An analysis about heterogeneity among cancers based on the DNA methylation patterns

Detection of diagnostic and prognostic methylation-based signatures in liquid biopsy specimens from patients with meningiomas

Recurrence of meningiomas is unpredictable by current invasive methods based on surgically removed specimens. Identification of patients likely to recur using noninvasive approaches could inform treatment strategy, whether intervention or monitoring. In this study, we analyze the DNA methylation levels in blood (serum and plasma) and tissue samples from 155 meningioma patients, compared to other central nervous system tumor and non-tumor entities. We discover DNA methylation markers unique to meningiomas and use artificial intelligence to create accurate and universal models for identifying and predicting meningioma recurrence, using either blood or tissue samples. Here we show that liquid biopsy is a potential noninvasive and reliable tool for diagnosing and predicting outcomes in meningioma patients. This approach can improve personalized management strategies for these patients.

A road map for happiness: The psychological factors related cell types in various parts of human body from single cell RNA-seq data analysis

Massive evidence from all sources including zoology, neurobiology and immunology has confirmed that psychological factors can raise remarkable physiological effects. Researchers have long been aware of the potential value of these effects and wanted to harness them in the development of new drugs and therapies, for which the mechanism study is a necessary prerequisite. However, most of these studies are restricted to neuroscience, or starts with blood sample and fall into the area of immunity. In this study, we choose to focus on the psychological factor of happiness, mining existing publicly available single cell RNA sequencing (scRNA-seq) data for the expression of happiness-related genes collected from various sources of literature in all types of cells in the samples, finding that the expression of these genes is not restricted within neuro-regulated cells or tissue-resident immune cells, on the opposite, cell types that are unique to tissue and organ without direct regulation from nervous system account for the majority to express the happiness-related genes. Our research is a preliminary exploration of where our body respond to our mind at cell level, and lays the foundation for more detailed mechanism research.

Identification of Prognostic Biomarkers for Bladder Cancer Based on DNA Methylation Profile

Background: DNA methylation is an important epigenetic modification, which plays an important role in regulating gene expression at the transcriptional level. In tumor research, it has been found that the change of DNA methylation leads to the abnormality of gene structure and function, which can provide early warning for tumorigenesis. Our study aims to explore the relationship between the occurrence and development of tumor and the level of DNA methylation. Moreover, this study will provide a set of prognostic biomarkers, which can more accurately predict the survival and health of patients after treatment.

Methods: Datasets of bladder cancer patients and control samples were collected from TCGA database, differential analysis was employed to obtain genes with differential DNA methylation levels between tumor samples and normal samples. Then the protein-protein interaction network was constructed, and the potential tumor markers were further obtained by extracting Hub genes from subnet. Cox proportional hazard regression model and survival analysis were used to construct the prognostic model and screen out the prognostic markers of bladder cancer, so as to provide reference for tumor prognosis monitoring and improvement of treatment plan.

Results: In this study, we found that DNA methylation was indeed related with the occurrence of bladder cancer. Genes with differential DNA methylation could serve as potential biomarkers for bladder cancer. Through univariate and multivariate Cox proportional hazard regression analysis, we concluded that FASLG and PRKCZ can be used as prognostic biomarkers for bladder cancer. Patients can be classified into high or low risk group by using this two-gene prognostic model. By detecting the methylation status of these genes, we can evaluate the survival of patients.

Conclusion: The analysis in our study indicates that the methylation status of tumor-related genes can be used as prognostic biomarkers of bladder cancer.

C-Reactive Protein and C-Reactive Protein-Based Scores to Predict Survival in Esophageal and Junctional Adenocarcinoma: Systematic Review and Meta-Analysis

BACKGROUND

Esophageal adenocarcinoma (EAC) has a poor prognosis; predictive markers of prognosis would facilitate advances in personalized therapy. C-reactive protein (CRP) and CRP-based scores are increasingly recommended across oncology; however, their role and value in EAC is unclear. This systematic review and meta-analysis examined CRP cut-point and scores and how they may best be applied in predicting survival in EAC.

METHODS

A systematic literature search was conducted in EMBASE, Medline, Web of Science, Cochrane, Scopus and CINAHL databases, from inception to 1st October 2020. Studies reporting data from adults with EAC including adenocarcinoma of the gastro-esophageal junction (AEG), pre-treatment CRP or CRP-based score and Hazard Ratio (HR) for survival were included. QUIPS tool assessed risk of bias. Meta-analysis was undertaken.

RESULTS

A total of 819 records were screened. Eight papers were included, with data for 1475 people. CRP cut-points ranged from 2.8 to 10 mg/L. The Glasgow Prognostic Score (GPS) and modified GPS were the most commonly reported scores. On meta-analysis, elevated preoperative GPS/mGPS was significantly associated with worse overall survival (hazards ratio [HR] 1.81, 95% confidence interval [CI] 1.25-2.62, p = 0.002); results were similar in subgroup analyses of multimodal treatment, M0 disease, and R0 resection.

CONCLUSIONS

This is the first review to evaluate comprehensively the evidence for CRP and CRP-based scores in EAC. Meta-analysis demonstrated that elevated preoperative GPS or mGPS was significantly associated with reduced overall survival in EAC, including AEG. There is insufficient evidence to support use of CRP alone. Future studies should examine GPS/mGPS in EAC prospectively, alone and combined with other prognostic markers.

The regulatory pattern of target gene expression by aberrant enhancer methylation in glioblastoma

Background

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor with grim prognosis. Aberrant DNA methylation is an epigenetic mechanism that promotes GBM carcinogenesis, while the function of DNA methylation at enhancer regions in GBM remains poorly described.

Results

We integrated multi-omics data to identify differential methylation enhancer region (DMER)-genes and revealed global enhancer hypomethylation in GBM. In addition, a DMER-mediated target genes regulatory network and functional enrichment analysis of target genes that might be regulated by hypomethylation enhancer regions showed that aberrant enhancer regions could contribute to tumorigenesis and progression in GBM. Further, we identified 22 modules in which lncRNAs and mRNAs synergistically competed with each other. Finally, through the construction of drug-target association networks, our study identified potential small-molecule drugs for GBM treatment.

Conclusions

Our study provides novel insights for understanding the regulation of aberrant enhancer region methylation and developing methylation-based biomarkers for the diagnosis and treatment of GBM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04345-8.

Comparison of Histone H3K4me3 between IVF and ICSI Technologies and between Boy and Girl Offspring

Epigenetics play a vital role in early embryo development. Offspring conceived via assisted reproductive technologies (ARTs) have a three times higher risk of epigenetic diseases than naturally conceived children. However, investigations into ART-associated placental histone modifications or sex-stratified analyses of ART-associated histone modifications remain limited. In the current study, we carried out immunohistochemistry, chip-sequence analysis, and a series of in vitro experiments. Our results demonstrated that placentas from intra-cytoplasmic sperm injection (ICSI), but not in vitro fertilization (IVF), showed global tri-methylated-histone-H3-lysine-4 (H3K4me3) alteration compared to those from natural conception. However, for acetylated-histone-H3-lysine-9 (H3K9ac) and acetylated-histone-H3-lysine-27 (H3K27ac), no significant differences between groups could be found. Further, sex -stratified analysis found that, compared with the same-gender newborn cord blood mononuclear cell (CBMC) from natural conceptions, CBMC from ICSI-boys presented more genes with differentially enriched H3K4me3 (n = 198) than those from ICSI-girls (n = 79), IVF-girls (n = 5), and IVF-boys (n = 2). We also found that varying oxygen conditions, RNA polymerase II subunit A (Polr2A), and lysine demethylase 5A (KDM5A) regulated H3K4me3. These findings revealed a difference between IVF and ICSI and a difference between boys and girls in H3K4me3 modification, providing greater insight into ART-associated epigenetic alteration.

Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma

Background

A plethora of prognostic biomarkers for esophageal squamous cell carcinoma (ESCC) that have hitherto been reported are challenged with low reproducibility due to high molecular heterogeneity of ESCC. The purpose of this study was to identify the optimal biomarkers for ESCC using machine learning algorithms.

Methods

Biomarkers related to clinical survival, recurrence or therapeutic response of patients with ESCC were determined through literature database searching. Forty-eight biomarkers linked to recurrence or prognosis of ESCC were used to construct a molecular interaction network based on NetBox and then to identify the functional modules. Publicably available mRNA transcriptome data of ESCC downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets included GSE53625 and TCGA-ESCC. Five machine learning algorithms, including logical regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and XGBoost, were used to develop classifiers for prognostic classification for feature selection. The area under ROC curve (AUC) was used to evaluate the performance of the prognostic classifiers. The importances of identified molecules were ranked by their occurrence frequencies in the prognostic classifiers. Kaplan-Meier survival analysis and log-rank test were performed to determine the statistical significance of overall survival.

Results

A total of 48 clinically proven molecules associated with ESCC progression were used to construct a molecular interaction network with 3 functional modules comprising 17 component molecules. The 131,071 prognostic classifiers using these 17 molecules were built for each machine learning algorithm. Using the occurrence frequencies in the prognostic classifiers with AUCs greater than the mean value of all 131,071 AUCs to rank importances of these 17 molecules, stratifin encoded by SFN was identified as the optimal prognostic biomarker for ESCC, whose performance was further validated in another 2 independent cohorts.

Conclusion

The occurrence frequencies across various feature selection approaches reflect the degree of clinical importance and stratifin is an optimal prognostic biomarker for ESCC.

DNA methylation and breast cancer-associated variants

BACKGROUND

A breast cancer polygenic risk score (PRS) comprising 313 common variants reliably predicts disease risk. We examined possible relationships between genetic variation, regulation, and expression to clarify the molecular alterations associated with these variants.

METHODS

Genome-wide methylomic variation was quantified (MethylationEPIC) in Asian breast cancer patients (1152 buffy coats from peripheral whole blood). DNA methylation (DNAm) quantitative trait loci (mQTL) mapping was performed for 235 of the 313 variants with minor allele frequencies > 5%. Stability of identified mQTLs (p < 5e-8) across lifetime was examined using a public mQTL database. Identified mQTLs were also mapped to expression quantitative trait loci (eQTLs) in the Genotype-Tissue Expression Project and the eQTLGen Consortium.

RESULTS

Breast cancer PRS was not associated with DNAm. A higher proportion of significant cis-mQTLs were observed. Of 822 significant cis-mQTLs (179 unique variants) identified in our dataset, 141 (59 unique variants) were significant (p < 5e-8) in a public mQTL database. Eighty-six percent (121/141) of the matched mQTLs were consistent at multiple time points (birth, childhood, adolescence, pregnancy, middle age, post-diagnosis, or treatment). Ninety-three variants associated with DNAm were also cis-eQTLs (35 variants not genome-wide significant). Multiple loci in the breast cancer PRS are associated with DNAm, contributing to the polygenic nature of the disease. These mQTLs are mostly stable over time.

CONCLUSIONS

Consistent results from DNAm and expression data may reveal new candidate genes not previously associated with breast cancer.

Clinicopathological impacts of DNA methylation alterations on pancreatic ductal adenocarcinoma: prediction of early recurrence based on genome-wide DNA methylation profiling

PURPOSE

The present study was conducted to clarify the clinicopathological impacts of DNA methylation alterations on pancreatic ductal adenocarcinoma (PDAC).

METHODS

Genome-wide DNA methylation screening was performed using the Infinium HumanMethylation450 BeadChip, and DNA methylation quantification was verified using pyrosequencing. We analyzed fresh-frozen tissues from an initial cohort (17 samples of normal control pancreatic tissue [C] from 17 patients without PDAC, and 34 samples of non-cancerous pancreatic tissue [N] and 82 samples of cancerous tissue [T] both obtained from 82 PDAC patients) and formalin-fixed paraffin-embedded T samples from 34 patients in a validation cohort.

RESULTS

The DNA methylation profiles of N samples tended to differ from those of C samples, and 91,907 probes showed significant differences in DNA methylation levels between C and T samples. Epigenetic clustering of T samples was significantly correlated with a larger tumor diameter and early recurrence (ER), defined as relapse within 6 months after surgery. Three marker CpG sites, applicable to formalin-fixed paraffin-embedded surgically resected materials regardless of their tumor cell content, were identified for prediction of ER. The sensitivity and specificity for detection of patients belonging to the ER group using a panel combining these three marker CpG sites, including a CpG site in the CDK14 gene, were 81.8% and 71.7% and 88.9% and 70.4% in the initial and validation cohorts, respectively.

CONCLUSION

These findings indicate that DNA methylation alterations may have a clinicopathological impact on PDAC. Application of our criteria will ultimately allow prediction of ER after surgery to improve the outcome of PDAC patients.

Molecular subtypes based on DNA promoter methylation predict prognosis in lung adenocarcinoma patients

BACKGROUND

The heterogeneity of lung adenocarcinoma (LADC) makes the early diagnosis and treatment of the disease difficult. Gene silencing of DNA methylation is an important mechanism of tumorigenesis. A combination of methylation and clinical features can improve the classification of LADC heterogeneity.

RESULTS

We investigated the prognostic significance of 335 specimen subgroups of Lung adenocarcinoma based on the DNA methylation level. The differences in DNA methylation levels were related to the TNM stage classification, age, gender, and prognostic values. Seven subtypes were determined using 774 CpG sites that significantly affected the survival rate based on the consensus clustering. Finally, we constructed a prognostic model that performed well and further verified it in our test group.

CONCLUSIONS

This study shows that classification based on DNA methylation might aid in demonstrating heterogeneity within formerly characterized LADC molecular subtypes, assisting in the development of efficient, personalized therapy.

METHODS

Methylation data of lung adenocarcinoma were downloaded from the University of California Santa Cruz (UCSC) cancer browser, and the clinical patient information and RNA-seq archives were acquired from the Cancer Genome Atlas (TCGA). CpG sites were identified based on the significant correlation with the prognosis and used further to cluster the cases uniformly into several subtypes.