Individualized analysis reveals CpG sites with methylation aberrations in almost all lung adenocarcinoma tissues

Blood Leukocyte DNA Methylation Markers of Periodontal Disease and Risk of Lung Cancer in a Case-Control Study Nested in the CLUE II Cohort

BACKGROUND

Periodontal disease and DNA methylation markers have separately been associated with lung cancer risk. Examining methylation levels at genomic regions previously linked to periodontal disease may provide insights on the link between periodontal disease and lung cancer.

METHODS

In a nested case-control study drawn from the CLUE II cohort, we measured DNA methylation levels in 208 lung cancer cases and 208 controls. We examined the association between 37 DNA-methylated 5'-C-phosphate-G-3' (CpG) sites at three genomic regions, homeobox 4 (HOXA4), zinc finger protein (ZFP57), and a long noncoding RNA gene located in Chr10 (ENSG00000231601), and lung cancer risk.

RESULTS

Statistically significant associations with lung cancer risk were observed for all 14 CpG sites from HOXA4 (OR ranging 1.41-1.62 for 1 SD increase in the DNA methylation level, especially within 15 years) and 1 CpG site on gene ENSG00000231601 (OR = 1.34 for 1 SD increase in the DNA methylation level). Although CpG sites on gene ZFP57 were not associated with lung cancer risk overall, statistically significant inverse associations were noted for six CpG sites when restricting follow-up to 15 years (OR = 0.73-0.77 for 1 SD increase in the DNA methylation level).

CONCLUSIONS

Key methylation levels associated with periodontal disease are also associated with lung cancer risk. For both HOXA4 and ZFP57, the associations were stronger within 15 years of follow-up, which suggest that, if causal, the impact of methylation is acting late in the natural history of lung cancer.

IMPACT

Identifying biological pathways that link periodontal disease and lung cancer could provide new opportunities for lung cancer detection and prevention.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

BACKGROUND

Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals.

RESULTS

We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh's method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl .

CONCLUSIONS

The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Flipping the script: Advances in understanding how and why P4-ATPases flip lipid across membranes

Type IV P-type ATPases (P4-ATPases) are a family of transmembrane enzymes that translocate lipid substrates from the outer to the inner leaflet of biological membranes and thus create an asymmetrical distribution of lipids within membranes. On the cellular level, this asymmetry is essential for maintaining the integrity and functionality of biological membranes, creating platforms for signaling events and facilitating vesicular trafficking. On the organismal level, this asymmetry has been shown to be important in maintaining blood homeostasis, liver metabolism, neural development, and the immune response. Indeed, dysregulation of P4-ATPases has been linked to several diseases; including anemia, cholestasis, neurological disease, and several cancers. This review will discuss the evolutionary transition of P4-ATPases from cation pumps to lipid flippases, the new lipid substrates that have been discovered, the significant advances that have been achieved in recent years regarding the structural mechanisms underlying the recognition and flipping of specific lipids across biological membranes, and the consequences of P4-ATPase dysfunction on cellular and physiological functions. Additionally, we emphasize the requirement for additional research to comprehensively understand the involvement of flippases in cellular physiology and disease and to explore their potential as targets for therapeutics in treating a variety of illnesses. The discussion in this review will primarily focus on the budding yeast, C. elegans, and mammalian P4-ATPases.

Construction of a prognostic model for lung adenocarcinoma based on m6A/m5C/m1A genes

BACKGROUND

Developing a prognostic model for lung adenocarcinoma (LUAD) that utilizes m6A/m5C/m1A genes holds immense importance in providing precise prognosis predictions for individuals.

METHODS

This study mined m6A/m5C/m1A-related differential genes in LUAD based on public databases, identified LUAD tumor subtypes based on these genes, and further built a risk prognostic model grounded in differential genes between subtypes. The immune status between high- and low-risk groups was investigated, and the distribution of feature genes in tumor immune cells was analyzed using single-cell analysis. Based on the expression levels of feature genes, a projection of chemotherapeutic and targeted drugs was made for individuals identified as high-risk. Ultimately, cell experiments were further verified.

RESULTS

The 6-gene risk prognosis model based on differential genes between tumor subtypes had good predictive performance. Individuals classified as low-risk exhibited a higher (P < 0.05) abundance of infiltrating immune cells. Feature genes were mainly distributed in tumor immune cells like CD4+T cells, CD8+T cells, and regulatory T cells. Four drugs with relatively low IC50 values were found in the high-risk group: Elesclomol, Pyrimethamine, Saracatinib, and Temsirolimus. In addition, four drugs with significant positive correlation (P < 0.001) between IC50 values and feature gene expression were found, including Alectinib, Estramustine, Brigatinib, and Elesclomol. The low expression of key gene NTSR1 reduced the IC50 value of irinotecan.

CONCLUSION

Based on the m6A/m5C/m1A-related genes in LUAD, LUAD patients were divided into 2 subtypes, and a m6A/m5C/m1A-related LUAD prognostic model was constructed to provide a reference for the prognosis prediction of LUAD.

Functional impact of multi-omic interactions in lung cancer

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

Identification of gene-level methylation for disease prediction

DNA methylation is an epigenetic alteration that plays a fundamental part in governing gene regulatory processes. The DNA methylation mechanism affixes methyl groups to distinct cytosine residues, influencing chromatin architectures. Multiple studies have demonstrated that DNA methylation's regulatory effect on genes is linked to the beginning and progression of several disorders. Researchers have recently uncovered thousands of phenotype-related methylation sites through the epigenome-wide association study (EWAS). However, combining the methylation levels of several sites within a gene and determining the gene-level DNA methylation remains challenging. In this study, we proposed the supervised UMAP Assisted Gene-level Methylation method (sUAGM) for disease prediction based on supervised UMAP (Uniform Manifold Approximation and Projection), a manifold learning-based method for reducing dimensionality. The methylation values at the gene level generated using the proposed method are evaluated by employing various feature selection and classification algorithms on three distinct DNA methylation datasets derived from blood samples. The performance has been assessed employing classification accuracy, F-1 score, Mathews Correlation Coefficient (MCC), Kappa, Classification Success Index (CSI) and Jaccard Index. The Support Vector Machine with the linear kernel (SVML) classifier with Recursive Feature Elimination (RFE) performs best across all three datasets. From comparative analysis, our method outperformed existing gene-level and site-level approaches by achieving 100% accuracy and F1-score with fewer genes. The functional analysis of the top 28 genes selected from the Parkinson's disease dataset revealed a significant association with the disease.

Multi-omics integration analysis unveils heterogeneity in breast cancer at the individual level

Identifying robust breast cancer subtypes will help to reveal the cancer heterogeneity. However, previous breast cancer subtypes were based on population-level quantitative gene expression, which is affected by batch effects and cannot be applied to individuals. We detected differential gene expression, genomic, and epigenomic alterations to identify driver differential expression at the individual level. The individual driver differential expression reflected the breast cancer patients' heterogeneity and revealed four subtypes. Mesenchymal subtype as the most aggressive subtype harbored deletion and downregulated expression of genes in chromosome 11q23 region. Specifically, silencing of the SDHD gene in 11q23 promoted the invasion and migration of breast cancer cells in vitro by the epithelial-mesenchymal transition. The immunologically hot subtype displayed an immune-hot microenvironment, including high T-cell infiltration and upregulated PD-1 and CTLA4. Luminal and genomic-unstable subtypes showed opposite macrophage polarization, which may be regulated by the ligand-receptor pairs of CD99. The integration of multi-omics data at the individual level provides a powerful framework for elucidating the heterogeneity of breast cancer.

Type IV P-Type ATPases: Recent Updates in Cancer Development, Progression, and Treatment

Adaptations of cancer cells for survival are remarkable. One of the most significant properties of cancer cells to prevent the immune system response and resist chemotherapy is the altered lipid metabolism and resulting irregular cell membrane composition. The phospholipid distribution in the plasma membrane of normal animal cells is distinctly asymmetric. Lipid flippases are a family of enzymes regulating membrane asymmetry, and the main class of flippases are type IV P-type ATPases (P4-ATPases). Alteration in the function of flippases results in changes to membrane organization. For some lipids, such as phosphatidylserine, the changes are so drastic that they are considered cancer biomarkers. This review will analyze and discuss recent publications highlighting the role that P4-ATPases play in the development and progression of various cancer types, as well as prospects of targeting P4-ATPases for anti-cancer treatment.

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods' performance and DM signatures' quality in the absence of gold standard data solving a long-existing problem in DM analysis.

CircSCN8A suppresses malignant progression and induces ferroptosis in non-small cell lung cancer by regulating miR-1290/ACSL4 axis

Circular RNAs (CircRNAs) are reported to exert vital regulatory roles in the occurrence and development of various human malignancies, including non-small cell lung cancer (NSCLC). Bioinformatics methods identified the down-regulation of circSCN8A (circBase ID: hsa_circ_0026337) in NSCLC tissues. However, its biological functions and molecular mechanisms in NSCLC remain unknown. In this study, we found that circSCN8A expression was down-regulated in NSCLC tissues and cells. Low circSCN8A expression was positively associated with aggressive clinicopathological characteristics and poor prognosis in NSCLC patients. CircSCN8A suppressed cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) in vitro and blocked tumor growth in vivo. Moreover, circSCN8A promoted cell ferroptosis in NSCLC. Mechanistically, circSCN8A acted as a competing endogenous RNA (ceRNA) by sponging miR-1290 to enhance the expression of long-chain acyl-CoA synthetase-4 (ACSL4). Furthermore, the knockdown of ACSL4 or overexpression of miR-1290 reversed the effect of circSCN8A on facilitating ferroptosis and inhibiting cell proliferation and metastasis. In summary, circSCN8A represses cell proliferation and metastasis in NSCLC by regulating the miR-1290/ACSL4 axis to induce ferroptosis. Thus, circSCN8A may represent a promising therapeutic target against NSCLC.

Cooperative In Situ Assembly of G-Quadruplex DNAzyme Nanowires for One-Step Sensing of CpG Methylation in Human Genomes

CpG methylation is one the most predominant epigenetic modification that has been recognized as a molecular-level biomarker for various human diseases. Taking advantage of methylation-dependent cleavage and encoding flexibility in nucleic acid functions and structures, we demonstrate the cooperative in situ assembly of G-quadruplex DNAzyme nanowires for one-step sensing of CpG methylation in human genomes. This nanodevice displays good specificity and high sensitivity with a limit of detection (LOD) of 0.565 aM in vitro and 1 cell in vivo. It can distinguish 0.001% CpG methylation level from excess unmethylated DNA, quantify different CpG methylation targets from diverse human cancer cells, and even discriminate CpG methylation expressions between lung tumor and precancerous tissues. Importantly, this nanodevice can be performed isothermally in one step within 2 h in a label-free manner without any bisulfite conversion, fluorescence tagging, and PCR amplification process, providing a new platform for genomic methylation-related clinical diagnosis and biomedical research.

The bioinformatics analysis of RIOX2 gene in lung adenocarcinoma and squamous cell carcinoma

Lung cancer is characterized by high morbidity and mortality rates, and it has become an important public health issue worldwide. The occurrence and development of tumors is a multi-gene and multi-stage complex process. As an oncogene, ribosomal oxygenase 2 (RIOX2) has been associated with a variety of cancers. In this article, we analyzed the correlation between RIOX2 expression and methylation in lung cancer based on the databases including the cancer genome atlas (TCGA) (https://portal.gdc.cancer.gov/) and the gene expression omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). It was found that RIOX2 is highly expressed in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissues, whose expression is negatively correlated with its methylation level. In this regard, methylation at cg09716038, cg14773523, cg14941179, and cg22299097 had a significant negative correlation with RIOX2 expression in LUAD, whereas in LUSC, methylation at cg09716038, cg14773523, cg14941179, cg22299097, cg05451573, cg10779801, and cg23629183 is negatively correlated with RIOX2 expression. According to the analysis based on the databases, RIOX2 gene could not be considered as the independent prognostic biomarker in lung adenocarcinoma or squamous cell lung cancer. However, the molecular mechanism of RIOX2 gene in the development of lung cancer may be helpful in improving lung cancer therapy.

SPARCL1 exhibits different expressions in left- and right-sided colon cancer and is downregulated via DNA methylation

Aim: The authors previously found that SPARCL1 functions to suppress colorectal cancer metastasis. Here, the epigenetic mechanism of SPARCL1 regulation and its relationship with clinicopathological features in colon cancer were investigated. Materials & methods: SPARCL1 expression was evaluated by immunohistochemistry staining in a tissue array containing 271 left-sided colon cancer samples and 257 right-sided colon cancer samples. In vivo and in vitro DNA methylation states were measured by biochemical sulfide potential assay. The transcription and DNA methylation states in cells were altered by siRNA or decitabine treatment, respectively. Cellular motility properties were compared through transwell assay. Results & conclusion: SPARCL1, mediated by its DNA methylation, may arrest colorectal carcinoma motility. Furthermore, SPARCL1 expression is higher and may have a specific prognostic value in left-sided colon cancer.

Construction of a novel methylation-related prognostic model for colorectal cancer based on microsatellite status

The present study aimed to construct a novel methylation-related prognostic model based on microsatellite status that may enhance the prognosis of colorectal cancer (CRC) from methylation and microsatellite status perspective. DNA methylation and mRNA expression data with clinical information were downloaded from The Cancer Genome Atlas (TCGA) data set. The samples were divided into microsatellite stability and microsatellite instability group, and CIBERSORT was used to assess the immune cell infiltration characteristics. After identifying the differentially methylated genes and differentially expression genes using R packages, the methylation-driven genes were further identified. Prognostic genes that were used to establish the methylation-related risk score model were generated by the univariate and multivariate Cox regression model. Finally, we established and evaluated the methylation-related prognostic model for CRC patients. A total of 69 MDGs were obtained and three of these genes (MIOX, TH, DKFZP434K028) were selected to construct the prognostic model. Patients in the low-risk score group had a conspicuously better overall survival than those in the high-risk score group (p < .0001). The area under the receiver operating characteristic curve for this model was 0.689 at 3 years, 0.674 at 4 years, and 0.658 at 5 years. The Wilcoxon test showed that higher risk score was associated with higher T stage (p = .01), N stages (p = .0028), metastasis (p = .013), and advanced pathological stage (p = .0013). However, the more instability of microsatellite status, the lower risk score of CRC patients (p = .0048). Our constructed methylation-related prognostic model based on microsatellite status presents potential significance in assessing recurrence risk stratification, tumor staging, and immunotherapy for CRC patients.

Hypomethylation-Mediated AGR2 Overexpression Facilitates Cell Proliferation, Migration, and Invasion of Lung Adenocarcinoma

Objective

Studies have indicated that AGR2 is crucial in many cancers. However, its methylation level in lung adenocarcinoma (LUAD) is rarely known. Hence, the effect of AGR2 methylation on LUAD was explored in the study.

Methods

qRT-PCR was adopted to detect the expression of AGR2 in LUAD cells and normal lung cells. Methylation-specific PCR (MSP) was used to detect the methylation of AGR2 promoter region in different cell lines. MTT, Transwell and wound healing assays were used to verify the progression of cells in each transfection group.

Results

The expression of AGR2 was significantly up-regulated in LUAD cells relative to that in normal cells. Moreover, the expression of AGR2 was inversely modulated by DNA methylation, and the hypomethylation of CpG islands would lead to the increased expression of AGR2. Finally, overexpression and hypomethylation of AGR2 facilitated the proliferation, invasion and migration of LUAD cells.

Conclusion

These results demonstrated that hypomethylation of AGR2 promoter region promoted the expression of AGR2 in LUAD cells, thus promoting the progression of LUAD cells.

Aberrant Methylation and Differential Expression of SLC2A1, TNS4, GAPDH, ATP8A2, and CASZ1 Are Associated with the Prognosis of Lung Adenocarcinoma

Lung cancer is one of the leading triggers for cancer death worldwide. In this study, the relationship of the aberrantly methylated and differentially expressed genes in lung adenocarcinoma (LUAD) with cancer prognosis was investigated, and 5 feature genes were identified eventually. Specifically, we firstly downloaded the LUAD-related mRNA expression profile (including 57 normal tissue samples and 464 LUAD tissue samples) and Methy450 expression data (including 32 normal tissue samples and 373 LUAD tissue samples) from the TCGA database. The package "limma" was used to screen differentially expressed genes and aberrantly methylated genes, which were intersected for identifying the hypermethylated downregulated genes (DGs Hyper) and the hypomethylated upregulated genes (UGs Hypo). GO annotation and KEGG pathway enrichment analysis were further performed, and it was found that these DGs Hyper and UGs Hypo were predominantly activated in the biological processes and signaling pathways such as the regulation of vasculature development, DNA-binding transcription activator activity, and Ras signaling pathway, indicating that these genes play a vital role in the initiation and progression of LUAD. Additionally, univariate and multivariate Cox regression analyses were conducted to find the genes significantly associated with LUAD prognosis. Five genes including SLC2A1, TNS4, GAPDH, ATP8A2, and CASZ1 were identified, with the former three highly expressed and the latter two poorly expressed in LUAD, indicating poor prognosis of LUAD patients as judged by survival analysis.

The Effects of Age, Cigarette Smoking, Sex, and Race on the Qualitative Characteristics of Lung Transcriptome

The within-sample relative expression orderings (REOs) of genes, which are stable qualitative transcriptional characteristics, can provide abundant information for a disease. Methods based on REO comparisons have been proposed for identifying differentially expressed genes (DEGs) at the individual level and for detecting disease-associated genes based on one-phenotype disease data by reusing data of normal samples from other sources. Here, we evaluated the effects of common potential confounding factors, including age, cigarette smoking, sex, and race, on the REOs of gene pairs within normal lung tissues transcriptome. Our results showed that age has little effect on REOs within lung tissues. We found that about 0.23% of the significantly stable REOs of gene pairs in nonsmokers' lung tissues are reversed in smokers' lung tissues, introduced by 344 DEGs between the two groups of samples (RankCompV2, FDR <0.05), which are enriched in metabolism of xenobiotics by cytochrome P450, glutathione metabolism, and other pathways (hypergeometric test, FDR <0.05). Comparison between the normal lung tissue samples of males and females revealed fewer reversal REOs introduced by 24 DEGs between the sex groups, among which 19 DEGs are located on sex chromosomes and 5 DEGs involving in spermatogenesis and regulation of oocyte are located on autosomes. Between the normal lung tissue samples of white and black people, we identified 22 DEGs (RankCompV2, FDR <0.05) which introduced a few reversal REOs between the two races. In summary, the REO-based study should take into account the confounding factors of cigarette smoking, sex, and race.

Identification and validation of novel DNA methylation markers for early diagnosis of lung adenocarcinoma

Lung cancer has the highest mortality of all cancers worldwide. Epigenetic alterations have emerged as potential biomarkers for early diagnosis of various cancer tissue types. To identify methylation markers for early diagnosis of lung adenocarcinoma, we aimed to integrate genome‐wide DNA methylation and gene expression data from The Cancer Genome Atlas. To this end, we first examined the global DNA methylation pattern of lung adenocarcinoma and investigated the relationship between DNA methylation subtypes and clinical features. We then extracted differentially methylated and expressed genes, and adopted feature selection techniques to determine the final methylation markers. The performance of the markers in predicting lung adenocarcinoma was evaluated on three independent datasets from Gene Expression Omnibus. Protein levels of marker genes were validated by immunohistochemistry, and their biological function was further verified in vivo. We identified three novel methylation markers in lung adenocarcinoma including cg08032924, cg14823851, and cg19161124, mapping to CMTM2, TBX4, and DPP6, respectively. Validating these results on three independent datasets indicated that the three markers can achieve extremely high sensitivity and specificity in distinguishing lung adenocarcinoma from normal samples. Immunohistochemistry quantification results confirmed that markers are weakly expressed in human lung adenocarcinoma, and CMTM2 decreased tumor growth of mouse Lewis lung carcinoma in vivo. Overall, our study identified three novel methylation markers in lung adenocarcinoma which may contribute toward an improved diagnosis potentially leading to a better outcome for patients with lung adenocarcinoma.

Absence of an embryonic stem cell DNA methylation signature in human cancer

BACKGROUND

Differentiated cells that arise from stem cells in early development contain DNA methylation features that provide a memory trace of their fetal cell origin (FCO). The FCO signature was developed to estimate the proportion of cells in a mixture of cell types that are of fetal origin and are reminiscent of embryonic stem cell lineage. Here we implemented the FCO signature estimation method to compare the fraction of cells with the FCO signature in tumor tissues and their corresponding nontumor normal tissues.

METHODS

We applied our FCO algorithm to discovery data sets obtained from The Cancer Genome Atlas (TCGA) and replication data sets obtained from the Gene Expression Omnibus (GEO) data repository. Wilcoxon rank sum tests, linear regression models with adjustments for potential confounders and non-parametric randomization-based tests were used to test the association of FCO proportion between tumor tissues and nontumor normal tissues. P-values of < 0.05 were considered statistically significant.

RESULTS

Across 20 different tumor types we observed a consistently lower FCO signature in tumor tissues compared with nontumor normal tissues, with 18 observed to have significantly lower FCO fractions in tumor tissue (total n = 6,795 tumor, n = 922 nontumor, P < 0.05). We replicated our findings in 15 tumor types using data from independent subjects in 15 publicly available data sets (total n = 740 tumor, n = 424 nontumor, P < 0.05).

CONCLUSIONS

The results suggest that cancer development itself is substantially devoid of recapitulation of normal embryologic processes. Our results emphasize the distinction between DNA methylation in normal tightly regulated stem cell driven differentiation and cancer stem cell reprogramming that involves altered methylation in the service of great cell heterogeneity and plasticity.

Identification of potential key genes associated with osteosarcoma based on integrated bioinformatics analyses

Due to high rates of metastasis and poor clinical outcomes for patients, it is important to study the pathomechanisms of osteosarcoma. However, due to the fact that osteosarcoma shows significant interindividual variation and high heterogeneity, the identification of differentially expressed genes (DEGs) at the population level cannot answer many important questions related to osteosarcoma tumorigenesis. Therefore, a new strategy to identify dysregulated genes in osteosarcoma samples is required. The aim of this study was to improve our understanding of osteosarcoma pathogenesis by identifying genes with universal aberrant expression in osteosarcoma samples. Because the relative expression ordering of genes is stable in normal bone tissues but is disrupted in osteosarcoma tissues, we used the RankComp algorithm to identify DEGs in normal and osteosarcoma tissue samples. We then calculated the dysregulation frequency for each gene. Genes with deregulation frequencies above 80% were deemed to be universal DEGs. Next, coexpression, pathway enrichment, and protein-protein interaction network analyses were performed to characterize the functions of these genes. From 188 samples of osteosarcoma obtained from four datasets measured on different platforms, 51 universal DEGs were identified, including 4 universally upregulated genes and 47 universally downregulated genes. Genes that were differentially coexpressed with these universal DEGs were found to be enriched in 46 cancer-related pathways. In addition, functional and network analyses showed that genes with high dysregulation frequencies were involved in cancer-related functions. Thus, the commonly aberrant genes identified in osteosarcoma tissues may be important targets for osteosarcoma diagnosis and therapy.

A Pilot Study Using a Multistaged Integrated Analysis of Gene Expression and Methylation to Evaluate Mechanisms for Evening Fatigue in Women Who Received Chemotherapy for Breast Cancer

CONTEXT

Fatigue is the most common symptom associated with cancer and its treatment. Investigation of molecular mechanisms associated with fatigue may identify new therapeutic targets.

OBJECTIVE

The objective of this pilot study was to evaluate the relationships between gene expression and methylation status and evening fatigue severity in women with breast cancer who received chemotherapy.

METHODS

Latent class analysis (LCA) was used to identify evening fatigue phenotypes. In this analysis, the lowest (i.e., moderate, n = 7) and highest (i.e., very high, n = 29) fatigue-severity classes identified using LCA were analyzed via two stages. First, a total of 32,609 transcripts from whole blood were evaluated for differences in expression levels between the classes. Next, 637 methylation sites located within the putative transcription factor binding sites for those genes demonstrating differential expression were evaluated for differential methylation state between the classes.

RESULTS

A total of 89 transcripts in 75 unique genes were differentially expressed between the moderate (the lowest fatigue-severity class identified) and very high evening fatigue classes. In addition, 23 differentially methylated probes and three differentially methylated regions were found between the moderate and very high evening fatigue classes.

CONCLUSIONS

Using a multistaged integrated analysis of gene expression and methylation, differential methylation was identified in the regulatory regions of genes associated with previously hypothesized mechanisms for fatigue, including inflammation, immune function, neurotransmission, circadian rhythm, skeletal muscle energy, carbohydrate metabolism, and renal function as well as core biological processes including gene transcription and the cell-cycle regulation.

Genome-wide DNA-methylation profiles in human bone marrow mesenchymal stem cells on titanium surfaces

The characteristics of titanium (Ti) have been shown to influence dental implant fixation. Treatment of surfaces using the sandblasted, large-grit, acid-etched (SLA) method is widely used to provide effective osseointegration. However, the DNA methylation-associated mechanism by which SLA surface treatment affects osseointegration of human bone marrow mesenchymal stem cells (hBMSCs) remains elusive. Genome-wide methylation profiling of hBMSCs on SLA-treated and machined smooth Ti was performed using Illumina Infinium Methylation EPIC BeadChip at day 7 of osteogenic induction. In total, 2,846 CpG sites were differentially methylated in the SLA group compared with the machined group. Of these sites, 1,651 (covering 1,066 genes) were significantly hypermethylated and 1,195 (covering 775 genes) were significantly hypomethylated. Thirty significant enrichment pathways were observed, with Wnt signaling being the most significant. mRNA expression was identified by microarray and combined with DNA-methylation profiles. Thirty-seven genes displayed negative association between mRNA expression and DNA-methylation level, with the osteogenesis-related genes insulin-like growth factor 2 (IGF2) and carboxypeptidase X, M14 Family Member 2 (CPXM2) showing significant up-regulation and down-regulation, respectively. In summary, our results demonstrate differences between SLA-treated and machined surfaces in their effects on genome-wide DNA methylation and enrichment of osteogenic pathways in hBMSCs. We provide novel insights into genes and pathways affected by SLA treatment in hBMSCs at the molecular level.

Identification of CAV1 and DCN as potential predictive biomarkers for lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common histological form of lung cancer that is clinically diagnosed. The aim of this study is to explore the novel genes associated with LUAD tumorigenesis. Comprehensive bioinformatics analyses of the data were obtained from several publicly available databases, such as the Gene Expression Omnibus, the Human Protein Atlas project, and the Cancer Cell Line Encyclopedia. The clinical relevance of these novel genes in LUAD was further examined by immunohistochemistry. We identified the overlapping differentially expressed genes (DEGs) in five independent microarray data sets from the Gene Expression Omnibus database ( GSE75037 , GSE85716 , GSE85841 , GSE63459 , and GSE32867 ). Using the criteria of |log (fold change)| ≥ 1 and P value <0.05, 167 genes were preliminarily validated as co-DEGs. Protein-protein interaction network analysis indicated that caveolin 1 (CAV1) and decorin (DCN) levels were significantly reduced and that these genes were the most promising predictive biomarkers for the occurrence and prognosis of LUAD. A cell proliferation assay indicated that overexpressed CAV1 and DCN could significantly inhibit the proliferation rate of A549 and H157 cells. Additionally, these two downregulated candidate genes were further verified by immunohistochemistry conducted on a LUAD tissue array and comprehensive bioinformatics analyses, including those using the Oncomine platform and the Cancer Cell Line Encyclopedia. Our study demonstrates low levels of CAV1 and DCN in LUAD. An understanding of their functional roles in LUAD biology would give us important insights that would be useful in further investigations.

Multiple correlation analyses revealed complex relationship between DNA methylation and mRNA expression in human peripheral blood mononuclear cells

DNA methylation is an important regulator on the mRNA expression. However, a genome-wide correlation pattern between DNA methylation and mRNA expression in human peripheral blood mononuclear cells (PBMCs) is largely unknown. The comprehensive relationship between mRNA and DNA methylation was explored by using four types of correlation analyses and a genome-wide methylation-mRNA expression quantitative trait locus (eQTL) analysis in PBMCs in 46 unrelated female subjects. An enrichment analysis was performed to detect biological function for the detected genes. Single pair correlation coefficient (r _T1) between methylation level and mRNA is moderate (-0.63-0.62) in intensity, and the negative and positive correlations are nearly equal in quantity. Correlation analysis on each gene (T4) found 60.1% genes showed correlations between mRNA and gene-based methylation at P < 0.05 and more than 5.96% genes presented very strong correlation (R _T4 > 0.8). Methylation sites have regulation effects on mRNA expression in eQTL analysis, with more often observations in region of transcription start site (TSS). The genes under significant methylation regulation both in correlation analysis and eQTL analysis tend to cluster to the categories (e.g., transcription, translation, regulation of transcription) that are essential for maintaining the basic life activities of cells. Our findings indicated that DNA methylation has predictive regulation effect on mRNA with a very complex pattern in PBMCs. The results increased our understanding on correlation of methylation and mRNA and also provided useful clues for future epigenetic studies in exploring biological and disease-related regulatory mechanisms in PBMC.

DNA methylation in lung tissues of mouse offspring exposed in utero to polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbons (PAHs) comprise an important class of environmental pollutants that are known to cause lung cancer in animals and are suspected lung carcinogens in humans. Moreover, evidence from cell-based studies points to PAHs as modulators of the epigenome. The objective of this work was to assess patterns of genome-wide DNA methylation in lung tissues of adult offspring initiated in utero with the transplacental PAH carcinogens dibenzo [def,p]chrysene (DBC) or benzo [a]pyrene (BaP). Genome-wide methylation patterns for normal (not exposed), normal adjacent and lung tumor tissues obtained from adult offspring were determined using methylated DNA immunoprecipitation (MeDIP) with the NimbleGen mouse DNA methylation CpG island array. Lung tumor incidence in 45-week old mice initiated with BaP was 32%, much lower than that of the DBC-exposed offspring at 96%. Also, male offspring appeared more susceptible to BaP as compared to females. Distinct patterns of DNA methylation were associated with non-exposed, normal adjacent and adenocarcinoma lung tissues, as determined by principal components, hierarchical clustering and gene ontology analyses. From these methylation profiles, a set of genes of interest was identified that includes potential important targets for epigenetic modification during the process of lung tumorigenesis in animals exposed to environmental PAHs.