Identification of Hyper-Methylated Tumor Suppressor Genes-Based Diagnostic Panel for Esophageal Squamous Cell Carcinoma (ESCC) in a Chinese Han Population

Effects of DNA Methylation of HPA-Axis Genes of F1 Juvenile Induced by Maternal Density Stress on Behavior and Immune Traits in Root Voles (Microtus oeconomus)-A Field Experiment

The literature shows that maternal stress can influence behavior and immune function in F1. Yet, most studies on these are from the laboratory, and replicated studies on the mechanisms by which maternal stress drives individual characteristics are still not fully understood in wild animals. We manipulated high- and low-density parental population density using large-scale field enclosures and examined behavior and immune traits. Within the field enclosures, we assessed anti-keyhole limpet hemocyanin immunoglobulin G (anti-KLH IgG) level, phytohemagglutinin (PHA) responses, hematology, cytokines, the depressive and anxiety-like behaviors and prevalence and intensity of coccidial infection. We then collected brain tissue from juvenile voles born at high or low density, quantified mRNA and protein expression of corticotropin-releasing hormone (CRH) and glucocorticoid receptor gene (NR3C1) and measured DNA methylation at CpG sites in a region that was highly conserved with the prairie vole CRH and NR3C1 promoter. At high density, we found that the F1 had a lower DNA methylation level of CRH and a higher DNA methylation level of NR3C1, which resulted in an increase in the expression levels of the CRH mRNA and protein expression and further reduced the expression levels of the NR3C1 mRNA and protein expression, and ultimately led to have delayed responses to acute immobilization stress. Juvenile voles born at high density also reduced anti-KLH IgG levels and PHA responses, increased cytokines, and depressive and anxiety-like behaviors, and the effects further led to higher coccidial infection. From the perspective of population density inducing the changes in behavior and immunity at the brain level, our results showed a physiological epigenetic mechanism for population self-regulation in voles. Our results indicate that altering the prenatal intrinsic stress environment can fundamentally impact behavior and immunity by DNA methylation of HPA-axis genes and can further drive population fluctuations in wild animals.

Alteration in DNA methylation patterns: Epigenetic signatures in gastrointestinal cancers

Abnormalities in epigenetic modifications can cause malignant transformations in cells, leading to cancers of the gastrointestinal (GI) tract, which accounts for 20% of all cancers worldwide. Among the epigenetic alterations, DNA hypomethylation is associated with genomic instability. In addition, CpG methylation and promoter hypermethylation have been recognized as biomarkers for different malignancies. In GI cancers, epigenetic alterations affect genes responsible for cell cycle control, DNA repair, apoptosis, and tumorigenic-specific signaling pathways. Understanding the pattern of alterations in DNA methylation in GI cancers could help scientists discover new molecular-based pharmaceutical treatments. This study highlights alterations in DNA methylation in GI cancers. Understanding epigenetic differences among GI cancers may improve targeted therapies and lead to the discovery of new diagnostic biomarkers.

DNA methylation markers in esophageal cancer

Background

Esophageal cancer (EC) is a prevalent malignancy characterized by a low 5-year survival rate, primarily attributed to delayed diagnosis and limited therapeutic options. Currently, early detection of EC heavily relies on endoscopy and pathological examination, which pose challenges due to their invasiveness and high costs, leading to low patient compliance. The detection of DNA methylation offers a non-endoscopic, cost-effective, and secure approach that holds promising prospects for early EC detection.

Methods

To identify improved methylation markers for early EC detection, we conducted a comprehensive review of relevant literature, summarized the performance of DNA methylation markers based on different input samples and analytical methods in EC early detection and screening.

Findings

This review reveals that blood cell free DNA methylation-based method is an effective non-invasive method for early detection of EC, although there is still a need to improve its sensitivity and specificity. Another highly sensitive and specific non-endoscopic approach for early detection of EC is the esophageal exfoliated cells based-DNA methylation analysis. However, while there are substantial studies in esophageal adenocarcinoma, further more validation is required in esophageal squamous cell carcinoma.

Conclusion

In conclusion, DNA methylation detection holds significant potential as an early detection and screening technology for EC.

Genome-wide DNA methylation profiling of gastric cardia cancer

BACKGROUND AND AIM

Aberrant DNA methylation has been found in various cancer types including gastric cancer, yet the genome-wide DNA methylation profile of gastric cardia cancer (GCC) remains unclear. Therefore, we aimed to profile the DNA methylation pattern of GCC and identify promising diagnostic epigenetic biomarkers.

METHODS

We investigated the genome-wide DNA methylation pattern in eight pairs of GCC and adjacent normal tissues using Illumina 850K microarrays. Subsequently, bisulfite-pyrosequencing and quantitative real-time PCR were performed on eight pairs of GCC-adjacent normal tissues for validation. Finally, we performed immunohistochemistry to examine ADHFE1 expression on 126 pairs of GCC-adjacent normal samples.

RESULTS

DNA methylome analysis showed global hypomethylation and local hypermethylation of promoter cytosine-phosphate-guanine (CpG) islands (CGIs) in GCC tissues compared with gastric cardia normal mucosa (P < 2.2 × 10^-16 ). Differential methylation analysis identified a total of 91 723 differentially-methylated probes (DMPs), and the candidate gene with the largest average DNA methylation difference mapped to ADHFE1 (mean Δβ = 0.53). Subsequently, three DMPs in the ADHFE1 promoter were validated by pyrosequencing. Notably, the mean methylation level of the three candidate DMPs (ADHFE1_cg08090772, ADHFE1_cg19283840, and ADHFE1_cg20295442) was negatively associated with ADHFE1 mRNA expression level (Spearman rho = -0.64, P = 0.01). Moreover, both mRNA (P = 0.0213) and protein (P < 0.0001) expression of ADHFE1 were significantly decreased in GCCs compared with the adjacent normal tissues.

CONCLUSIONS

Our results reveal DNA methylation aberrations in GCC and that ADHFE1 gene DNA methylation contributes to the risk of GCC, thus providing novel mechanistic insights into gastric cardia cancer carcinogenesis.

Contribution of labor related gene subtype classification on heterogeneity of polycystic ovary syndrome

OBJECTIVE

As one of the most common endocrine disorders in women of reproductive age, polycystic ovary syndrome (PCOS) is highly heterogeneous with varied clinical features and diverse gestational complications among individuals. The patients with PCOS have 2-fold higher risk of preterm labor which is associated with substantial infant morbidity and mortality and great socioeconomic cost. The study was designated to identify molecular subtypes and the related hub genes to facilitate the susceptibility assessment of preterm labor in women with PCOS.

METHODS

Four mRNA datasets (GSE84958, GSE5090, GSE43264 and GSE98421) were obtained from Gene Expression Omnibus database. Twenty-eight candidate genes related to preterm labor or labor were yielded from the researches and our unpublished data. Then, we utilized unsupervised clustering to identify molecular subtypes in PCOS based on the expression of above candidate genes. Key modules were generated with weighted gene co-expression network analysis R package, and their hub genes were generated with CytoHubba. The probable biological function and mechanism were explored through Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis. In addition, STRING and Cytoscape software were used to identify the protein-protein interaction (PPI) network, and the molecular complex detection (MCODE) was used to identify the hub genes. Then the overlapping hub genes were predicted.

RESULTS

Two molecular subtypes were found in women with PCOS based on the expression similarity of preterm labor or labor-related genes, in which two modules were highlighted. The key modules and PPI network have five overlapping five hub genes, two of which, GTF2F2 and MYO6 gene, were further confirmed by the comparison between clustering subgroups according to the expression of hub genes.

CONCLUSIONS

Distinct PCOS molecular subtypes were identified with preterm labor or labor-related genes, which might uncover the potential mechanism underlying heterogeneity of clinical pregnancy complications in women with PCOS.

Multi-omic characterization of genome-wide abnormal DNA methylation reveals diagnostic and prognostic markers for esophageal squamous-cell carcinoma

This study investigates aberrant DNA methylations as potential diagnosis and prognosis markers for esophageal squamous-cell carcinoma (ESCC), which if diagnosed at advanced stages has <30% five-year survival rate. Comparing genome-wide methylation sites of 91 ESCC and matched adjacent normal tissues, we identified 35,577 differentially methylated CpG sites (DMCs) and characterized their distribution patterns. Integrating whole-genome DNA and RNA-sequencing data of the same samples, we found multiple dysregulated transcription factors and ESCC-specific genomic correlates of identified DMCs. Using featured DMCs, we developed a 12-marker diagnostic panel with high accuracy in our dataset and the TCGA ESCC dataset, and a 4-marker prognostic panel distinguishing high-risk patients. In-vitro experiments validated the functions of 4 marker host genes. Together these results provide additional evidence for the important roles of aberrant DNA methylations in ESCC development and progression. Our DMC-based diagnostic and prognostic panels have potential values for clinical care of ESCC, laying foundations for developing targeted methylation assays for future non-invasive cancer detection methods.

Epigenome-Wide DNA Methylation Profiling in Colorectal Cancer and Normal Adjacent Colon Using Infinium Human Methylation 450K

The aims were to profile the DNA methylation in colorectal cancer (CRC) and to explore cancer-specific methylation biomarkers. Fifty-four pairs of CRCs and the adjacent normal tissues were subjected to Infinium Human Methylation 450K assay and analysed using ChAMP R package. A total of 26,093 differentially methylated probes were identified, which represent 6156 genes; 650 probes were hypermethylated, and 25,443 were hypomethylated. Hypermethylated sites were common in CpG islands, while hypomethylated sites were in open sea. Most of the hypermethylated genes were associated with pathways in cancer, while the hypomethylated genes were involved in the PI3K-AKT signalling pathway. Among the identified differentially methylated probes, we found evidence of four potential probes in CRCs versus adjacent normal; HOXA2 cg06786372, OPLAH cg17301223, cg15638338, and TRIM31 cg02583465 that could serve as a new biomarker in CRC since these probes were aberrantly methylated in CRC as well as involved in the progression of CRC. Furthermore, we revealed the potential of promoter methylation ADHFE1 cg18065361 in differentiating the CRC from normal colonic tissue from the integrated analysis. In conclusion, aberrant DNA methylation is significantly involved in CRC pathogenesis and is associated with gene silencing. This study reports several potential important methylated genes in CRC and, therefore, merit further validation as novel candidate biomarker genes in CRC.

Identification of Signature Genes in the PD-1 Relative Gastric Cancer Using a Combined Analysis of Gene Expression and Methylation Data

BACKGROUND

The morbidity and mortality rates for gastric cancer (GC) rank second among all cancers, indicating the serious threat it poses to human health, as well as human life. This study aims to identify the pathways and genes as well as investigate the molecular mechanisms of tumor-related genes in gastric cancer (GC).

METHOD

We compared differentially expressed genes (DEGs) and differentially methylated genes (DMGs) in gastric cancer and normal tissue samples using The Cancer Genome Atlas (TCGA) data. The Kyoto Encyclopedia of Gene and Genome (KEGG) and the Gene Ontology (GO) enrichment analysis' pathway annotations were conducted on DMGs and DEGs using a clusterProfiler R package to identify the important functions, as well as the biological processes and pathways involved. The intersection of the two was chosen and defined as differentially methylated and expressed genes (DMEGs). For DMEGs, we used the principal component analysis (PCA) to differentiate gastric cancer from adjacent samples. The linear discriminant analysis method was applied to categorize the samples using DMEGs methylation data and DMEGs expression profiles data and was validated using the leave-one-out cross-validation (LOOCV) method. We plotted the ROC curve for the classification and calculated the AUC (area under the ROC curve) value for a more intuitive view of the classification effect. We also used the NetworkAnalyst 3.0 tool to analyze DMEGs, using DrugBank to acquire information on protein-drug interactions and generate a network map of gene-drug interactions.

RESULTS

We identified a total of 971 DMGs in 188 PD-1 negative and 187 PD-1 positive gastric cancer samples obtained from TCGA. The KEGG and GO enrichment analysis showed the involvement of the regulation of ion transmembrane transport, collagen-containing extracellular matrix, cell-cell junction, and peptidase regulator activity. We simultaneously obtained 1,189 DEGs, out of which 986 were downregulated, while 203 were upregulated in tumors. The enriched analysis of the GO's and KEGG's pathways indicated that the most significant pathways included an intestinal immune network for IgA production, Staphylococcus aureus infection, cytokine-cytokine receptor interaction, and viral protein interaction with cytokine and cytokine receptor, which have previously been linked with gastric cancer. The compound DB01830 can bind well to the active site of the LCK protein and shows good stability, thus making it a potential inhibitor of the LCK protein. To observe the relationship between DMEGs' expression and prognosis, we observed 10 genes, among which were TRIM29, TSPAN8, EOMES, PPP1R16B, SELL, PCED1B, IYD, JPH1, CEACAM5, and RP11-44K6.2. Their high expressions were related to high risks. Besides, those genes were validated in different internal and external validation sets.

CONCLUSION

These results may provide potential molecular biological therapy for PD-1 negative gastric cancer.

SALL Proteins; Common and Antagonistic Roles in Cancer

Simple Summary

Transcription factors play essential roles in regulating gene expression, impacting the cell phenotype and function, and in the response of cells to environmental conditions. Alterations in transcription factors, including gene amplification or deletion, point mutations, and expression changes, are implicated in carcinogenesis, cancer progression, metastases, and resistance to cancer treatments. Not surprisingly, transcription factor activity is altered in numerous cancers, representing a unique class of cancer drug targets. This review updates and integrates information on the SALL family of transcription factors, highlighting the synergistic and/or antagonistic functions they perform in various cancer types.

Abstract

SALL proteins are a family of four conserved C2H2 zinc finger transcription factors that play critical roles in organogenesis during embryonic development. They regulate cell proliferation, survival, migration, and stemness; consequently, they are involved in various human genetic disorders and cancer. SALL4 is a well-recognized oncogene; however, SALL1–3 play dual roles depending on the cancer context and stage of the disease. Current reviews of SALLs have focused only on SALL2 or SALL4, lacking an integrated view of the SALL family members in cancer. Here, we update the recent advances of the SALL members in tumor development, cancer progression, and therapy, highlighting the synergistic and/or antagonistic functions they perform in similar cancer contexts. We identified common regulatory mechanisms, targets, and signaling pathways in breast, brain, liver, colon, blood, and HPV-related cancers. In addition, we discuss the potential of the SALL family members as cancer biomarkers and in the cancer cells’ response to therapies. Understanding SALL proteins’ function and relationship will open new cancer biology, clinical research, and therapy perspectives.

Role of T-box genes in cancer, epithelial-mesenchymal transition, and cancer stem cells

Sharing a common DNA binding motif called T-box, transcription factor T-box gene family controls embryonic development and is also involved in cancer progression and metastasis. Cancer metastasis shows therapy resistance and involves complex processes. Among them, epithelial-mesenchymal transition (EMT) triggers cancer cell invasiveness and the acquisition of stemness of cancer cells, called cancer stem cells (CSCs). CSCs are a small fraction of tumor bulk and are capable of self-renewal and tumorsphere formation. Recent progress has highlighted the critical roles of T-box genes in cancer progression, EMT, and CSC function, and such regulatory functions of T-box genes have emerged as potential therapeutic candidates for cancer. Herein we summarize the current understanding of the regulatory mechanisms of T-box genes in cancer, EMT, and CSCs, and discuss the implications of targeting T-box genes as anticancer therapeutics.

Identification of early diagnostic biomarkers via WGCNA in gastric cancer

BACKGROUND

Gastric cancer (GC) is the world's second-leading cause of cancer-related mortality, continuing to make it a serious healthcare concern. Even though the prevalence of GC reduces, the prognosis for GC patients remains poor in terms of a lack of reliable biomarkers to diagnose early GC and predict chemosensitivity and recurrence.

METHODS AND MATERIAL

We integrated the gene expression patterns of gastric cancers from four RNAseq datasets (GSE113255, GSE142000, GSE118897, and GSE130823) from Gene Expression Omnibus (GEO) database to recognize differentially expressed genes (DEGs) between normal and GC samples. A gene co-expression network was built using weighted co-expression network analysis (WGCNA). Furthermore, RT-qPCR was performed to validate the in silico results.

RESULTS

The red modules in GSE113255, Turquoise in GSE142000, Brown in GSE118897, and the green-yellow module in GSE130823 datasets were found to be highly correlated with the anatomical site of GC. ITGAX, CCL14, ADHFE1, and HOXB13) as the hub gene are differentially expressed in tumor and non-tumor gastric tissues in this study. RT-qPCR demonstrated a high level of the expression of this gene.

CONCLUSION

The expression levels of ITGAX, CCL14, ADHFE1, and HOXB13 in GC tumor tissues are considerably greater than in adjacent normal tissues. Systems biology approaches identified that these genes could be possible GC marker genes, providing ideas for other experimental studies in the future.

ADHFE1 is a correlative factor of patient survival in cancer

Alcohol dehydrogenase iron containing 1 (ADHFE1) encodes a hydroxyacid-oxoacid transhydrogenase participating in multiple biological processes. The role of ADHFE1 in cancer has not been fully uncovered. Herein, we performed data analysis to investigate the expression of ADHFE1 and the underlying regulatory mechanisms, its relationship with cancer patients’ survival, and the relevant pathways in cancer. A range of recognized, web-available databases and bioinformatics tools were used in this in silico study. We found that ADHFE1 was frequently downregulated and hypermethylated in various cancer cell lines and tissue samples. High expression of ADHFE1 was positively associated with favorable patient prognosis in breast, colon, and gastric cancers. Pathway analysis revealed its potential role in cancer-related biological processes, including energy metabolism, DNA replication, and cell cycle regulation. AHDFE1 mRNA expression and DNA methylation can potentially be used as diagnostic markers in cancer and might be of great value in predicting the survival of patients with cancer.

Development and validation of 3-CpG methylation prognostic signature based on different survival indicators for colorectal cancer

Abnormal DNA methylation is considered a vital hallmark to regulate gene expression and influence the development and progression of colorectal cancer (CRC). Although CRC-related methylation prognostic models have been developed, their clinical application is limited due to the lack of external validation and extension to other survival evaluation indicators. Therefore, this study aimed to develop and validate novel methylation prognostic models correlated with different survival indicators for individualized prognosis prediction for CRC patients. The prognostic-related CpG sites of methylation-driven genes screened by the MethylMix algorithm were identified and validated in The Cancer Genome Atlas (TCGA) CRC methylation data and our methylation data. The prognostic models correlated with different survival evaluation indicators (overall survival [OS] and disease-free survival [DFS]) were developed and validated in the TCGA CRC dataset (N = 376) and our independent CRC dataset (N = 227). We utilized the combination of selected 3-CpG methylation sites in three genes (DAPP1, FAM3D, and PIGR) to construct a prognostic risk-score model. In the training dataset, Kaplan-Meier survival analysis demonstrated that high-risk patients had significantly poorer survival than low-risk patients (p_OS  = .0014; p_DFS  < .001). Then, the 3-CpG methylation signature was successfully validated as an independent predictor in the testing data set (p_OS  = .016; p_DFS  = .016). A prognostic nomogram was constructed and validated. Additionally, mediation analysis revealed the direct effect of the methylation signature on CRC prognosis (p_OS  = 9.149e-06; p_DFS  = .001). In summary, our study revealed that the 3-CpG methylation signature might be a potential prognostic indicator to facilitate individualized survival prediction for CRC patients.

Integrative genome-scale analysis of immune infiltration in esophageal carcinoma

To explore the molecular mechanism in the esophageal squamous carcinoma (ESCC) environment, we selected datasets of ESCC patients from The Cancer Genome Atlas (TCGA) (n = 78) and explored the infiltration condition of 24 immune cells in each sample. We assorted the microenvironment of ESCC into two Infiltration groups (I and II) and built a random forest classifier model. We showed traits of gene and clinicopathology in the tumor microenvironment (TME) phenotypes systematically. Infiltration I had low infiltration of immune cells and immunomodulators but relatively higher mutation load, while Infiltration II was enriched with cytotoxic T cells and immunosuppressive cells. The upregulation of several immune cytokines like IFN-γ, TNF-β, and PD-L1 was seen in Infiltration II. The infiltration group was an independent predictor of prognosis showed by Multivariable Cox analysis (Infiltration II vs. I, hazard ratio = 2.73, 95% confidence interval = 1.08-6.91, p = 0.03). All the results can be verified in datasets from the Gene Expression Omnibus database (GEO) and our institution (n = 98). Our results demonstrate a synthesis of the infiltration pattern of the immune in ESCC. We reveal the mechanism of TME, which may contribute to the progress of immunotherapy for patients with ESCC.

Eomes promotes esophageal carcinoma progression by recruiting Treg cells through the CCL20-CCR6 pathway

Eomesodermin (Eomes) is a T-box transcription factor that drives the differentiation and function of cytotoxic lymphocytes. However, the underlying function and mechanism of Eomes in tumor cells remains elusive. Here, we studied the role of Eomes in human esophageal squamous cell carcinoma (ESCC). Using 2 human ESCC cell lines, we found that Eomes knockdown reduced esophageal cancer cell proliferation and that the esophageal cancer cell cycle was blocked in the G2/M phase. Mechanistically, we identified CCL20 as the main downstream target of Eomes. Furthermore, we found that CCL20 could chemoregulate regulatory T cells (Tregs) through their specific receptor CCR6, then promoting the proliferation of esophageal cancer cells. Eomes knockdown also delayed the growth of human ESCC xenografts in BALB/c nude mice. Importantly, in 133 human ESCC tissues, high Eomes levels were associated with poor clinical prognosis. Overall, our findings suggested that the Eomes-CCL20-CCR6 pathway plays a vital role in human ESCC progress. Therefore, targeting this pathway may represent a promising strategy for controlling human ESCC.

Six Novel Biomarkers for Diagnosis and Prognosis of Esophageal squamous cell carcinoma: validated by scRNA-seq and qPCR

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide. ESCC has a generally poor prognosis and there is a lack of available biomarkers for diagnosis and prognosis. The aim of the study was to identify novel biomarkers for ESCC. We screened the overlapping differentially expressed genes (DEGs) acquired from six Gene Expression Omnibus (GEO) ESCC datasets and The Cancer Genome Atlas (TCGA) ESCC datasets. Subsequently, protein-protein interaction network analysis was performed to identify the key hub genes. Then, Kaplan Meier survival and receiver operating curve (ROC) analysis were utilized to clarify the diagnostic and prognostic role of these hub genes. The UALCAN database, single cell RNA sequencing (scRNA-seq) and real-time quantitative PCR (qPCR) were performed to confirm the expression levels of identified hub genes. Finally, immune infiltration analysis was conducted to investigate the role of these genes in the pathogenesis of ESCC. The results showed that PBK, KIF2C, NUF2, KIF20A, RAD51AP1, and DEPDC1 effectively distinguish ESCC tissues from normal samples, and all of them were significantly correlated with overall survival. The results of scRNA-seq and qPCR indicated that the expression levels of hub genes in ESCC were significantly higher than in normal cells or tissues. Further immune infiltration analysis showed that infiltration of dendritic cells was significantly negatively correlated with PBK, KIF2C, NUF2, RAD51AP1, and DEPDC1 expression levels. In conclusion, our results suggest that PBK, KIF2C, NUF2, KIF20A, RAD51AP1 and DEPDC1 are all potential biomarkers for ESCC diagnosis and prognosis may also be potential therapeutic targets for ESCC.

Aberrant DNA Methylation in Esophageal Squamous Cell Carcinoma: Biological and Clinical Implications

Almost all cancer cells possess multiple epigenetic abnormalities, which cooperate with genetic alterations to enable the acquisition of cancer hallmarks during tumorigenesis. As the most frequently found epigenetic change in human cancers, aberrant DNA methylation manifests at two major forms: global genomic DNA hypomethylation and locus-specific promoter region hypermethylation. It has been recognized as a critical contributor to esophageal squamous cell carcinoma (ESCC) malignant transformation. In ESCC, DNA methylation alterations affect genes involved in cell cycle regulation, DNA damage repair, and cancer-related signaling pathways. Aberrant DNA methylation patterns occur not only in ESCC tumors but also in precursor lesions. It adds another layer of complexity to the ESCC heterogeneity and may serve as early diagnostic, prognostic, and chemo-sensitive markers. Characterization of the DNA methylome in ESCC could help better understand its pathogenesis and develop improved therapies. We herein summarize the current research and knowledge about DNA methylation in ESCC and its clinical significance in diagnosis, prognosis, and treatment.

A comprehensive comparison of residue-level methylation levels with the regression-based gene-level methylation estimations by ReGear

MOTIVATION

DNA methylation is a biological process impacting the gene functions without changing the underlying DNA sequence. The DNA methylation machinery usually attaches methyl groups to some specific cytosine residues, which modify the chromatin architectures. Such modifications in the promoter regions will inactivate some tumor-suppressor genes. DNA methylation within the coding region may significantly reduce the transcription elongation efficiency. The gene function may be tuned through some cytosines are methylated.

METHODS

This study hypothesizes that the overall methylation level across a gene may have a better association with the sample labels like diseases than the methylations of individual cytosines. The gene methylation level is formulated as a regression model using the methylation levels of all the cytosines within this gene. A comprehensive evaluation of various feature selection algorithms and classification algorithms is carried out between the gene-level and residue-level methylation levels.

RESULTS

A comprehensive evaluation was conducted to compare the gene and cytosine methylation levels for their associations with the sample labels and classification performances. The unsupervised clustering was also improved using the gene methylation levels. Some genes demonstrated statistically significant associations with the class label, even when no residue-level methylation features have statistically significant associations with the class label. So in summary, the trained gene methylation levels improved various methylome-based machine learning models. Both methodology development of regression algorithms and experimental validation of the gene-level methylation biomarkers are worth of further investigations in the future studies. The source code, example data files and manual are available at http://www.healthinformaticslab.org/supp/.

SCTR hypermethylation is a diagnostic biomarker in colorectal cancer

Diagnostic markers for both colorectal cancer (CRC) and its precursor lesions are lacking. Although aberrant methylation of the secretin receptor (SCTR) gene was observed in CRC, the diagnostic performance has not been evaluated. Therefore, this study aimed to assess and verify the diagnostic value of SCTR methylation of CRC and its precursor lesions through integrating the largest methylation data. The diagnostic performance of SCTR methylation was analyzed in the discovery set from The Cancer Genome Atlas (TCGA) CRC methylation data (N = 440), and verified in a large-scale test set (N = 938) from the Gene Expression Omnibus (GEO). Targeted bisulfite sequencing analysis was developed and applied to detect the methylation status of SCTR in our independent validation set (N = 374). Our findings revealed that the SCTR gene was frequently hypermethylated at its CpG islands in CRC. In the TCGA discovery set, the diagnostic score was constructed using 4 CpG sites (cg01013590, cg20505223, cg07176264, and cg26009192) and achieved high diagnostic performance (area under the ROC curve [AUC] = 0.964). In the GEO test set, the diagnostic score had robust diagnostic ability to distinguish CRC (AUC = 0.948) and its precursor lesions (AUC = 0.954) from normal samples. Moreover, hypermethylation of the SCTR gene was also found in cell-free DNA samples collected from CRC patients, but not in those from healthy controls. In the validation set, consistent results were observed using the targeted bisulfite sequencing array. Our study highlights that hypermethylation at CpG islands of the SCTR gene is a potential diagnostic biomarker in CRCs and its precursor lesions.

Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network

Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.

The Pathologic and Molecular Landscape of Esophageal Squamous Cell Carcinogenesis

Esophageal squamous cell carcinoma represents the most common histotype of epithelial neoplasm occurring within esophageal mucosa worldwide. Despite the comprehensive molecular characterization of this entity, to date no significant targeted therapy has been introduced into clinical practice. In this review, we describe the molecular landscape of esophageal squamous cell carcinoma based on the most recent literature. Moreover, we focus on other rare variants and on the relationship with head and neck squamous cell carcinomas.

Epigenetic silencing of ZNF132 mediated by methylation-sensitive Sp1 binding promotes cancer progression in esophageal squamous cell carcinoma

Epigenetic alteration of tumor suppression gene is one of the most significant indicators in human esophageal squamous cell carcinoma (ESCC). In this study, we identified a novel ESCC hypermethylation biomarker ZNF132 by integrative computational analysis to comprehensive genome-wide DNA methylation microarray dataset. We validated the hypermethylation status of ZNF132 in 91 Chinese Han ESCC patients and adjacent normal tissues with methylation target bisulfite sequencing (MTBS) assay. Meanwhile, ZNF132 gene silencing mediated by hypermethylation was confirmed in both solid tissues and cancer cell lines. What is more, we found that in vitro overexpression of ZNF132 in ESCC cells could significantly reduce the abilities of the cell in growth, migration and invasion, and tumorigenicity of cells in a nude mouse model. We validated the Sp1-binding site in the ZNF132 promoter region with chromatin immunoprecipitation assay and demonstrated that the hypermethylation status could reduce the Sp1 transcript factor activity. Our results suggest that ZNF132 plays an important role in the development of ESCC as a tumor suppressor gene and support the underlying mechanism caused by the DNA hypermethylation-mediated Sp1-binding decay and gene silencing.

Expression of NF-κB and TLR-4 is associated with the occurrence, progression and prognosis of esophageal squamous cell carcinoma

BACKGROUND

As a crucial regulator of inflammation and immune responses, the NF-κB transcription factor regulates the transcription of target genes which are closely related with cell survival, cell proliferation, apoptosis, invasion and metastasis. The toll-like receptor (TLR) family is critical in aiding pathogen recognition and the subsequent activation of innate immunity. TLR4, one of the TLR family members, is the main receptor of innate immunity and functions to identify pathogens. However, the significance of the expressions of both NF-κB and TLR-4 in the occurrence, development and prognosis of esophageal squamous cell carcinoma (ESCC) remains unclear.

METHODS

NF-κB and TLR-4 expressions were analyzed in tissue microarrays made up of low grade intraepithelial neoplasia (LGIN), high grade intraepithelial neoplasia (HGIN), early-stage ESCC, advanced ESCC tissue samples, as well as normal healthy esophageal mucosa. Chi-squared tests and Kaplan-Meier plots were utilized to determine the prognostic values of the NF-κB and TLR-4 expressions.

RESULTS

We discovered that NF-κB and TLR-4 expressions were progressively increasing from normal esophageal mucosa, LGIN, HGIN and were highest in early-stage ESCC. Interestingly, TLR-4 and NF-κB expressions were attenuated in advanced ESCC tissue. Suppressed expressions of TLR-4 and NF-κB correlated to increasingly malignant ESCC. Additionally, there was a positive correlation between NF-κB and TLR-4 expressions.

CONCLUSION

TLR-4 and NF-κB are associated with the occurrence, development and malignant evolution of ESCC.