Identification of aberrantly methylated‑differentially expressed genes and gene ontology in prostate cancer

A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation

SETD2-dependent H3 Lysine-36 trimethylation (H3K36me3) has been recently linked to the deposition of de-novo DNA methylation. SETD2 is frequently mutated in cancer, however, the functional impact of SETD2 loss and depletion on DNA methylation across cancer types and tumorigenesis is currently unknown. Here, we perform a pan-cancer analysis and show that both SETD2 mutation and reduced expression are associated with DNA methylation dysregulation across 21 out of the 24 cancer types tested. In renal cancer, these DNA methylation changes are associated with altered gene expression of oncogenes, tumour suppressors, and genes involved in neoplasm invasiveness, including TP53, FOXO1, and CDK4. This suggests a new role for SETD2 loss in tumorigenesis and cancer aggressiveness through DNA methylation dysregulation. Moreover, using a robust machine learning methodology, we develop and validate a 3-CpG methylation signature which is sufficient to predict SETD2 mutation status with high accuracy and correlates with patient prognosis.

Ontologies and Knowledge Graphs in Oncology Research

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

DNA Methylation Variation Is Identified in Monozygotic Twins Discordant for Non-syndromic Cleft Lip and Palate

Non-syndromic cleft lip with or without cleft palate (NSCLP) is the most common craniofacial birth defect. The etiology of NSCLP is complex with multiple genes and environmental factors playing causal roles. Although studies have identified numerous genetic markers associated with NSCLP, the role of epigenetic variation remains relatively unexplored. Because of their identical DNA sequences, monozygotic (MZ) twins discordant for NSCLP are an ideal model for examining the potential contribution of DNA methylation to non-syndromic orofacial clefting. In this study, we compared the patterns of whole genome DNA methylation in six MZ twin pairs discordant for NSCLP. Differentially methylated positions (DMPs) and regions (DMRs) were identified in NSCLP candidate genes, including differential methylation in MAFB and ZEB2 in two independent MZ twin pairs. In addition to DNA methylation differences in NSCLP candidate genes, we found common differential methylation in genes belonging to the Hippo signaling pathway, implicating this mechanosensory pathway in the etiology of NSCLP. The results of this novel approach using MZ twins discordant for NSCLP suggests that differential methylation is one mechanism contributing to NSCLP, meriting future studies on the role of DNA methylation in familial and sporadic NSCLP.

Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers

Background

N6-methyladenosine (m6A), the most abundant chemical modification on eukaryotic messenger RNA (mRNA), is modulated by three class of regulators namely "writers," "erasers," and "readers." Increasing studies have shown that aberrant expression of m6A regulators plays broad roles in tumorigenesis and progression. However, it is largely unknown regarding the expression regulation for RNA m6A regulators in human cancers.

Results

Here we characterized the expression profiles of RNA m6A regulators in 13 cancer types with The Cancer Genome Atlas (TCGA) data. We showed that METTL14, FTO, and ALKBH5 were down-regulated in most cancers, whereas YTHDF1 and IGF2BP3 were up-regulated in 12 cancer types except for thyroid carcinoma (THCA). Survival analysis further revealed that low expression of several m6A regulators displayed longer overall survival times. Then, we analyzed microRNA (miRNA)-regulated and DNA methylation-regulated expression changes of m6A regulators in pan-cancer. In total, we identified 158 miRNAs and 58 DNA methylation probes (DMPs) involved in expression regulation for RNA m6A regulators. Furthermore, we assessed the survival significance of those regulatory pairs. Among them, 10 miRNAs and 7 DMPs may promote cancer initiation and progression; conversely, 3 miRNA/mRNA pairs in kidney renal clear cell carcinoma (KIRC) may exert tumor-suppressor function. These findings are indicative of their potential prognostic values. Finally, we validated two of those miRNA/mRNA pairs (hsa-miR-1307-3p/METTL14 and hsa-miR-204-5p/IGF2BP3) that could serve a critical role for potential clinical application in KIRC patients.

Conclusions

Our findings highlighted the importance of upstream regulation (miRNA and DNA methylation) governing m6A regulators' expression in pan-cancer. As a result, we identified several informative regulatory pairs for prognostic stratification. Thus, our study provides new insights into molecular mechanisms of m6A modification in human cancers.

Identification of Novel Biomarkers Associated With the Prognosis and Potential Pathogenesis of Breast Cancer via Integrated Bioinformatics Analysis

Background:

Breast cancer is the most commonly diagnosed malignancy and a major cause of cancer-related deaths in women globally. Identification of novel prognostic and pathogenesis biomarkers play a pivotal role in the management of the disease.

Methods:

Three data sets from the GEO database were used to identify differentially expressed genes (DEGs) in breast cancer. Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes pathway analyses were performed to elucidate the functional roles of the DEGs. Besides, we investigated the translational and protein expression levels and survival data of the DEGs in patients with breast cancer from the Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine, Human Protein Atlas, and Kaplan Meier plotter tool databases. The corresponding change in the expression level of microRNAs in the DEGs was also predicted using miRWalk and TargetScan, and the expression profiles were analyzed using OncomiR. Finally, the expression of novel DEGs were validated in Chinese breast cancer tissues by RT-qPCR.

Results:

A total of 46 DEGs were identified, and GO analysis revealed that these genes were mainly associated with biological processes involved in fatty acid, lipid localization, and regulation of lipid metabolism. Two novel biomarkers, ADH1A and IGSF10, and 4 other genes (APOD, KIT, RBP4, and SFRP1) that were implicated in the prognosis and pathogenesis of breast cancer, exhibited low expression levels in breast cancer tissues. Besides, 14/25 microRNAs targeting 6 genes were first predicted to be associated with breast cancer prognosis. RT-qPCR results of ADH1A and IGSF10 expression in Chinese breast cancer tissues were consistent with the database analysis and showed significant down-regulation.

Conclusion:

ADH1A, IGSF10, and the 14 microRNAs were found to be potential novel biomarkers for the diagnosis, treatment, and prognosis of breast cancer.