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.

A three-methylation-driven genes based deep learning model for tuberculosis diagnosis in people with and without human immunodeficiency virus co-infection

Improved diagnostic tests for tuberculosis (TB) among people living with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methylation-driven genes of host blood could be used to diagnosis patients co-infected with HIV/TB. In this study, we identified three methylation-driven genes (MDGs) between patients with HIV mono-infection and with HIV/TB co-infection using R package MethylMix. Then, we developed a deep learning model using three MDGs screened which distinguished HIV/TB co-infection from HIV mono-infection with a sensitivity of 95.2% and a specificity of 88.3%. On the two independent datasets, the sensitivity was 80% to 92.8%, respectively; the specificity was 72.7% to 87.5%, respectively. Besides, our deep learning model also accurately classified TB from healthy controls (75.0-100% sensitivity, 91.3-98.1% specificity) and other respiratory disorders (ORDs) (72.7-75.0% sensitivity, 70.9-72.7% specificity). This study will contribute to improve molecular diagnosis for HIV/TB co-infection. This article is protected by copyright. All rights reserved.

Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Accumulating evidence suggests that DNA methylation has essential roles in the development of renal cell carcinoma (RCC). Aberrant DNA methylation acts as a vital role in RCC progression through regulating the gene expression, yet little is known about the role of methylation and its association with prognosis in RCC. The purpose of this study is to explore the DNA methylation-driven genes for establishing prognostic-related molecular clusters and providing a basis for survival prediction. In this study, 5,198 differentially expressed genes (DEGs) and 270 DNA methylation-driven genes were selected to obtain 146 differentially expressed DNA methylation-driven genes (DEMDGs). Two clusters were distinguished by consensus clustering using 146 DEMDGs. We further evaluated the immune status of two clusters and selected 106 DEGs in cluster 1. Cluster-based immune status analysis and functional enrichment analysis of 106 DEGs provide new insights for the development of RCC. To predict the prognosis of patients with RCC, a prognostic model based on eight DEMDGs was constructed. The patients were divided into high-risk groups and low-risk groups based on their risk scores. The predictive nomogram and the web-based survival rate calculator (http://127.0.0.1:3496) were built to validate the predictive accuracy of the prognostic model. Gene set enrichment analysis was performed to annotate the signaling pathways in which the genes are enriched. The correlation of the risk score with clinical features, immune status, and drug susceptibility was also evaluated. These results suggested that the prognostic model might be a promising prognostic tool for RCC and might facilitate the management of patients with RCC.

Construction of Novel Methylation-Driven Gene Model and Investigation of PARVB Function in Glioblastoma

Background

Glioblastoma multiforme (GBM) is characterized by widespread genetic and transcriptional heterogeneity. Aberrant DNA methylation plays a vital role in GBM progression by regulating gene expression. However, little is known about the role of methylation and its association with prognosis in GBM. Our aim was to explore DNA methylation-driven genes (DMDGs) and provide evidence for survival prediction and individualized treatment of GBM patients.

Methods

Use of the MethylMix R package identified DMDGs in GBM. The prognostic signature of DMDGs based on the risk score was constructed by multivariate Cox regression analysis. Receiver operating characteristics (ROC) curve and C-index were applied to assess the predictive performance of the DMDG prognostic signature. The predictive ability of the multigene signature model was validated in TCGA and CGGA cohorts. Finally, the role of DMDG β-Parvin (PARVB) was explored in vitro.

Results

The prognostic signature of DMDGs was constructed based on six genes (MDK, NMNAT3, PDPN, PARVB, SERPINB1, and UPP1). The low-risk cohort had significantly better survival than the high-risk cohort (p < 0.001). The area under the curve of the ROC of the six-gene signature was 0.832, 0.927, and 0.980 within 1, 2, and 3 years, respectively. The C-index of 0.704 indicated superior specificity and sensitivity. The six-gene model has been demonstrated to be an independent prognostic factor for GBM. In addition, joint survival analysis indicated that the MDK, NMNAT3, PARVB, SERPINB1, and UPP1 genes were significantly associated with prognosis and therapeutic targets for GBM. Importantly, our DMDG prognostic model was more suitable and accurate for low-grade gliomas. Finally, we verified that PARVB induced epithelial-mesenchymal transition partially through the JAK2/STAT3 pathway, which in turn promoted GBM cell proliferation, migration, and invasion.

Conclusion

This study demonstrated the potential value of the prognostic signature of DMDGs and provided important bioinformatic and potential therapeutic target data to facilitate individualized treatment for GBM, and to elucidate the specific mechanism by which PARVB promotes GBM progression.

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

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

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Background

Aberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.

Methods

Methylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).

Results

We identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P < 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HR_indirect = 1.473, P = 0.001, Proportion mediated, 69.10%).

Conclusions

We identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients.

Methylation-Driven Genes Identified as Novel Prognostic Indicators for Thyroid Carcinoma

BACKGROUND

Aberrant DNA methylation plays an crucial role in tumorigenesis through regulating gene expression. Nevertheless, the exact role of methylation in the carcinogenesis of thyroid cancer and its association with prognosis remains unclear. The purpose of this study is to explore the DNA methylation-driven genes in thyroid cancer by integrative bioinformatics analysis.

METHODS

The transcriptome profiling data and DNA methylation data of thyroid cancer were downloaded from The Cancer Genome Atlas (TCGA) database. The methylmix R package was used to screen DNA methylation-driven genes in thyroid cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted to annotate the function of methylation-driven genes. Univariate Cox regression analyses was performed to distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses was utilized to build a prognostic multi-gene signature. A survival analysis was carried out to determine the individual prognostic significance of this multi-gene signature.

RESULTS

A total of 51 methylation-driven genes were identified. The functional analysis indicated that these genes were significantly enriched in diverse biological processes (BP) and pathways related to the malignancy processes. Four of these genes (RDH5, TREM1, BIRC7, and SLC26A7) were selected to construct the risk evaluation model. Patients in the low-risk group had an conspicuously better overall survival (OS) than those in high-risk group (p < 0.001). The area under the receiver operating characteristic (ROC) curve for this model was 0.836, suggesting a good specificity and sensitivity. Subsequent survival analysis revealed that this four-gene signature served as an independent indicator for the prognosis of thyroid cancer. Moreover, the prognostic signature was well validated in a external thyroid cancer cohort.

CONCLUSION

We identified methylation-driven genes in thyroid cancer with independent prognostic value, which may offer new insight into molecular mechanisms of thyroid cancer and provide new possibility for individualized treatment of thyroid cancer patients.

A FITM1-Related Methylation Signature Predicts the Prognosis of Patients With Non-Viral Hepatocellular Carcinoma

Although great progress has been made in treatment against hepatitis virus infection, the prognosis of hepatocellular carcinoma (HCC) remains unsatisfied. Therefore, there is an unmet need to explore biomarkers or prognostic models for monitoring non-viral hepatocellular carcinoma. Accumulating evidence indicates that DNA methylation participates in carcinogenesis of malignancies. In the present study, we analyzed 101 non-viral HCC patients from TCGA database to figure out methylation-driven genes (MDGs) that might get involved in non-viral HCC pathogenesis using MethyMix algorithm. Then we picked out 8 key genes out of 137 MDGs that could affect the overall survival (OS) of both methylation and expression level. Using PCA, Uni-variate, Multi-variate, and LASSO cox regression analyses, we confirmed the potential prognostic value of these eight epigenetic genes. Ultimately, combined with immunohistochemistry (IHC), ROC, OS, and GSEA analyses, fat storage-inducing transmembrane protein1 (FITM1) was identified as a novel tumor suppressor gene in non-viral HCC and an applicable FITM1-methylation-based signature was built in a training set and validated in a testing set. Briefly, our work provides several potential biomarkers, especially FITM1, as well as a new method for disease surveillance and treatment strategy development.

Comprehensive and integrative analysis identifies COX7A1 as a critical methylation-driven gene in breast invasive carcinoma

Background

Aberrant DNA methylation plays a crucial part in cancer progression through the silencing of gene expression. The purpose of this article was to investigate the DNA methylation-driven genes in breast invasive carcinoma (BRCA) by using integrated bioinformatics analysis and in vitro experiments.

Methods

The methylation and expression profile data of BRCA patients were downloaded from the TCGA database. Besides, the MethylMix algorithm was performed to distinguish differentially methylation-driven genes. Moreover, methylation-specific PCR was used to test the methylation-driven genes.

Results

A total of 218 differentially expressed methylation-driven genes were obtained. Then, four of these genes were applied to establish a prognostic risk model. Moreover, we found that hypermethylation was in the CpG islands of the promoter of COX7A1 gene in BRCA tissues. Furthermore, we found that COX7A1 was significantly down-regulated BRCA tissues and the COX7A1 expression level was markedly increased in BRCA cells after 5-Aza-dC treatment.

Conclusions

Our study reveals that aberrant promoter hypermethylation is critical for COX7A1 gene silencing in BRCA and that COX7A1 emerge as a new biomarker and therapeutic target for BRCA.

Identification of methylation-driven genes related to prognosis in clear-cell renal cell carcinoma

With the participation of the existing treatment methods, the prognosis of advanced clear‐cell renal cell carcinoma (ccRCC) is poor. More evidence indicates the presence of methylation in ccRCC cancer cells, but there is a lack of studies on methylation‐driven genes in ccRCC. We analyzed the open data of ccRCC in The Cancer Genome Atlas database to obtain ccRCC‐related methylation‐driven genes, and then carried out pathway enrichment, survival, and joint survival analyses. More important, we deeply explored the correlation between differential methylation sites and the expression of these driving genes. Finally, we screened 29 methylation‐driven genes via MethylMix, of which six were significantly associated with the survival of ccRCC patients. This study demonstrated that the effect of hypermethylation or hypomethylation on prognosis is different, and the level of methylation of key methylation sites is associated with gene expression. We identified methylation‐driven genes independently predicting prognosis in ccRCC, which offers theoretical support in bioinformatics for the study of methylation in ccRCC and a new perspective for the epigenetic study of ccRCC.