiles ◽

Integrative Analysis ◽

Future Research ◽

Mutation Status ◽

Egfr Mutated ◽

Insight Into

The EGFR gene was one of the first molecules to be selected for targeted gene therapy. EGFR-mutated lung adenocarcinoma, which is responsive to EGFR inhibitors, is characterized by a distinct oncogenic pathway in which unique microRNA (miRNA)–mRNA interactions have been observed. However, little information is available about the miRNA–mRNA regulatory network involved. Both miRNA and mRNA expression profiles were investigated using microarrays in 155 surgically resected specimens of lung adenocarcinoma with a known EGFR mutation status (52 mutated and 103 wild-type cases). An integrative analysis of the data was performed to identify the unique miRNA–mRNA regulatory network in EGFR-mutated lung adenocarcinoma. Expression profiling of miRNAs and mRNAs yielded characteristic miRNA/mRNA signatures (19 miRNAs/431 mRNAs) in EGFR-mutated lung adenocarcinoma. Five of the 19 miRNAs were previously listed as EGFR-mutation-specific miRNAs (i.e., miR-532-3p, miR-500a-3p, miR-224-5p, miR-502-3p, and miR-532-5p). An integrative analysis of miRNA and mRNA expression revealed a refined list of putative miRNA–mRNA interactions, of which 63 were potentially involved in EGFR-mutated tumors. Network structural analysis provided a comprehensive view of the complex miRNA–mRNA interactions in EGFR-mutated lung adenocarcinoma, including DUSP4 and MUC4 axes. Overall, this observational study provides insight into the unique miRNA–mRNA regulatory network present in EGFR-mutated tumors. Our findings, if validated, would inform future research examining the interplay of miRNAs and mRNAs in EGFR-mutated lung adenocarcinoma.

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