Methylation-driven Genes Research Articles

DNA methylation-driven gene FAM3D promotes colorectal cancer growth via the ATF4-SESN2-mTORC1 pathway.

Globally, colorectal cancer (CRC) is the malignant tumor with the highest mortality rate after lung cancer. Abnormal DNA methylation drives dysregulated gene expression, thereby promoting CRC progression and leading to poor prognosis. We identified a 3-CpG methylation signature that is independently associated with CRC prognosis. The model consists of three methylation-driven genes: FAM3 Metabolism Regulating Signaling Molecule D (FAM3D), DAPP1, and PIGR. However, the prognostic significance, biological function, and related mechanisms of the individual methylation-driven gene FAM3D in CRC have not been studied. Here, we discovered that FAM3D expression was reduced in CRC tissues and cells, and that high methylation and low expression of FAM3D were independent prognostic risk factors for CRC. In addition, FAM3D promoted the growth and movement of CRC cells in vitro and the proliferation in nude mice, mainly by inhibiting ATF4 transcription and downregulating SESN2 expression, and ultimately activating mTORC1. Furthermore, FAM3D resulted in reduced sensitivity of CRC cells to oxaliplatin, cisplatin, and 5-fluorouracil. Our study showed that FAM3D activates the mTORC1 pathway through the ATF4-SESN2 axis and promotes the malignant progression of CRC, which contributes to predict CRC prognosis and guide individualized treatment.

Methylation and transcriptome analyses construct a prognostic model and reveal the suppressor role of VMO1 in lung adenocarcinoma

BackgroundDNA methylation is an important epigenetic mechanism of gene regulation. The aberrant DNA methylation has been found to play an important role in the initiation and progression of tumors. ResultsTranscriptome and DNA methylation data of lung adenocarcinoma (LUAD) patients were co-analyzed and 95 methylation-driven genes (MDGs) was found in relation to LUAD. A prognostic model based on 3 MDGs (GMNN, SPINK2 and VMO1) was constructed by Univariate and Multivariate cox regression analyses. The risk score generated from the prognostic model could be used to classify LUAD patients into high and low risk groups. Furthermore, it was found that the risk score was associated with tumor microenvironment (TME) and clinical characteristics (survival status and T stage) of patients. Interestingly, we identified and validated that the patients in the low-risk group responded better to immunotherapy treatment. Then, a nomogram model based on the risk score and clinical characteristics was established which showed significant prediction value. The down-regulation and hypermethylation levels of vitelline membrane outer layer protein 1 homolog (VMO1) were verified in paired LUAD tumor and non-tumor tissues by pyrosequencing assay and RT-qPCR. Furthermore, MTT, migration and wound healing assays were performed with lentivector-mediated ectopic over-expression and 5-Aza-dC demethylation followed by siRNA rescue experiments to investigate the role of VMO1 in LUAD cells. Our results indicated that VMO1 could inhibit proliferation and migration of A549 and NCI-H1299 cells. ConclusionsIn summary, our experiments constructed a prognostic model with high capacity for risk prediction in LUAD patients. VMO1 had a malignant suppressor role in LUAD cells. The correlation between risk score and TME might elucidate a potential mechanism of oncogenesis and provide an avenue for further therapeutic targets.

Integrated analysis of methylation and transcriptome identifies a novel risk model for diagnosis, prognosis, and immune characteristics in head and neck squamous cell carcinoma.

DNA methylation is an important epigenetic modification that plays a crucial role in the development and progression of various tumors. However, the association between methylation‑driven genes and diagnosis, prognosis, and immune characteristics of head and neck squamous cell carcinoma (HNSCC) remains unclear. We obtained transcriptome, methylation, and clinical data from HNSCC patients in TCGA database, and used MethylMix algorithm to identify methylation-driven genes. A methylation driven gene-related risk model was constructed using Lasso regression analysis, and validated using data from GEO database. Immune infiltration and immune function analysis of the expression profiles were conducted using ssGSEA. Differences in immune checkpoint-related genes were analyzed, and the efficacy of immunotherapy was evaluated using TCIA database. Finally, a series of cell functional experiments were conducted to validate the results. Five methylation-driven genes were identified and utilized to construct a prognostic risk model. Based on the median risk score, all patients were categorized into high-risk and low-risk groups. The K-M analysis revealed that patients in the high-risk group have a worse prognosis. Additionally, the risk model demonstrated better prognostic predictive value as indicated by ROC analysis. GSEA enrichment analysis indicated that gene sets in the high and low-risk groups were primarily enriched in pathways associated with tumor immunity and metabolism. Our subsequent investigations showed that high-risk patients exhibited more immunosuppressive phenotypes, while low-risk patients were more likely to respond positively to immunotherapy. These findings of our research have the potential to improve patient stratification, guide treatment decisions, and advance the development of personalized therapies for HNSCC.

RBMS1 reflects a distinct microenvironment and promotes tumor progression in ocular melanoma

Ocular melanoma, including uveal melanoma (UM) and conjunctival melanoma (CM), is the most common ocular cancer among adults with a high rate of recurrence and poor prognosis. Loss of epigenetic homeostasis disturbed gene expression patterns, resulting in oncogenesis. Herein, we comprehensively analyzed the DNA methylation, transcriptome profiles, and corresponding clinical information of UM patients through multiple machine-learning algorithms, finding that a methylation-driven gene RBMS1 was correlated with poor clinical outcomes of UM patients. RNA-seq and single-cell RNA-seq analyses revealed that RBMS1 reflected diverse tumor microenvironments, where high RBMS1 expression marked an immune active TME. Furthermore, we found that tumor cells were identified to have the higher communication probability in RBMS1+ state. The functional enrichment analysis revealed that RBMS1 was associated with pigment granule and melanosome, participating in cell proliferation as well as apoptotic signaling pathway. Biological experiments were performed and demonstrated that the silencing of RBMS1 inhibited ocular melanoma proliferation and promoted apoptosis. Our study highlighted that RBMS1 reflects a distinct microenvironment and promotes tumor progression in ocular melanoma, contributing to the therapeutic customization and clinical decision-making.

Analysis of methylation-driven genes for predicting the prognosis of patients with oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy that is characterized by early distant metastasis and poor prognosis. DNA methylation plays an important role in the etiology and pathogenesis of OSCC. This study aimed to identify methylation-driven genes through bioinformatics analysis as potential biomarkers for early diagnosis and prognostic assessment of OSCC. Methylation data, RNA sequencing (RNA-seq) data and clinical prognosis information of OSCC patients were retrieved from The Cancer Genome Atlas (TCGA) database. The R packages MethylMix were employed to analyze the correlation between methylation status and corresponding gene expression in tumor and normal tissues to obtain methylation-driven genes. Univariate Cox regression analysis was developed to further screen methylation-driven genes associated with the prognosis of OSCC patients. Subsequently, multivariate Cox regression analysis was utilized to construct a linear prognostic risk prediction model. Furthermore, a combined survival analysis integrating methylation and gene expression was performed to investigate the prognostic value. A total of 374 differentially expressed methylation-driven genes were identified. Seven methylation-driven genes (BST2, KRT15, ZNF134, NT5E, GSTA7P, NAPRT, and GOLPH3L) were found to be significantly associated with patient prognosis. Additionally, four methylation-driven genes (BST2, KRT15, ZNF134 and NAPRT) were used to construct a linear prognostic risk prediction model for OSCC patients. Furthermore, a combined Kaplan-Meier survival analysis revealed that three methylation-driven genes (ZKSCAN7, MFF, ZNF134) alone can be used as independent prognostic markers or drug targets. Our findings facilitate a better understanding of molecular mechanisms of OSCC and provide potential biomarkers of early diagnosis, precision treatment and prognosis evaluation.

Identification and Validation of Tumor Microenvironment-Associated Signature in Clear-Cell Renal Cell Carcinoma through Integration of DNA Methylation and Gene Expression.

The tumor microenvironment (TME) is crucial in tumor development, metastasis, and response to immunotherapy. DNA methylation can regulate the TME without altering the DNA sequence. However, research on the methylation-driven TME in clear-cell renal cell carcinoma (ccRCC) is still lacking. In this study, integrated DNA methylation and RNA-seq data were used to explore methylation-driven genes (MDGs). Immune scores were calculated using the ESTIMATE, which was employed to identify TME-related genes. A new signature connected with methylation-regulated TME using univariate, multivariate Cox regression and LASSO regression analyses was developed. This signature consists of four TME-MDGs, including AJAP1, HOXB9, MYH14, and SLC6A19, which exhibit high methylation and low expression in tumors. Validation was performed using qRT-PCR which confirmed their downregulation in ccRCC clinical samples. Additionally, the signature demonstrated stable predictive performance in different subtypes of ccRCC. Risk scores are positively correlated with TMN stages, immune cell infiltration, tumor mutation burden, and adverse outcomes of immunotherapy. Interestingly, the expression of four TME-MDGs are highly correlated with the sensitivity of first-line drugs in ccRCC treatment, especially pazopanib. Molecular docking indicates a high affinity binding between the proteins and pazopanib. In summary, our study elucidates the comprehensive role of methylation-driven TME in ccRCC, aiding in identifying patients sensitive to immunotherapy and targeted therapy, and providing new therapeutic targets for ccRCC treatment.

A Prognostic Methylation-Driven Two-Gene Signature in Medulloblastoma.

Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that one-third of patients will not achieve long-term survival. Conventional prognostic parameters have limited and unreliable correlations with MB outcome, presenting a major challenge for patients' clinical improvement. Acknowledging this issue, our aim was to build a gene signature and evaluate its potential as a new prognostic model for patients with the disease. In this study, we used six datasets totaling 1679 samples including RNA gene expression and DNA methylation data from primary MB as well as control samples from healthy cerebellum. We identified methylation-driven genes (MDGs) in MB, genes whose expression is correlated with their methylation. We employed LASSO regression, incorporating the MDGs as a parameter to develop the prognostic model. Through this approach, we derived a two-gene signature (GS-2) of candidate prognostic biomarkers for MB (CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall survival (OS) with Kaplan-Meier analysis. We evaluated its robustness and accuracy with receiver operating characteristic curves predicting OS at 1, 3, and 5 years in multiple independent datasets. The GS-2 showed highly significant results as an independent prognostic biomarker compared to traditional MB markers. The methylation-regulated GS-2 risk score model can effectively classify patients with MB into high and low-risk, reinforcing the importance of this epigenetic modification in the disease. Such genes stand out as promising prognostic biomarkers with potential application for MB treatment.

Identification of methylation driver genes for predicting the prognosis of pancreatic cancer patients based on whole-genome DNA methylation sequencing technology

This study was based on the use of whole-genome DNA methylation sequencing technology to identify DNA methylation biomarkers in tumor tissue that can predict the prognosis of patients with pancreatic cancer (PCa). TCGA database was used to download PCa-related DNA methylation and transcriptome atlas data. Methylation driver genes (MDGs) were obtained using the MethylMix package. Candidate genes in the MDGs were screened for prognostic relevance to PCa patients by univariate Cox analysis, and a prognostic risk score model was constructed based on the key MDGs. ROC curve analysis was performed to assess the accuracy of the prognostic risk score model. The effects of PIK3C2B knockdown on malignant phenotypes of PCa cells were investigated in vitro. A total of 2737 differentially expressed genes were identified, with 649 upregulated and 2088 downregulated, using 178 PCa samples and 171 normal samples. MethylMix was employed to identify 71 methylation-driven genes (47 hypermethylated and 24 hypomethylated) from 185 TCGA PCa samples. Cox regression analyses identified eight key MDGs (LEF1, ZIC3, VAV3, TBC1D4, FABP4, MAP3K5, PIK3C2B, IGF1R) associated with prognosis in PCa. Seven of them were hypermethylated, while PIK3C2B was hypomethylated. A prognostic risk prediction model was constructed based on the eight key MDGs, which was found to accurately predict the prognosis of PCa patients. In addition, the malignant phenotypes of PANC-1 cells were decreased after the knockdown of PIK3C2B. Therefore, the prognostic risk prediction model based on the eight key MDGs could accurately predict the prognosis of PCa patients.

Methylation-driven mechanisms of allergic rhinitis during pollen and non-pollen seasons using integrated bioinformatics analysis.

Allergic rhinitis (AR) is a widespread allergic airway disease that results from a complex interplay between genetic and environmental factors and affects approximately 10%-40% of the global population. Pollen is a common allergen, and exposure to pollen can cause epigenetic changes. However, the mechanism underlying pollen-induced DNA methylation changes and their potential effects on the allergic march are still unclear. The purpose of this study was to explore the methylation-driven mechanisms of AR during the pollen and non-pollen seasons using bioinformatics analysis and to investigate their relationship with asthma. We downloaded DNA methylation and gene expression data from the GEO database (GSE50387: GSE50222, GSE50101) and identified differentially methylated positions (DMPs) and differentially expressed genes (DEGs) during the pollen and non-pollen seasons using the CHAMP and limma packages. Through correlation analysis, we identified methylation-driven genes and performed pathway enrichment analysis to annotate their functions. We incorporated external data on AR combined with asthma (GSE101720) for analysis to identify key CpGs that promote the transformation of AR to asthma. We also utilized external data on olive pollen allergy (GSE54522) for analysis to validate the methylation-driven genes. Weighted correlation network analysis (WGCNA) was employed to identify gene modules significantly correlated with pollen allergy. We extracted genes related to the key methylation-driven gene ZNF667-AS1 from the significant module and performed pathway intelligent clustering using KOBAS-i. We also utilized gene set enrichment analysis to explore the potential function of ZNF667-AS1. We identified 20 and 24 CpG-Gene pairings during the pollen and non-pollen seasons. After incorporating external data from GSE101720, we found that ZNF667-AS1 is a key gene that may facilitate the transformation of AR into asthma during the pollen season. This finding was further validated in another external dataset, GSE54522, which is associated with pollen allergy. WGCNA identified 17 modules, among which the blue module showed significant correlation with allergies. ZNF667-AS1 was located in the blue module. We performed pathway analysis on the genes correlated with ZNF667-AS1 extracted from the blue module and identified a prominent cluster of pathways in the KOBAS-i results, including Toll-like receptor (TLR) family, MyD88, MAPK, and oxidative stress. Gene set enrichment analysis around cg05508084 (paired with ZNF667-AS1) also indicated its potential involvement in initiating and modulating allergic inflammation from the perspective of TLR and MAPK signaling. We identified methylation-driven genes and their related pathways during the pollen and non-pollen seasons in patients with AR and identified key CpGs that promote the transformation of AR into asthma due to pollen exposure. This study provides new insights into the underlying molecular mechanisms of the transformation of AR to asthma.

Integrative methylome and transcriptome analysis reveals epigenetic regulation of Fusobacterium nucleatum in laryngeal cancer.

The aetiological mechanisms of Fusobacterium nucleatum in laryngeal cancer remain unclear. This study aimed to reveal the epigenetic signature induced by F. nucleatum in laryngeal squamous cell carcinoma (LSCC). Combined analysis of methylome and transcriptome data was performed to address the functional role of F. nucleatum in laryngeal cancer. Twenty-nine differentially expressed methylation-driven genes were identified by mapping the methylation levels of significant differential methylation sites to the expression levels of related genes. The combined analysis revealed that F. nucleatum promoted Janus kinase 3 (JAK3) gene expression in LSCC. Further validation found decreased methylation and elevated expression of JAK3 in the F. nucleatum-treated LSCC cell group; F. nucleatum abundance and JAK3 gene expression had a positive correlation in tumour tissues. This analysis provides a novel understanding of the impact of F. nucleatum in the methylome and transcriptome of laryngeal cancer. Identification of these epigenetic regulatory mechanisms opens up new avenues for mechanistic studies to explore novel therapeutic strategies.

Expression of IZUMO2 in colorectal cancer in association with clinicopathological features

IZUMO2 belongs to the testis-expressed IZUMO family of proteins, which are characterized by an N-terminal IZUMO domain. Based on integrated analysis of expression profiles and matched DNA methylation data from a public database, IZUMO2 represents a prognosis-related methylation-driven gene in colorectal cancer. However, it remains unclear whether IZUMO2 protein expression is suppressed or overexpressed in colorectal cancer cells. In this study, we aimed to elucidate the expression of the IZUMO2 protein in colorectal cancer, with a focus on the clinicopathological features. Sixty-four colorectal cancer tissue specimens were immunohistochemically stained using specific antibodies against IZUMO2. IZUMO2 immunoreactivity was detected at the invasion front in 30 of the 64 colorectal cancer samples. Kaplan–Meier analysis demonstrated that patients with IZUMO2 immunoreactivity had a relatively shorter overall and progression-free survival (log-rank test, P = 0.046 and 0.019, respectively). IZUMO2 immunoreactivity served as an independent factor predictive of poor progression-free survival in colorectal cancer (P = 0.025) as determined via the Cox proportional hazard regression model. Moreover, IZUMO2 immunoreactivity represented an independent factor for poor overall survival (P = 0.035) and progression-free survival (P = 0.013) in patients with colon cancer. The present findings suggest that IZUMO2 is expressed in many colorectal cancers, especially at the cancer invasion front, and may represent an indicator of poor prognosis in colorectal cancer.

Comprehensive Analysis Based on the TCGA Database Identified SCIN as a Key DNA Methylation-Driver Gene in Epstein-Barr Virus-Associated Gastric Cancer.

An important feature of EBV-associated gastric cancer (EBVaGC) is extensive methylation of viral and host genomes. This study aims to analyze DNA methylation-driven genes (DMDG) in EBVaGC through bioinformatics methods, providing an important bioinformatics basis for the differential diagnosis and treatment of potential methylation biomarkers in EBVaGC. We downloaded the mRNA expression profiles and methylation datasets of EBVaGC and EBV-negative gastric cancer (EBVnGC) through the TCGA database to screen methylated-differentially expressed genes (MDEGs). DNA methylation-driver genes were identified based on MethylMix algorithm and key genes were further identified by LASSO regression and Random Forest algorithm. Then, we performed gene enrichment analysis for key genes and validated them by GEO database. Gene expression differences in EBVaGC and EBVnGC cell lines was determined by quantitative real-time PCR (qRT-PCR) and western blotting and in GT38 cell and SNU719 cell which all treated by 5-Aza-CdR. Finally, the effect of key gene on the migration and proliferation capacity of EBVaGC cells was determined by Transwells assay and Cell counting Kit-8 (CCK-8) assay. We obtained a total of 687 hypermethylation-low expression genes (Hyper-LGs) and further obtained 53 DNA methylation-driver genes based on the MethylMix algorithm. A total of six key genes (SCIN, ETNK2, PCDH20, PPP1R3C, MATN2, and HOXA5) were identified by LASSO regression and Random Forest algorithm. Among them, SCIN expression was significantly lower in EBVaGC cell lines than in EBVnGC cell lines, and its expression was significantly recovered in EBVaGC cell lines treated with 5-Aza-CdR. Overexpression of SCIN can promote the proliferation and migration capacity of EBVaGC cells. Our study will provide some bioinformatics basis for the study of EBVaGC-related methylation. SCIN may be used as potential methylation biomarkers for the diagnosis and treatment of EBVaGC.

Identification of colon cancer subtypes based on multi-omics data-construction of methylation markers for immunotherapy.

Being the most widely used biomarker for immunotherapy, the microsatellite status has limitations in identifying all patients who benefit in clinical practice. It is essential to identify additional biomarkers to guide immunotherapy. Aberrant DNA methylation is consistently associated with changes in the anti-tumor immune response, which can promote tumor progression. This study aims to explore immunotherapy biomarkers for colon cancers from the perspective of DNA methylation. The related data (RNA sequencing data and DNA methylation data) were obtained from The Cancer Genome Atlas (TCGA) and UCSC XENA database. Methylation-driven genes (MDGs) were identified through the Pearson correlation analysis. Unsupervised consensus clustering was conducted using these MDGs to identify distinct clusters of colon cancers. Subsequently, we evaluated the immune status and predicted the efficacy of immunotherapy by tumor immune dysfunction and exclusion (Tide) score. Finally, The Quantitative Differentially Methylated Regions (QDMR) software was used to identify the specific DNA methylation markers within particular clusters. A total of 282 MDGs were identified by integrating the DNA methylation and RNA-seq data. Consensus clustering using the K-means algorithm revealed that the optimal number of clusters was 4. It was revealed that the composition of the tumor immune microenvironment (TIME) in Cluster 1 was significantly different from others, and it exhibited a higher level of tumor mutation burdens (TMB) and stronger anti-tumor immune activity. Furthermore, we identified three specific hypermethylation genes that defined Cluster 1 (PCDH20, APCDD1, COCH). Receiver operating characteristic (ROC) curves demonstrated that these specific markers could effectively distinguish Cluster 1 from other clusters, with an AUC of 0.947 (95% CI 0.903-0.990). Finally, we selected clinical samples for immunohistochemical validation. In conclusion, through the analysis of DNA methylation, consensus clustering of colon cancer could effectively identify the cluster that benefit from immunotherapy along with specific methylation biomarkers.

A prognostic model based on DNA methylation-related gene expression for predicting overall survival in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) continues to increase in morbidity and mortality among all types of cancer. DNA methylation, an important epigenetic modification, is associated with cancer occurrence and progression. The objective of this study was to establish a model based on DNA methylation risk scores for identifying new potential therapeutic targets in HCC and preventing cancer progression. Transcriptomic, clinical, and DNA methylation data on 374 tumor tissues and 50 adjacent normal tissues were downloaded from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma database. The gene expression profiles of the GSE54236 liver cancer dataset, which contains data on 161 liver tissue samples, were obtained from the Gene Expression Omnibus database. We analyzed the relationship between DNA methylation and gene expression levels after identifying the differentially methylated and expressed genes. Then, we developed and validated a risk score model based on the DNA methylation-driven genes. A tissue array consisting of 30 human hepatocellular carcinoma samples and adjacent normal tissues was used to assess the protein and mRNA expression levels of the marker genes by immunohistochemistry and qRT-PCR, respectively. Three methylation-related differential genes were identified in our study: GLS, MEX3B, and GNA14. The results revealed that their DNA methylation levels were negatively correlated with local gene expression regulation. The gene methylation levels correlated strongly with the prognosis of patients with liver cancer. This was confirmed by qRT-PCR and immunohistochemical verification of the expression of these genes or proteins in tumors and adjacent tissues. These results revealed the relationship between the level of relevant gene methylation and the prognosis of patients with liver cancer as well as the underlying cellular and biological mechanisms. This allows our gene signature to provide more accurate and appropriate predictions for clinical applications. Through bioinformatics analysis and experimental validation, we obtained three DNA methylation marker: GLS, MEX3B, and GNA14. This helps to predict the prognosis and may be a potential therapeutic target for HCC patients.

DNA methylation and gene expression profiling reveal potential association of retinol metabolism related genes with hepatocellular carcinoma development.

Aberrant DNA methylation patterns play a critical role in the development of hepatocellular carcinoma (HCC). However, the molecular mechanisms associated with these aberrantly methylated genes remain unclear. This study aimed to comprehensively investigate the methylation-driven gene expression alterations in HCC using a multi-omics dataset. Whole genome bisulfite sequencing (WGBS) and RNA sequencing (RNA-seq) techniques were used to assess the methylation and gene expression profiles of HCC tissues (HCCs) and normal adjacent tissues (NATs). The candidate genes' potential function was further investigated using single-cell RNA sequencing (scRNA seq) data. We observed widespread hypomethylation in HCCs compared to NATs. Methylation levels in distinct genomic regions exhibited significant differences between HCCs and NATs. We identified 247,632 differentially methylated regions (DMRs) and 4,926 differentially expressed genes (DEGs) between HCCs and NATs. Integrated analysis of DNA methylation and RNA-seq data identified 987 methylation-driven candidate genes, with 970 showing upregulation and 17 showing downregulation. Four genes involved in the retinol metabolic pathway, namely ADH1A, CYP2A6, CYP2C8, and CYP2C19, were identified as hyper-downregulated genes. Their expression levels could stratify HCCs into three subgroups with distinct survival outcomes, immune cell infiltration, and tumor microenvironments. Validation of these findings in an independent dataset yielded similar outcomes, confirming the high concordance and potential prognostic value of these genes. ScRNA seq data revealed the low expression of these genes in immune cells, emphasizing their role in promoting malignant cell proliferation and migration. In conclusion, this study provides insights into the molecular characteristics of HCC, revealing the involvement of retinol metabolism-related genes in the development and progression of HCC. These findings have implications for HCC diagnosis, prognosis prediction, and the development of therapeutic targets.

Epigenetic signatures and prognostic biomarkers analysis of methylation-driven genes in uterine endometrial carcinosarcoma

Epigenetic signatures and prognostic biomarkers analysis of methylation-driven genes in uterine endometrial carcinosarcoma

Identifying a Risk Signature of Methylation-Driven Genes as a Predictor of Survival Outcome for Colon Cancer Patients.

Aberrant expression of gene is driven by its promoter methylation and is the key molecular basis of carcinogenic processes. This study aimed at identifying a risk signature of methylation-driven (MD) genes and evaluating its prognostic value for colon cancer (CC) patients. The expression profiles of methylation and mRNA in CC samples were obtained from the TCGA database, and the MethylMix algorithm was used to identify MD genes. The relationships between their expression levels and overall survival (OS) of CC patients were analyzed, and a prognostic signature of MD genes was established. The risk score of gene signature was calculated, and the median was used to divide all patients into high (H) and low (L) risk groups. The prognostic value of gene signature was tested by the TCGA cohort and an independent validation cohort (GSE17538 dataset). In total, 69 MD genes were identified, and 7 were associated with OS of CC patients. Ultimately, 4 (TWIST1, LDOC1, EPHX3, and STC2) were screened out to establish a risk signature. The H-risk patients (>0.923) had a worse OS than L-risk patients (≤0.923) in both the TCGA (5-year cumulative survival: 52.9% vs 72.0%, P=0.005) and GSE17538 cohort (49.4% vs 69.3%, P=0.004). The AUC values of MD genes signature for the prediction of 3- and 5-year OS were 0.648 and 0.643 in the TCGA dataset and 0.634 and 0.624 in the GSE17538 dataset, respectively. The risk signature of four MD genes was identified as an independent predictor of OS for CC patients (HR for TCGA dataset: 2.071, 95% CI=1.196-3.586, P=0.009; HR for GSE17538 dataset: 2.021, 95% CI=1.290-3.166, P=0.002). The risk signature of four MD genes might be a useful prognostic tool and help doctors improve the clinical management of CC patients.

Abnormal DNA methylation analysis of leucine-rich glioma-inactivated 1 antibody encephalitis reveals novel methylation-driven genes related to prognostic and clinical features

BackgroundAberrant DNA methylation occurs commonly during pathogenesis of neuroimmunological diseases and is of clinical value in various encephalitis subtypes. However, knowledge of the impact of DNA methylation changes on pathogenesis of leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis remains limited.MethodsA total of 44 cytokines and 10 immune checkpoint moleculars (ICMs) in the serum of patients with LGI1 encephalitis and healthy donors (HDs) were measured to evaluate the association of them with clinical parameters. Genome-wide DNA methylation profiles were performed in peripheral blood mononuclear cell (PBMC) from LGI1 encephalitis patients and HDs using reduced representation bisulfite sequencing (RRBS) and validated for the methylation status by pyrosequencing. MicroRNA profiles were acquired in serum exosome by small RNA sequencing. Targeted cytokines expression was assessed at the presence or absence of miR-2467-5p in PBMCs and the culture media, and the binding of miR-2467-5p and its targeted genes was validated by luciferase assay.ResultsThere existed significant difference in 22 cytokines/chemokines and 6 ICMs between LGI1 encephalitis patients and HDs. Decreased PDCD1 with increased ICAM1 could predict unfavorable prognosis in one-year follow-up for LGI1 encephalitis patients. Fifteen of cytokines/chemokines and ICMs presented DNA-methylated changes in the promoter and gene body using RRBS in which five were verified as methylation status by pyrosequencing, and the methylation level of CSF3, CCL2, and ICAM1 was conversely associated with their expression in PBMCs. By combining RRBS data with exosome-derived microRNA sequencing, we found that hypomethylated-driven hsa-miR-2467-5p presented elevated expression in serum exosomes and PBMCs in LGI1 encephalitis. Mechanically, miR-2467-5p significantly induced reduced expression of CSF3 and PDCD1 by binding with their 3`UTR while enhanced CCL15 expression, but not significantly correlated with peripheral blood CD19 + B cell proportion of LGI1 encephalitis patients.ConclusionsOur results provided convincing evidence for DNA methylation changes, microRNA profiles in serum exosome for LGI1 encephalitis, and we also identified several novel cytokines related to clinical features in which some represented epigenetic modification of methylated-driven pattern and microRNA modulation. Our study contributed to develop treatment for epigenetic pathogenesis in LGI1 encephalitis.

Development of a prognostic model for personalized prediction of colon adenocarcinoma (COAD) patient outcomes using methylation-driven genes.

The objective of this study was to identify methylation-driven genes and explore their prognostic value in colon adenocarcinoma (COAD). The Cancer Genome Atlas (TCGA) database was used to acquire collated COAD transcriptome gene expression matrix (containing 59,427 transcripts), transcriptome gene methylation level matrix (containing 29,602 methylated modified genes), which included 517 samples containing 41 samples of normal tissue (NT) &476 samples of COAD, and patient clinical information files (including patient survival time, survival status, age, gender and tumor stage, etc.), for all COAD samples. A total of 9807 differentially expressed genes (DEGs) were obtained by DEG analysis of the COAD transcriptional expression matrix, of which 5874 were up-regulated and 3933 were down-regulated. And 46 methylation-driven DEGs (MD-DEGs) in COAD were obtained by DEG analysis, differential analysis of gene methylation levels, and correlation analysis between them. Next, three prognostic associated MD-DEGs (PMD-DEGs) (IDUA, ZBTB18 and C5orf38) were identified by Cox regression analysis, and a prognostic model composed of the three PMD-DEGs was constructed by least absolute shrinkage and selection operator (LASSO) regression analysis and cross-validation analysis. In addition, survival analysis, the receiver operating characteristics (ROC) curve analysis and independent prognostic analysis were used to evaluate and verify that the prognostic model we constructed could accurately and independently predict the prognosis of COAD patients. Finally, we constructed a nomogram based on the prognosis model to accurately and personalized predict the survival prognosis of COAD patients. In conclusion, we identified the methylation driver gene of COAD and constructed a prognostic model and nomogram to personalized predict the prognosis of patients, which opened a new prospect for accurate diagnosis and treatment in clinical practice.

Identification and validation of DNA methylation-driven gene PCDHB4 as a novel tumor suppressor for glioblastoma diagnosis and prognosis.

Aberrant DNA methylation is a critical regulator of gene expression in the development and progression of glioblastoma (GBM). However, the impact of methylation-driven gene PCDHB4 changes on GBM occurrence and progression remains unclear. Therefore, this study aimed to identify the PCDHB4 gene for early diagnosis and prognostic evaluation and clarify its functional role in GBM. Methylation-driven gene PCDHB4 was selected for GBM using the multi-omics integration method based on publicly available data sets. The diagnostic capabilities of PCDHB4 methylation and 5-hydroxymethylcytosines were validated in tissue and blood cell-free DNA (cfDNA) samples, respectively. Combined survival analysis of PCDHB4 methylation and immune infiltration cells evaluated the prognostic predictive performance of GBM patients. We identified that the PCDHB4 gene achieved high discriminative capabilities for GBM and normal tissues with an area under the curve value of 0.941. PCDHB4 hypermethylation was observed in cfDNA blood samples from GBM patients. Compared with GBM patients with PCDHB4 hypermethylation level, patients with PCDHB4 hypomethylation level had significantly poorer overall survival (p = 0.035). In addition, GBM patients with PCDHB4 hypermethylation and high infiltration of CD4+ T cell activation level had a favorable survival (p = 0.026). Moreover, we demonstrated that mRNA expression of PCDHB4 was downregulated in GBM tissues and upregulated in GBM cell lines with PCDHB4 demethylation, and PCDHB4 overexpression inhibited GBM cell proliferation and migration. In summary, we discovered a novel methylation-driven gene PCDHB4 for the diagnosis and prognosis of GBM and demonstrated that PCDHB4 is a tumor suppressor in vitro experiments.