DNA methylation data-based molecular subtype classification related to the prognosis of patients with cervical cancer

Regeneration and anti-inflammatory effects of stem cells and their extracellular vesicles in gynecological diseases

There are many gynecological diseases, among which breast cancer (BC), cervical cancer (CC), endometriosis (EMs), and polycystic ovary syndrome (PCOS) are common and difficult to cure. Stem cells (SCs) are a focus of regenerative medicine. They are commonly used to treat organ damage and difficult diseases because of their potential for self-renewal and multidirectional differentiation. SCs are also commonly used for difficult-to-treat gynecological diseases because of their strong directional differentiation ability with unlimited possibilities, their tendency to adhere to the diseased tissue site, and their use as carriers for drug delivery. SCs can produce exosomes in a paracrine manner. Exosomes can be produced in large quantities and have the advantage of easy storage. Their safety and efficacy are superior to those of SCs, which have considerable potential in gynecological treatment, such as inhibiting endometrial senescence, promoting vascular reconstruction, and improving anti-inflammatory and immune functions. In this paper, we review the mechanisms of the regenerative and anti-inflammatory capacity of SCs and exosomes in incurable gynecological diseases and the current progress in their application in genetic engineering to provide a foundation for further research.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies

BACKGROUND

DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.

METHODS

We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively.

RESULTS

Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.

CONCLUSIONS

There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

Methylation biomarkers for early cancer detection and diagnosis: Current and future perspectives

The increase in recent scientific studies on cancer biomarkers has brought great new insights into the field. Moreover, novel technological breakthroughs such as long read sequencing and microarrays have enabled high throughput profiling of many biomarkers, while advances in bioinformatic tools have made the possibility of developing highly reliable and accurate biomarkers a reality. These changes triggered renewed interest in biomarker research and provided tremendous opportunities for enhancing cancer management and improving early disease detection. DNA methylation alterations are known to accompany and contribute to carcinogenesis, making them promising biomarkers for cancer, namely due to their stability, frequency and accessibility in bodily fluids. The advent of newer minimally invasive experimental methods such as liquid biopsies provide the perfect setting for methylation-based biomarker development and application. Despite their huge potential, accurate and robust biomarkers for the conclusive diagnosis of most cancer types are still not routinely used, hence a strong need for sustained research in this field is still needed. This review provides a brief exposition of current methylation biomarkers for cancer diagnosis and early detection, including markers already in clinical use as well as various upcoming ones. It also outlines how recent big data and novel technologies will revolutionise the next generation of cancer tests in supplementing or replacing currently existing invasive techniques.

Identification of Specific Cervical Cancer Subtypes and Prognostic Gene Sets in Tumor and Nontumor Tissues Based on GSVA Analysis

Background

Cervical cancer is the fourth common cancer among women. Its prognosis needs our more attention. Our purpose was to identity new prognostic gene sets to help other researchers develop more effective treatment for cervical cancer patients and improve the prognosis of patients.

Methods

We used gene set variation analysis (GSVA) to calculate the enrichment scores of gene sets and identified three subtypes of cervical cancer through the Cox regression model, k-means clustering algorithm, and nonnegative matrix factorization method (NMF). Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes. We further screened the prognostic gene sets using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to analyze which pathways and function the genes from screened gene sets enriched. Search Tool for the Retrieval of Interacting Genes (STRING) was used to draw the protein-protein interaction network, and Cytoscape was used to visualize the hub genes of protein-protein interaction network.

Results

We identified three novel subtypes of cervical cancer in The Cancer Genome Atlas (TCGA) samples and validated in Gene Expression Omnibus (GEO) samples. There were significant variations between the three subtypes in histological type, T stage, M stage, and N stage. T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N_HALLMARK_ANGIOGENESIS were screened prognostic gene sets. The prognostic model was as follows: riskScore = T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP^∗ 2.617 + N_HALLMARK_ANGIOGENESIS^∗ 4.860. Survival analysis presented that in these two gene sets, high enrichment scores were all significantly related to worse overall survival. The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. As for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH.

Conclusions

Three novel subtypes and two prognostic gene sets were identified. 15 hub genes for T gene set and 5 hub genes for N gene set were discovered. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene enriched pathways, we can development specific drugs targeting the pathways.

Tumor DNA Methylation Profiles Enable Diagnosis, Prognosis Prediction, and Screening for Cervical Cancer

Background

DNA-methylation-based machine learning algorithms have demonstrated powerful diagnostic capabilities, and these tools are currently emerging in many fields of tumor diagnosis and patient prognosis prediction. This work aimed to identify novel DNA methylation diagnostic biomarkers for differentiating cervical cancer (CC) from normal tissues, as well as a prognostic prediction model to predict survival of CC patients.

Methods

The methylation profiles with the available clinical characteristics were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) program. We first screened out the differential methylation sites in CC and normal tissues and performed multiple statistical analyses to discover DNA methylation diagnostic markers that are used to distinguish CC and normal control. Then, we developed a methylation-based survival model to improve risk stratification.

Results

A diagnostic prediction panel consists of five CpG markers that could predict cervical cancer versus normal tissue with highly correct rate of 100%, and cg16428251, cg22341310, and cg23316360 which in diagnostic prediction panel all could yield high sensitivity and specificity for detection of CC and normal in six cohorts (area under curve [AUC] > 0.8), in addition to excellent performance in discriminating between CC and normal sample. The diagnostic marker panel also effectively predicted the CIN3 versus normal tissue with high accuracy in two datasets (AUC = 0.80, 0.789, respectively). Furthermore, a prognostic prediction model aggregated two CpG markers that effectively stratified the prognosis of high-risk and low-risk groups (training cohort: hazard ratio [HR] 4, 95% CI: 1.7–9.6, P = 0.0021; testing cohort: hazard ratio [HR] 1.9, 95% CI: 1.2–3.1, P = 0.0072).

Conclusion

The findings of our study showed that DNA methylation markers are of great value in the diagnosis and prognosis of CC.

PMEPA1 Serves as a Prognostic Biomarker and Correlates with Immune Infiltrates in Cervical Cancer

Emerging studies have demonstrated that Prostate transmembrane protein androgen induced 1 (PMEPA1) plays crucial roles in the carcinogenesis of many developing human tumors. However, the clinical significance of PMEPA1 expression in cervical cancer (CC) and its contribution to cancer immunity have not been investigated. In this study, we identified PMEPA1 as a survival-related gene in CC based on TCGA datasets. Univariate and multivariate analysis showed that PMEPA1 expression was an independent predictor for overall survival in CC patients. We could observe a strong negative correlation between PMEPA1 expression and PMEPA1 methylation. Two CpG sites of PMEPA1 were associated with overall survival, and one CpG site of PMEPA1 was associated with progression-free survival. The low level of PMEPA1 methylation was associated with advanced clinical stage of CC patients. KEGG assays revealed the genes associated with PMEPA1 expression were mainly enriched in several tumor-related pathways. Increased PMEPA1 expressions were observed to be positively related to high immune infiltration levels in several immune cells. Finally, the pan-cancer assays revealed that PMEPA1 expression was associated with the overall survival of UVM, PAAD, LUSC, BLCA, CESC, and LUAD. Taken together, PMEPA1 is a prognosis-related biomarker for multiple cancer types, especially CC. PMEPA1 is involved in tumor immunity, suggesting PMEPA1 may be a potential immunotherapeutic target in CC.

The CpG island methylator phenotype increases the risk of high-grade squamous intraepithelial lesions and cervical cancer

Background

High-risk human papillomavirus (HR-HPV) infection is the main cause of cervical cancer, but additional alterations are necessary for its development. Abnormal DNA methylation has an important role in the origin and dissemination of cervical cancer and other human tumors. In this work, we analyzed the methylation of eight genes (AJAP1, CDH1, CDH13, MAGI2, MGMT, MYOD1, RASSF1A and SOX17) that participate in several biological processes for the maintenance of cell normality. We analyzed DNA methylation by methylation-specific PCR (MSP) and HPV infection using the INNO‑LiPA genotyping kit in 59 samples diagnostic of normal cervical tissue (non-SIL), 107 low-grade squamous intraepithelial lesions (LSILs), 29 high-grade squamous intraepithelial lesions (HSILs) and 51 cervical cancers (CCs).

Results

We found that all samples of LSIL, HSIL, and CC were HPV-positive, and the genotypes with higher frequencies were 16, 18, 51 and 56. In general, the genes analyzed displayed a significant tendency toward an increase in methylation levels according to increasing cervical lesion severity, except for the CDH13 gene. High CpG island methylator phenotype (CIMP) was associated with a 50.6-fold (95% CI 4.72–2267.3)-increased risk of HSIL and a 122-fold risk of CC (95% CI 10.04–5349.7).

Conclusions

We found that CIMP high was significantly associated with HSIL and CC risk. These results could indicate that CIMP together with HR-HPV infection and other factors participates in the development of HSIL and CC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01224-0.

Epigenetic approaches for cervical neoplasia screening (Review)

Human papillomavirus (HPV) infection is the leading cause of cervical cancer. The Papanicolaou cytology test is the usually employed type of screening for this infection; however, its sensibility is limited. Only a small percentage of women infected with high-risk HPV develop cervical cancer with an array of genetic and epigenetic modifications. Thus, it is necessary to develop rapid, reproducible and minimally invasive technologies for screening. DNA methylation has gained attention as an alternative method for molecular diagnosis and prognosis in HPV infection. The aim of the present review was to highlight the potential of DNA methylation in cervical neoplasia screening for clinical applications. It was observed that the methylation human and viral genes was correlated with high-grade lesions and cancer. Methylation biomarkers have shown a good capacity to discriminate between high-grade lesions with a transformative potential and cervical cancer, being able to detect these modifications at an early stage. With further research, the epigenetic profiles and subtypes of the tumors could be elaborated, which would aid in therapy selection by opening avenues in personalized precision medicine. Response to therapy could also be evaluated through such methods and the accessibility of liquid biopsies would allow a constant monitoring of the patient's status without invasive sampling techniques.

An 8-gene DNA methylation signature predicts the recurrence risk of cervical cancer

Objective

This study examined the predictive utility of DNA methylation for cervical cancer recurrence.

Methods

DNA methylation and RNA expression data for patients with cervical cancer were downloaded from The Cancer Genome Atlas. Differentially methylated genes (DMGs) and differentially expressed genes were screened and extracted via correlation analysis. A support vector machine (SVM)-based recurrence prediction model was established using the selected DMGs. Cox regression analysis and receiver operating characteristic curve analysis were used for self-evaluation. The Gene Expression Omnibus (GEO) database was applied for external validation. Functional enrichment was determined using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.

Results

An eight-gene DNA methylation signature identified patients with a high risk of recurrence (area under the curve = 0.833). The SVM score was an independent risk factor for recurrence (hazard ratio [HR] = 0.418; 95% confidence interval [CI] = 0.26–0.67). The independent GEO database analysis further supported the result.

Conclusion

An eight-gene DNA methylation signature predictive of cervical cancer recurrence was identified in this study, and this signature may help identify patients at high risk of recurrence and improve clinical treatment.

Novel prognostic prediction model constructed through machine learning on the basis of methylation-driven genes in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a common tumor with poor prognosis and is closely related to many aberrant gene expressions. DNA methylation is an important epigenetic modification mechanism and a novel research target. Thus, exploring the relationship between methylation-driven genes and KIRC prognosis is important. The methylation profile, methylation-driven genes, and methylation characteristics in KIRC was revealed through the integration of KIRC methylation, RNA-seq, and clinical information data from The Cancer Genome Atlas. The Lasso regression was used to establish a prognosis model on the basis of methylation-driven genes. Then, a trans-omics prognostic nomogram was constructed and evaluated by combining clinical information and methylated prognosis model. A total of 242 methylation-driven genes were identified. The Gene Ontology terms of these methylation-driven genes mainly clustered in the activation, adhesion, and proliferation of immune cells. The methylation prognosis prediction model that was established using the Lasso regression included four genes in the methylation data, namely, FOXI2, USP44, EVI2A, and TRIP13. The areas under the receiver operating characteristic curve of 1-, 3-, and 5-year survival rates were 0.810, 0.824, and 0.799, respectively, in the training group and 0.794, 0.752, and 0.731, respectively, in the testing group. An easy trans-omics nomogram was successfully established. The C-indices of the nomogram in the training and the testing groups were 0.8015 and 0.8389, respectively. The present study revealed the overall perspective of methylation-driven genes in KIRC and can help in the evaluation of the prognosis of KIRC patients and provide new clues for further study.

Reclassification of Kidney Clear Cell Carcinoma Based on Immune Cell Gene-Related DNA CpG Pairs

Background: A new method was developed based on the relative ranking of gene expression level, overcoming the flaw of the batch effect, and having reliable results in various studies. In the current study, we defined the two methylation sites as a pair. The methylation level in a specific sample was subject to pairwise comparison to calculate a score for each CpGs-pair. The score was defined as a CpGs-pair score. If the first immune-related CpG value was higher than the second one in a specific CpGs-pair, the output score of this immune-related CpGs-pair was 1; otherwise, the output score was 0. This study aimed to construct a new classification of Kidney Clear Cell Carcinoma (KIRC) based on DNA CpGs (methylation sites) pairs. Methods: In this study, the biomarkers of 28 kinds of immune infiltration cells and corresponding methylation sites were acquired. The methylation data were compared between KIRC and normal tissue samples, and differentially methylated sites (DMSs) were obtained. Then, DNA CpGs-pairs were obtained according to the pairs of DMSs. In total, 441 DNA CpGs-pairs were utilized to construct a classification using unsupervised clustering analysis. We also analyzed the potential mechanism and therapy of different subtypes, and validated them in a testing set. Results: The classification of KIRC contained three subgroups. The clinicopathological features were different across three subgroups. The distribution of immune cells, immune checkpoints and immune-related mechanisms were significantly different across the three clusters. The mutation and copy number variation (CNV) were also different. The clinicopathological features and potential mechanism in the testing dataset were consistent with those in the training set. Conclusions: Our findings provide a new accurate and stable classification for developing personalized treatments for the new specific subtypes.

DNA methylation patterns-based subtype distinction and identification of soft tissue sarcoma prognosis

Soft tissue sarcomas (STSs) are heterogeneous at the clinical with a variable tendency of aggressive behavior. In this study, we constructed a specific DNA methylation-based classification to identify the distinct prognosis-subtypes of STSs based on the DNA methylation spectrum from the TCGA database. Eventually, samples were clustered into 4 subgroups, and their survival curves were distinct from each other. Meanwhile, the samples in each subgroup reflected differentially in several clinical features. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was also conducted on the genes of the corresponding promoter regions of the above-described specific methylation sites, revealing that these genes were mainly concentrated in certain cancer-associated biological functions and pathways. In addition, we calculated the differences among clustered methylation sites and performed the specific methylation sites with LASSO algorithm. The selection operator algorithm was employed to derive a risk signature model, and a prognostic signature based on these methylation sites performed well for risk stratification in STSs patients. At last, a nomogram consisted of clinical features and risk score was developed for the survival prediction. This study declares that DNA methylation-based STSs subtype classification is highly relevant for future development of personalized therapy as it identifies the prediction value of patient prognosis.

DNA methylation-based lung adenocarcinoma subtypes can predict prognosis, recurrence, and immunotherapeutic implications

The marked heterogeneity of lung adenocarcinoma (LUAD) makes its diagnosis and treatment difficult. In addition, the aberrant DNA methylation profile contributes to tumor heterogeneity and alters the immune response. We used DNA methylation array data from publicly available databases to establish a predictive model for LUAD prognosis. Thirty-three methylation sites were identified as specific prognostic biomarkers, independent of patients' clinical characteristics. These methylation profiles were used to identify potential drug candidates and study the immune microenvironment of LUAD and response to immunotherapy. When compared with the high-risk group, the low-risk group had a lower recurrence rate and favorable prognosis. The tumor microenvironment differed between the two groups as reflected by the higher number of resting dendritic cells and a lower number of monocytes and resting mast cells in the low-risk group. Moreover, low-risk patients reported higher immune and stromal scores, lower tumor purity, and higher expression of HLA genes. Low-risk patients responded well to immunotherapy due to higher expression of immune checkpoint molecules and lower stemness index. Thus, our model predicted a favorable prognosis and increased overall survival for patients in the low-risk methylation group. Further, this model could provide potential drug targets to develop effective immunotherapies for LUAD.

A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

Simple Summary

Bladder cancer (BC) development is highly related to immune cell infiltration. In this study, we aimed to construct a new classification of bladder cancer molecular subtypes based on immune-cell-associated CpG(Methylation) sites. The classification was accurate and stable. BC patients were successfully divided into three subtypes based on the immune-cell-associated CpG sites. The clinicopathologic features, distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN_respons, Type II IFN response, and DNA stemness score (DNAss) presented significant differences among the three subgroups. The specific genomic alteration was also different across subgroups. High-level immune infiltration showed a correlation with high-level methylation. A lower RNA stemness score (RNAss) was associated with higher immune infiltration. Cluster 2 demonstrated a better response to chemotherapy. The anti-cancer targeted drug therapy results are different among the three subgroups.

Abstract

Background: Bladder cancer is highly related to immune cell infiltration. This study aimed to develop a new classification of BC molecular subtypes based on immune-cell-associated CpG sites. Methods: The genes of 28 types of immune cells were obtained from previous studies. Then, methylation sites corresponding to immune-cell-associated genes were acquired. Differentially methylated sites (DMSs) were identified between normal samples and bladder cancer samples. Unsupervised clustering analysis of differentially methylated sites was performed to divide the sites into several subtypes. Then, the potential mechanism of different subtypes was explored. Results: Bladder cancer patients were divided into three groups. The cluster 3 subtype had the best prognosis. Cluster 1 had the poorest prognosis. The distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN Response, Type II IFN Response, and DNAss presented significant differences among the three subgroups. The distribution of genomic alterations was also different. Conclusions: The proposed classification was accurate and stable. BC patients could be divided into three subtypes based on the immune-cell-associated CpG sites. Specific biological signaling pathways, immune mechanisms, and genomic alterations were varied among the three subgroups. High-level immune infiltration was correlated with high-level methylation. The lower RNAss was associated with higher immune infiltration. The study of the intratumoral immune microenvironment may provide a new perspective for BC therapy.

Markers of Angiogenesis, Lymphangiogenesis, and Epithelial-Mesenchymal Transition (Plasticity) in CIN and Early Invasive Carcinoma of the Cervix: Exploring Putative Molecular Mechanisms Involved in Early Tumor Invasion

The establishment of a proangiogenic phenotype and epithelial-to-mesenchymal transition (EMT) are considered as critical events that promote the induction of invasive growth in epithelial tumors, and stimulation of lymphangiogenesis is believed to confer the capacity for early dissemination to cancer cells. Recent research has revealed substantial interdependence between these processes at the molecular level as they rely on common signaling networks. Of great interest are the molecular mechanisms of (lymph-)angiogenesis and EMT associated with the earliest stages of transition from intraepithelial development to invasive growth, as they could provide the source of potentially valuable tools for targeting tumor metastasis. However, in the case of early-stage cervical cancer, the players of (lymph-)angiogenesis and EMT processes still remain substantially uncharacterized. In this study, we used RNA sequencing to compare transcriptomes of HPV(+) preinvasive neoplastic lesions and early-stage invasive carcinoma of the cervix and to identify (lymph-)angiogenesis- and EMT-related genes and pathways that may underlie early acquisition of invasive phenotype and metastatic properties by cervical cancer cells. Second, we applied flow cytometric analysis to evaluate the expression of three key lymphangiogenesis/EMT markers (VEGFR3, MET, and SLUG) in epithelial cells derived from enzymatically treated tissue specimens. Overall, among 201 differentially expressed genes, a considerable number of (lymph-)angiogenesis and EMT regulatory factors were identified, including genes encoding cytokines, growth factor receptors, transcription factors, and adhesion molecules. Pathway analysis confirmed enrichment for angiogenesis, epithelial differentiation, and cell guidance pathways at transition from intraepithelial neoplasia to invasive carcinoma and suggested immune-regulatory/inflammatory pathways to be implicated in initiation of invasive growth of cervical cancer. Flow cytometry showed cell phenotype-specific expression pattern for VEGFR3, MET, and SLUG and revealed correlation with the amount of tumor-infiltrating lymphocytes at the early stages of cervical cancer progression. Taken together, these results extend our understanding of driving forces of angiogenesis and metastasis in HPV-associated cervical cancer and may be useful for developing new treatments.

DNA methylation data-based prognosis-subtype distinctions in patients with esophageal carcinoma by bioinformatic studies

Esophageal carcinoma (ESCA) is caused by the accumulation of genetic and epigenetic alterations in esophageal mucosa. Of note, the earliest and the most frequent molecular behavior in the complicated pathogenesis of ESCA is DNA methylation. In the present study, we downloaded data of 178 samples from The Cancer Genome Atlas (TCGA) database to explore specific DNA methylation sites that affect prognosis in ESCA patients. Consequently, we identified 1,098 CpGs that were significantly associated with patient prognosis. Hence, these CpGs were used for consensus clustering of the 178 samples into seven clusters. Specifically, the samples in each group were different in terms of age, gender, tumor stage, histological type, metastatic status, and patient prognosis. We further analyzed 1,224 genes in the corresponding promoter regions of the 1,098 methylation sites, and enriched these genes in biological pathways with close correlation to cellular metabolism, enzymatic synthesis, and mitochondrial autophagy. In addition, nine representative specific methylation sites were screened using the weighted gene coexpression network analysis. Finally, a prognostic prediction model for ESCA patients was built in both training and validation cohorts. In summary, our study revealed that classification based on specific DNA methylation sites could reflect ESCA heterogeneity and contribute to the improvement of individualized treatment and precise prognostic prediction.

Dual Regulation of Histone Methylation by mTOR Complexes Controls Glioblastoma Tumor Cell Growth via EZH2 and SAM

Epigenetic regulation known for DNA methylation and histone modification is critical for securing proper gene expression and chromosomal function, and its aberration induces various pathologic conditions including cancer. Trimethylation of histone H3 on lysine 27 (H3K27me3) is known to suppress various genes related to cancer cell survival and the level of H3K27me3 may have an influence on tumor progression and malignancy. However, it remains unclear how histone methylation is regulated in response to genetic mutation and microenvironmental cues to facilitate the cancer cell survival. Here, we report a novel mechanism of the specific regulation of H3K27me3 by cooperatively two mTOR complexes, mTORC1 and mTORC2 in human glioblastoma (GBM). Integrated analyses revealed that mTORC1 upregulates the protein expression of enhancer of zeste homolog 2, a main component of polycomb repressive complex 2 which is known as H3K27-specific methyltransferase. The other mTOR complex, mTORC2, regulates production of S-adenosylmethionine, an essential substrate for histone methylation. This cooperative regulation causes H3K27 hypermethylation which subsequently promotes tumor cell survival both in vitro and in vivo xenografted mouse tumor model. These results indicate that activated mTORC1 and mTORC2 complexes cooperatively contribute to tumor progression through specific epigenetic regulation, nominating them as an exploitable therapeutic target against cancer. IMPLICATIONS: A dynamic regulation of histone methylation by mTOR complexes promotes tumor growth in human GBM, but at the same time could be exploitable as a novel therapeutic target against this deadly tumor.

DNA Methylation and Hydroxymethylation in Cervical Cancer: Diagnosis, Prognosis and Treatment

Recent discoveries have led to the development of novel ideas and techniques that have helped elucidate the correlation between epigenetics and tumor biology. Nowadays, the field of tumor genetics has evolved to include a new type of regulation by epigenetics. An increasing number of studies have demonstrated the importance of DNA methylation and hydroxymethylation in specific genes in the progression of cervical cancer. Determining the methylation and hydroxymethylation profiles of these genes will help in the early prevention and diagnosis, monitoring recurrence, prognosis, and treatment of patients with cervical cancer. In this review, we focus on the significance of aberrant DNA methylation and hydroxymethylation in cervical cancer and the use of these epigenetic signatures in clinical settings.