Co-occurrence and Mutual Exclusivity Analysis of DNA Methylation Reveals Distinct Subtypes in Multiple Cancers

ALDH2 as an immunological and prognostic biomarker: Insights from pan-cancer analysis

Aldehyde dehydrogenase 2 (ALDH2) plays a critical role in safeguarding cells against acetaldehyde toxicity and is closely linked to human metabolism. Nevertheless, the involvement of ALDH2 in cancer remains enigmatic. This investigation seeks to comprehensively assess ALDH2's significance in pan-cancer. We conducted an all-encompassing analysis of pan-cancer utilizing multiple databases, including TCGA, linkedomicshs, UALCAN, and Kaplan-Meier plotter. We employed diverse algorithms such as EPIC, MCPCOUNTER, TIDTIMER, xCell, MCP-counter, CIBERSORT, quanTIseq, and EPIC to examine the connection between ALDH2 expression and immune cell infiltration. Single-cell sequencing analysis furnished insights into ALDH2's functional status in pan-cancer. Immunohistochemical staining was performed to validate ALDH2 expression in cancer tissues. In a comprehensive assessment, we observed that tumor tissues demonstrated diminished ALDH2 expression levels compared to normal tissues across 16 different cancer types. ALDH2 expression exhibited a significant positive correlation with the infiltration of immune cells, including CD4 + T cells, CD8 + T cells, neutrophils, B cells, and macrophages, in various tumor types. Moreover, this study explored the association between ALDH2 and patient survival, examined the methylation patterns of ALDH2 in normal and primary tumor tissues, and delved into genetic variations and mutations of ALDH2 in tumors. The findings suggest that ALDH2 could serve as a valuable prognostic biomarker in pan-cancer, closely linked to the tumor's immune microenvironment.

Carcinogenic roles of MAFG-AS1 in human cancers

The MAF bZIP transcription factor G-antisense RNA 1 (MAFG-AS1) is located on chromosome 17. MAFG-AS1 was upregulated in 15 human cancers. MAFG-AS1 not only suppresses 16 miRNAs but also directly impacts 22 protein-coding genes' expression. Notably, abnormal MAFG-AS1 expression is connected to clinicopathological characteristics and a worse prognosis in a variety of cancers. Moreover, MAFG-AS1 takes its part in the tumorigenesis and progression of various human malignancies by suppressing apoptosis and promoting proliferation, migration, invasion, aerobic glycolysis, ferroptosis, angiogenesis, EMT, and metastasis. Besides, it can predict treatment effectiveness in ER + breast cancer, urothelial bladder carcinoma, and liver cancer by functioning as a trigger of resistance to tamoxifen, sorafenib, and cisplatin. This study systematically presents the functions of MAFG-AS1 in various cancers, as well as the findings of bioinformatics analyses of the MAFG-AS1, which should give clear advice for future research.

Triggering pyroptosis enhances the antitumor efficacy of PARP inhibitors in prostate cancer

PURPOSE

PARP inhibitors have revolutionized the treatment landscape for advanced prostate cancer (PCa) patients who harboring mutations in homologous recombination repair (HRR) genes. However, the molecular mechanisms underlying PARP inhibitors function beyond DNA damage repair pathways remain elusive, and identifying novel predictive targets that favorably respond to PARP inhibitors in PCa is an active area of research.

METHODS

The expression of GSDME in PCa cell lines and human PCa samples was determined by western blotting. Targeted bisulfite sequencing, gene enrichment analysis (GSEA), clone formation, construction of the stably transfected cell lines, lactate dehydrogenase (LDH) assay, western blotting as well as a mouse model of subcutaneous xenografts were used to investigate the role of GSDME in PCa. The combinational therapeutic effect of olaparib and decitabine was determined using both in vitro and in vivo experiments.

RESULTS

We have found low expression of GSDME in PCa. Interestingly, we demonstrated that GSDME activity is robustly induced in olaparib-treated cells undergoing pyroptosis, and that high methylation of the GSDME promoter dampens its activity in PCa cells. Intriguingly, genetically overexpressing GSDME does not inhibit tumor cell proliferation but instead confers sensitivity to olaparib. Furthermore, pharmacological treatment with the combination of olaparib and decitabine synergistically induces GSDME expression and cleavage through caspase-3 activation, thus promoting pyroptosis and enhancing anti-tumor response, ultimately resulting in tumor remission.

CONCLUSION

Our findings highlight a novel therapeutic strategy for enhancing the long-term response to olaparib beyond HRR-deficient tumors in PCa, underscoring the critical role of GSDME in regulating tumorigenesis.

Integrative Analysis of DNA Methylation and Gene Expression Data Identifies Potential Biomarkers and Functional Epigenetic Modules for SARS-CoV-2

To integrate gene expression and DNA methylation data and find the potential role of DNA methylation in the invasion and replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We first conducted differential expression and methylation analysis between the coronavirus disease of 2019 (COVID-19) and healthy controls. FEM was employed to identify functional epigenetic modules, from which a diagnostic model for COVID-19 was built. SKA1 and WSB1 modules were identified, with SKA1 module enriched in COVID-19 replication and transcription, and WSB1 module related to ubiquitin-protein activity. The differentially expressed or differentially methylated genes in these two modules could be used to distinguish COVID-19 from healthy controls, with AUC reaching 1 and 0.98 for SKA1 and WSB1 modules, respectively. Two epigenetically activated genes (CENPM and KNL1) from the SKA1 module were upregulated in HPV- or HBV-positive tumor samples and were found to be significantly associated with the survival of tumor patients. In conclusion, the identified FEM modules and potential signatures play an essential role in the replication and transcription of coronavirus.

Epigenetic Alterations of DNA Methylation and miRNA Contribution to Lung Adenocarcinoma

This study focused on the epigenetic alterations of DNA methylation and miRNAs for lung adenocarcinoma (LUAD) diagnosis and treatment using bioinformatics analyses. DNA methylation data and mRNA and miRNA expression microarray data were obtained from The Cancer Genome Atlas (TCGA) database. The differentially methylated genes (DMGs), differentially expressed genes (DEGs), and differentially expressed miRNAs were analyzed by using the limma package. The DAVID database performed GO and KEGG pathway enrichment analyses. Using STRING and Cytoscape, we constructed the protein-protein interaction (PPI) network and achieved visualization. The online analysis tool CMap was used to identify potential small-molecule drugs for LUAD. In LUAD, 607 high miRNA-targeting downregulated genes and 925 low miRNA-targeting upregulated genes, as well as 284 hypermethylated low-expression genes and 315 hypomethylated high-expression genes, were obtained. They were mainly enriched in terms of pathways in cancer, neuroactive ligand-receptor interaction, cAMP signaling pathway, and cytosolic DNA-sensing pathway. In addition, 40 upregulated and 84 downregulated genes were regulated by both aberrant alternations of DNA methylation and miRNAs. Five small-molecule drugs were identified as a potential treatment for LUAD, and five hub genes (SLC2A1, PAX6, LEP, KLF4, and FGF10) were found in PPI, and two of them (SLC2A1 and KLF4) may be related to the prognosis of LUAD. In summary, our study identified a series of differentially expressed genes associated with epigenetic alterations of DNA methylation and miRNA in LUAD. Five small-molecule drugs and five hub genes may be promising drugs and targets for LUAD treatment.

The DNA methylation landscape of five pediatric-tumor types

Fewer DNA mutations have been identified in pediatric tumors than in adult tumors, suggesting that alternative tumorigenic mechanisms, including aberrant DNA methylation, may play a prominent role. In one epigenetic process of regulating gene expression, methyl groups are attached at the 5-carbon of the cytosine ring, leading to 5-methylcytosine (5mC). In somatic cells, 5mC occurs mostly in CpG islands, which are often within promoter regions. In Wilms tumors and acute myeloid leukemias, increased levels of epigenetic silencing have been associated with worse patient outcomes. However, to date, researchers have studied methylation primarily in adult tumors and for specific genes-but not on a pan-pediatric cancer scale. We addressed these gaps first by aggregating methylation data from 309 noncancerous samples, establishing baseline expectations for each probe and gene. Even though these samples represent diverse, noncancerous tissue types and population ancestral groups, methylation levels were consistent for most genes. Second, we compared tumor methylation levels against the baseline values for 489 pediatric tumors representing five cancer types: Wilms tumors, clear cell sarcomas of the kidney, rhabdoid tumors, neuroblastomas, and osteosarcomas. Tumor hypomethylation was more common than hypermethylation, and as many as 41.7% of genes were hypomethylated in a given tumor, compared to a maximum of 34.2% for hypermethylated genes. However, in known oncogenes, hypermethylation was more than twice as common as in other genes. We identified 139 probes (31 genes) that were differentially methylated between at least one tumor type and baseline levels, and 32 genes that were differentially methylated across the pediatric tumor types. We evaluated whether genomic events and aberrant methylation were mutually exclusive but did not find evidence of this phenomenon.

Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease

The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.

Thorough statistical analyses of breast cancer co-methylation patterns

Background

Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data).

Results

Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal samples have more than 93% positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites’ co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites. The top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites’ locations are significantly different from the genome-wide CG sites’ locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns.

Conclusions

Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.

The Prognostic Value and Immunological Role of STEAP1 in Pan-Cancer: A Result of Data-Based Analysis

Purpose. This study is aimed at systematically analyzing the expression, function, and prognostic value of six transmembrane epithelial antigen of the prostate 1 (STEAP1) in various cancers. Methods. The expressions of STEAP1 between normal and tumor tissues were analyzed using TCGA and GTEx. Clinicopathologic data was collected from GEPIA and TCGA. Prognostic analysis was conducted by Cox proportional hazard regression and Kaplan-Meier survival. DNA methylation, mutation features, and molecular subtypes of cancers were also investigated. The top-100 coexpressed genes with STEAP1 were involved in functional enrichment analysis. ESTIMATE algorithm was used to analyze the correlation between STEAP1 and immunity value. The relationships of STEAP1 and biomarkers including tumor mutational burden (TMB), microsatellite instability (MSI), and stemness score as well as chemosensitivity were also illustrated. Results. Among 33 cancers, STEAP1 was overexpressed in 19 cancers such as cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma, and lymphoid neoplasm diffuse large B cell lymphoma while was downregulated in 5 cancers such as adrenocortical carcinoma, breast invasive carcinoma (BRCA), and kidney chromophobe renal cell carcinoma. STEAP1 has significant prognostic relationships in multiple cancers. 15 cancers exhibited differences of DNA methylation including bladder urothelial carcinoma, BRCA, and CESC. STEAP1 expression was positively correlated to immune molecules especially in thyroid carcinoma and negatively especially in uveal melanoma. STEAP1 was associated with TMB and MSI in certain cancers. In addition, STEAP1 was connected with increased chemosensitivity of drugs such as trametinib and pimasertib. Conclusions. STEAP1 was an underlying target for prognostic prediction in different cancer types and a potential biomarker of TMB, MSI, tumor microenvironment, and chemosensitivity.

Methylation silencing CDH23 is a poor prognostic marker in diffuse large B-cell lymphoma

Cadherin-23(CDH23) mediates homotypic and heterotypic cell-cell adhesions in cancer cells. However, the epigenetic regulation, the biological functions, the mechanisms and the prognostic value of CDH23 in diffuse large B-cell lymphoma (DLBCL) are still unclear. The Gene Expression Profiling Interactive Analysis (GEPIA) and the Gene Expression Omnibus (GEO) database were employed to analyze the CDH23 expression level in DLBCL. The correlation of CDH23 expression and methylation was analyzed by LinkedOmics database. The prognostic value was analyzed via GEPIA. Correlated genes, target kinase, target miRNA, target transcription factor and biological functions were identified by LinkedOmics and GeneMANIA database. The relationship between CDH23 and the immune cell infiltration was explored by the Tumor Immune Estimation Resource (TIMER). The expression of CDH23 was reduced by DNA methylation significantly in DLBCL tissue. Reduction of CDH23 represented poor outcome of DLBCL patients. Functional enrichment analysis showed that CDH23 mainly enriched in cancer cell growth, cell metastasis, cell adhesion, cell cycle, drug catabolic process, leukocyte mediated immunity and DNA repair by some cancer related kinases, miRNAs and transcription factors. These results indicated that methylated reduction of CDH23 represented poor outcome of DLBCL. CDH23 is associated with essential biological functions and key molecules in DLBCL. CDH23 may play crucial roles in DLBCL tumorigenesis. Our results lay a foundation for further investigation of the role of CDH23 in DLBCL tumorigenesis.

A Risk Prediction Model for Breast Cancer Based on Immune Genes Related to Early Growth Response Proteins Family

Early growth response proteins (EGRs), a transcriptional regulatory family comprised of EGR1, EGR2, EGR3, and EGR 4, are reportedly involved in a vast array of functions. However, EGRs, as a whole, are rarely studied in breast cancer cases. This research was performed based on public datasets. The results demonstrated that, except EGR4, the other EGRs were differentially expressed genes in breast cancer. Subsequently, this study determined the prognosis significance of the EGR family, higher expression levels of EGRs indicating better overall survival (OS) and disease-free survival (DFS), except EGR4. So we attempted to explore the potential mechanism behind the prognostic value of EGRs. At the DNA level, however, neither DNA methylation status nor genetic alterations of EGRs contributed to the prognosis significance. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that EGRs were involved in several immune-related functions. Afterward, we assessed the correlation between EGRs and the immune system before establishing a risk prediction model with a 14-gene immune signature associated with EGRs, a prognostic nomogram predicting individuals’ 1-, 3-, and 5-year survival probabilities. The risk score was an independent prognosis predictor in the breast cancer cohorts. This study evidenced EGRs’ significance for tumor immunity, demonstrating that the EGR family may be a potential immunotherapeutic target for breast cancer. The 14-gene immune signature is a promising prognostic biomarker in breast cancer.

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

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

Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer

Background

It is crucial to unravel molecular determinants of responses to immune checkpoint blockade (ICB) therapy because only a small subset of advanced non-small cell lung cancer (NSCLC) patients responds to ICB therapy. Previous studies were concentrated on genomic and transcriptomic markers (e.g., mutation burden and immune gene expression). However, these markers are not sufficient to accurately predict a response to ICB therapy.

Results

Here, we analyzed DNA methylomes of 141 advanced NSCLC samples subjected to ICB therapy (i.e., anti-programmed death-1) from two independent cohorts (60 and 81 patients from our and IDIBELL cohorts). Integrative analysis of patients with matched transcriptome data in our cohort (n = 28) at pathway level revealed significant overlaps between promoter hypermethylation and transcriptional repression in nonresponders relative to responders. Fifteen immune-related pathways, including interferon signaling, were identified to be enriched for both hypermethylation and repression. We built a reliable prognostic risk model based on eight genes using LASSO model and successfully validated the model in independent cohorts. Furthermore, we found 30 survival-associated molecular interaction networks, in which two or three hypermethylated genes showed significant mutual exclusion across nonresponders.

Conclusions

Our study demonstrates that methylation patterns can provide insight into molecular determinants underlying the clinical benefit of ICB therapy.