Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package

DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test using . Then we applied a deep learning method, “nnet” to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) . After performing deep learning analysis, we obtained average classification accuracy () of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis using . We reported five top in-degree genes (, , , and ) and five top out-degree genes (, , , and ). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using tool WebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study.

BackgroundWhile Alzheimer’s disease (AD) remains one of the most challenging diseases to tackle, genome-wide genetic/epigenetic studies reveal many disease-associated risk loci, which sheds new light onto disease heritability, provides novel insights to understand its underlying mechanism and potentially offers easily measurable biomarkers for early diagnosis and intervention.MethodsWe analyzed whole-genome DNA methylation data collected from peripheral blood in a cohort (n = 649) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and compared the DNA methylation level at baseline among participants diagnosed with AD (n = 87), mild cognitive impairment (MCI, n = 175) and normal controls (n = 162), to identify differentially methylated regions (DMRs). We also leveraged up to 4 years of longitudinal DNA methylation data, sampled at approximately 1 year intervals to model alterations in methylation levels at DMRs to delineate methylation changes associated with aging and disease progression, by linear mixed-effects (LME) modeling for the unchanged diagnosis groups (AD, MCI and control, respectively) and U-shape testing for those with changed diagnosis (converters).ResultsWhen compared with controls, patients with MCI consistently displayed promoter hypomethylation at methylation QTL (mQTL) gene locus PM20D1. This promoter hypomethylation was even more prominent in patients with mild to moderate AD. This is in stark contrast with previously reported hypermethylation in hippocampal and frontal cortex brain tissues in patients with advanced-stage AD at this locus. From longitudinal data, we show that initial promoter hypomethylation of PM20D1 during MCI and early stage AD is reversed to eventual promoter hypermethylation in late stage AD, which helps to complete a fuller picture of methylation dynamics. We also confirm this observation in an independent cohort from the Religious Orders Study and Memory and Aging Project (ROSMAP) Study using DNA methylation and gene expression data from brain tissues as neuropathological staging (Braak score) advances.ConclusionsOur results confirm that PM20D1 is an mQTL in AD and demonstrate that it plays a dynamic role at different stages of the disease. Further in-depth study is thus warranted to fully decipher its role in the evolution of AD and potentially explore its utility as a blood-based biomarker for AD.

DNA methylation plays a critical role in breast cancer development. Previous studies have identified DNA methylation marks in white blood cells as promising biomarkers for breast cancer. However, these studies were limited by low statistical power and potential biases. Using a new methodology, we investigated DNA methylation marks for their associations with breast cancer risk. Statistical models were built to predict levels of DNA methylation marks using genetic data and DNA methylation data from HumanMethylation450 BeadChip from the Framingham Heart Study (n = 1595). The prediction models were validated using data from the Women's Health Initiative (n = 883). We applied these models to genomewide association study (GWAS) data of 122 977 breast cancer patients and 105 974 controls to evaluate if the genetically predicted DNA methylation levels at CpG sites (CpGs) are associated with breast cancer risk. All statistical tests were two-sided. Of the 62 938 CpG sites CpGs investigated, statistically significant associations with breast cancer risk were observed for 450 CpGs at a Bonferroni-corrected threshold of P less than 7.94 × 10-7, including 45 CpGs residing in 18 genomic regions, that have not previously been associated with breast cancer risk. Of the remaining 405 CpGs located within 500 kilobase flaking regions of 70 GWAS-identified breast cancer risk variants, the associations for 11 CpGs were independent of GWAS-identified variants. Integrative analyses of genetic, DNA methylation, and gene expression data found that 38 CpGs may affect breast cancer risk through regulating expression of 21 genes. Our new methodology can identify novel DNA methylation biomarkers for breast cancer risk and can be applied to other diseases.

Aberrant hypomethylated STAT3 was identified as a biomarker of chronic benzene poisoning through integrating DNA methylation and mRNA expression data

Aberrant DNA methylation is a hallmark of many cancer types. Despite our knowledge of epigenetic and transcriptomic alterations in lung adenocarcinoma (LUAD), we lack robust multi-modal molecular classifications for patient stratification. This is partly because the impact of epigenetic alterations on lung cancer development and progression is still not fully understood. To that end, we identified disease-associated processes under epigenetic regulation in LUAD. We performed a genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis by integrating DNA methylation and gene expression data from 453 patients in the TCGA cohort. Using a community detection algorithm, we identified distinct communities of CpG-gene associations with diverse biological processes. Interestingly, we identified a community linked to hormone response and lipid metabolism; the identified CpGs in this community were enriched in enhancer regions and binding regions of transcription factors such as FOXA1/2, GRHL2, HNF1B, AR, and ESR1. Furthermore, the CpGs were connected to their associated genes through chromatin interaction loops. These findings suggest that the expression of genes involved in hormone response and lipid metabolism in LUAD is epigenetically regulated through DNA methylation and enhancer-promoter interactions. By applying consensus clustering on the integrated expression-methylation pattern of the emQTL-genes and CpGs linked to hormone response and lipid metabolism, we further identified subclasses of patients with distinct prognoses. This novel patient stratification was validated in an independent patient cohort of 135 patients and showed increased prognostic significance compared to previously defined molecular subtypes.

Global methylation analysis identifies prognostically important epigenetically inactivated tumor suppressor genes in multiple myeloma

IntroductionDetecting declines in cognitive function is a critical global health concern, highlighting the need for timely identification to implement effective intervention strategies. This study investigates the potential of blood‐based biomarkers as accurate and non‐invasive measures of cognitive function. We developed a novel deep learning architecture that integrates multi‐omics data by considering their relationship. Specifically, we integrated single nucleotide polymorphisms (SNPs), gene expression, and DNA methylation data to predict cognitive test scores.MethodsA novel computational architecture called Hierarchical Input Neural Network (HINN) (Figure 1) was developed to integrate multi‐omics datasets. In this study, HINN had three input layers receiving SNPs, DNA Methylation, and gene expression data, respectively. First, we conducted multiple GWAS runs to identify significant SNPs associated with cognitive scores based on different cognitive tests and found 373 SNPs across these runs. We identified 13037 DNA methylation sites proximal to these SNPs and 455 genes associated with these probes.ResultsWe observed that MAE of the HINN model for MMSE, MoCA, ADAS11, and RAVLT‐I was 2.28, 2.98, 4.54, and 8.88, respectively. Similarly, MSE of the same model for MMSE, MoCA, ADAS11, and RAVLT‐I was 10.41, 17.16, 45.30, and 135.59, respectively. We compared our model with the other baseline models, namely L1‐regularized, support vector machine, random forest, and deep neural network. We also compared HINN with the pathway guided deep neural network in which all omics data were fed to single layer, which was connected based on biological processes relationship. The HINN model showed improvement of 2.98% to 9.81% for MAE and 3.99% to 12.3% for MSE in comparison to all other models (Figure 2).ConclusionThe findings derived from the HINN model highlight its efficacy in detecting novel biomarkers for cognitive assessment. Through the hierarchical input approach, we outperformed baseline methods to predict cognitive score. Notably, our method is characterized by its independence from assumptions and subjective scoring, ensuring the robustness and reliability of predictions across diverse cognitive measures. The versatility of our model, capable of predicting multiple cognitive metrics, amplifies its utility and confidence in biological studies targeting cognitive assessment.

BackgroundThe Cancer Genome Atlas (TCGA) offers a multilayered view of genomics and epigenomics data of many human cancer types. However, the retrieval of expression and methylation data from TCGA is a cumbersome and time-consuming task.ResultsWanderer is an intuitive Web tool allowing real time access and visualization of gene expression and DNA methylation profiles from TCGA. Given a gene query and selection of a TCGA dataset (e.g., colon adenocarcinomas), the Web resource provides the expression profile, at the single exon level, and the DNA methylation levels of HumanMethylation450 BeadChip loci inside or in the vicinity of the queried gene. Graphic and table outputs include individual and summary data as well as statistical tests, allowing the comparison of tumor and normal profiles and the exploration along the genomic locus and across tumor collections.ConclusionsWanderer offers a simple interface to straightforward access to TCGA data, amenable to experimentalists and clinicians without bioinformatics expertise. Wanderer may be accessed at http://maplab.cat/wanderer.

Genomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 281 B-cell precursor ALL (BCP-ALL) cases with previously undefined molecular phenotype, resulting in a single revised subtype for 81.5% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides a comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries.

Effects of DNA Methylation on Progression to Interstitial Fibrosis and Tubular Atrophy in Renal Allograft Biopsies: A Multi-Omics Approach.

Background:Approximately one-third of breast cancer (BC) patients show poorer cognitive function (CF). Using DNA methylation (DNAm) data here we aimed to identify genes and biological pathways associated with CF in postmenopausal women with early-stage hormone receptor-positive (HR+) BC.Methods:Epigenome-wide association studies (EWAS) and differentially methylated region analyses were performed for each CF phenotype (seven objective domains and one subjective phenotype) using DNAm data from whole blood samples (n=109) taken at the time of enrollment.Results:When adjusting for age, verbal IQ scores, and global DNAm signature, cg10331779 near CTNND2 (p-value=9.65 × 10−9) and cg25906741 in MLIP (p-value=2.01 × 10−8) were associated with processing speed and subjective CF, respectively, while regions in/near SLC6A11, PRKG1/CSTF2T, and FAM3B for processing speed, and regions in/near PI4KB and SGCE/PEG10 for mental flexibility were differentially methylated. In addition, beta-estradiol was identified as a common upstream regulator for all the CF phenotypes, suggesting an essential role of estrogen in explaining variation in CF of HR+ BC patients.Conclusions:In our EWAS of 8 CF phenotypes, we found two epigenome-wide significant signals, one for processing speed and the other for subjective CF. We also found three differentially methylated regions associated with processing speed and two associated with mental flexibility.Clinical trial registration:www.clinicaltrials.gov identifier is NCT02793921.

Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms.

Prediction of circRNAs Based on the DNA Methylation-Mediated Feature Sponge Function in Breast Cancer

Genetic Control of Individual Differences in Gene-Specific Methylation in Human Brain

Tumor-associated macrophages (TAM) initiate and maintain a chronic inflammation in malignant tumors, which promotes tumor growth and metastasis. It becomes increasingly important to monitor the quantity and distribution of TAM in human tumors as new TAM-targeted cancer immunotherapies are developed. To solve this unmet clinical need, Aghighi and colleagues developed an MR imaging test for detection of TAM in sarcomas and lymphomas using the iron supplement ferumoxytol “off label” as an MRI-detectable TAM biomarker. In a first-in-human proof-of-concept clinical trial, the authors show that tumor ferumoxytol enhancement on MR images correlates with TAM quantities on histopathology.Bezrookove and colleagues examine the role of copy number elevations in the PHIP gene in melanoma progression using fluorescence in situ hybridization in three independent cohorts. The authors demonstrate an independent prognostic role for PHIP in primary melanoma, identify molecular subtypes of melanoma in which PHIP is enriched, and show that PHIP copy number is monotonically increased in the transition from primary to nodal to distant metastatic melanoma. Taken together with prior work showing that PHIP promotes melanoma metastasis in murine models, these results demonstrate a critical role for PHIP in melanoma progression, identifying PHIP as a promising target for therapy.To date, the epigenomic underpinnings of pituitary adenomas remains largely unexplored. Here, Salomon and colleagues leverage a multiomics study design to investigate the role of epigenetic alterations among PA subtypes. Genome-wide methylation analysis reveals that each pituitary adenoma subtype displays a disease-specific DNA methylation pattern. Furthermore, integrating DNA methylation and gene expression data revealed that hypomethylation of promoter regions drive increased expression of GH1 and SSTR5 genes in GH-secreting adenomas, and POMC gene in ACTH-secreting adenomas. The study suggests that epigenetic differences among pituitary adenoma subtypes play a crucial role and could be important in diagnosis and treatment decisions.Oncolytic viruses are demonstrating increasing promise as anticancer agents, with adenovirus (Ad) -based vectors proving popular. Efficacy could be enhanced through rational refinement of the capsid to maximize sequestration in cancerous cells. To achieve this, Uusi-Kerttula and colleagues redesigned the capsid to prevent uptake via all known native infectious routes and inserted a targeting peptide highly specific to the tumor-selective αvβ6 integrin. The resultant vector, Ad5NULL-A20, demonstrated exquisitely tumor-selective infection, minimal uptake in off-target organs, and significant efficacy in a model of ovarian cancer, thus providing a promising platform for future translational immuno-oncolytic applications.