ZNF350 promoter methylation accelerates colon cancer cell migration

A Meta-Analysis Approach to Gene Regulatory Network Inference Identifies Key Regulators of Cardiovascular Diseases

Cardiovascular diseases (CVDs) represent a major concern for global health, whose mechanistic understanding is complicated by a complex interplay between genetic predisposition and environmental factors. Specifically, heart failure (HF), encompassing dilated cardiomyopathy (DC), ischemic cardiomyopathy (ICM), and hypertrophic cardiomyopathy (HCM), is a topic of substantial interest in basic and clinical research. Here, we used a Partial Correlation Coefficient-based algorithm (PCC) within the context of a meta-analysis framework to construct a Gene Regulatory Network (GRN) that identifies key regulators whose activity is perturbed in Heart Failure. By integrating data from multiple independent studies, our approach unveiled crucial regulatory associations between transcription factors (TFs) and structural genes, emphasizing their pivotal roles in regulating metabolic pathways, such as fatty acid metabolism, oxidative stress response, epithelial-to-mesenchymal transition, and coagulation. In addition to known associations, our analysis also identified novel regulators, including the identification of TFs FPM315 and OVOL2, which are implicated in dilated cardiomyopathies, and TEAD1 and TEAD2 in both dilated and ischemic cardiomyopathies. Moreover, we uncovered alterations in adipogenesis and oxidative phosphorylation pathways in hypertrophic cardiomyopathy and discovered a role for IL2 STAT5 signaling in heart failure. Our findings underscore the importance of TF activity in the initiation and progression of cardiac disease, highlighting their potential as pharmacological targets.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes

Artificial intelligence (AI) approaches in cancer analysis typically utilize a 'one-size-fits-all' methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment.

The role of zinc finger proteins in malignant tumors

Zinc finger proteins (ZNFs) are the largest family of transcriptional factors in mammalian cells. Recently, their role in the development, progression, and metastasis of malignant tumors via regulating gene transcription and translation processes has become evident. Besides, their possible involvement in drug resistance has also been found, indicating that ZNFs have the potential to become new biological markers and therapeutic targets. In this review, we summarize the oncogenic and suppressive roles of various ZNFs in malignant tumors, including lung, breast, liver, gastric, colorectal, pancreatic, and other cancers, highlighting their role as prognostic markers, and hopefully provide new ideas for the treatment of malignant tumors in the future.

Genome-wide DNA methylation profiling of HPV-negative leukoplakia and gingivobuccal complex cancers

BACKGROUND

Gingivobuccal complex oral squamous cell carcinoma (GBC-OSCC) is an aggressive malignancy with high mortality often preceded by premalignant lesions, including leukoplakia. Previous studies have reported genomic drivers in OSCC, but much remains to be elucidated about DNA methylation patterns across different stages of oral carcinogenesis.

RESULTS

There is a serious lack of biomarkers and clinical application of biomarkers for early detection and prognosis of gingivobuccal complex cancers. Hence, in search of novel biomarkers, we measured genome-wide DNA methylation in 22 normal oral tissues, 22 leukoplakia, and 74 GBC-OSCC tissue samples. Both leukoplakia and GBC-OSCC had distinct methylation profiles as compared to normal oral tissue samples. Aberrant DNA methylation increases during the different stages of oral carcinogenesis, from premalignant lesions to carcinoma. We identified 846 and 5111 differentially methylated promoters in leukoplakia and GBC-OSCC, respectively, with a sizable fraction shared between the two sets. Further, we identified potential biomarkers from integrative analysis in gingivobuccal complex cancers and validated them in an independent cohort. Integration of genome, epigenome, and transcriptome data revealed candidate genes with gene expression synergistically regulated by copy number and DNA methylation changes. Regularised Cox regression identified 32 genes associated with patient survival. In an independent set of samples, we validated eight genes (FAT1, GLDC, HOXB13, CST7, CYB5A, MLLT11, GHR, LY75) from the integrative analysis and 30 genes from previously published reports. Bisulfite pyrosequencing validated GLDC (P = 0.036), HOXB13 (P < 0.0001) promoter hypermethylation, and FAT1 (P < 0.0001) hypomethylation in GBC-OSCC compared to normal controls.

CONCLUSIONS

Our findings identified methylation signatures associated with leukoplakia and gingivobuccal complex cancers. The integrative analysis in GBC-OSCC identified putative biomarkers that enhance existing knowledge of oral carcinogenesis and may potentially help in risk stratification and prognosis of GBC-OSCC.

A novel DNA damage repair gene-related prognostic model for evaluating the prognosis and tumor microenvironment infiltration of esophageal squamous cell carcinoma

BACKGROUND

This study aimed to investigate the potential prognostic value of DNA damage repair genes (DDRGs) in esophageal squamous cell carcinoma (ESCC) and their relationship with immune-related characteristics.

METHODS

We analyzed DDRGs of the Gene Expression Omnibus database (GSE53625). Subsequently, the GSE53625 cohort was used to construct a prognostic model based on least absolute shrinkage and selection operator regression, and Cox regression analysis was used to construct a nomogram. The immunological analysis algorithms explored the differences between the potential mechanism, tumor immune activity, and immunosuppressive genes in the high- and low-risk groups. Of the prognosis model-related DDRGs, we selected PPP2R2A for further investigation. Functional experiments were conducted to evaluate the effect on ESCC cells in vitro.

RESULTS

A 5-DDRG (ERCC5, POLK, PPP2R2A, TNP1 and ZNF350) prediction signature was established for ESCC, stratifying patients into two risk groups. Multivariate Cox regression analysis showed that the 5-DDRG signature was an independent predictor of overall survival. Immune cells such as CD4 T cells and monocytes displayed lower infiltration levels in the high-risk group. Additionally, the immune, ESTIMATE, and stromal scores in the high-risk group were all considerably higher than those in the low-risk group. Functionally, knockdown of PPP2R2A significantly suppressed cell proliferation, migration and invasion in two ESCC cell lines (ECA109 and TE1).

CONCLUSION

The clustered subtypes and prognostic model of DDRGs could effectively predict the prognosis and immune activity of ESCC patients.

Maternal Copy Number Imbalances in Non-Invasive Prenatal Testing: Do They Matter?

Non-invasive prenatal testing (NIPT) has become a routine practice in screening for common aneuploidies of chromosomes 21, 18, and 13 and gonosomes X and Y in fetuses worldwide since 2015 and has even expanded to include smaller subchromosomal events. In fact, the fetal fraction represents only a small proportion of cell-free DNA on a predominant background of maternal DNA. Unlike fetal findings that have to be confirmed using invasive testing, it has been well documented that NIPT provides information on maternal mosaicism, occult malignancies, and hidden health conditions due to copy number variations (CNVs) with diagnostic resolution. Although large duplications or deletions associated with certain medical conditions or syndromes are usually well recognized and easy to interpret, very little is known about small, relatively common copy number variations on the order of a few hundred kilobases and their potential impact on human health. We analyzed data from 6422 NIPT patient samples with a CNV detection resolution of 200 kb for the maternal genome and identified 942 distinct CNVs; 328 occurred repeatedly. We defined them as multiple occurring variants (MOVs). We scrutinized the most common ones, compared them with frequencies in the gnomAD SVs v2.1, dbVar, and DGV population databases, and analyzed them with an emphasis on genomic content and potential association with specific phenotypes.

Identifying the critical states and dynamic network biomarkers of cancers based on network entropy

Background

There are sudden deterioration phenomena during the progression of many complex diseases, including most cancers; that is, the biological system may go through a critical transition from one stable state (the normal state) to another (the disease state). It is of great importance to predict this critical transition or the so-called pre-disease state so that patients can receive appropriate and timely medical care. In practice, however, this critical transition is usually difficult to identify due to the high nonlinearity and complexity of biological systems.

Methods

In this study, we employed a model-free computational method, local network entropy (LNE), to identify the critical transition/pre-disease states of complex diseases. From a network perspective, this method effectively explores the key associations among biomolecules and captures their dynamic abnormalities.

Results

Based on LNE, the pre-disease states of ten cancers were successfully detected. Two types of new prognostic biomarkers, optimistic LNE (O-LNE) and pessimistic LNE (P-LNE) biomarkers, were identified, enabling identification of the pre-disease state and evaluation of prognosis. In addition, LNE helps to find “dark genes” with nondifferential gene expression but differential LNE values.

Conclusions

The proposed method effectively identified the critical transition states of complex diseases at the single-sample level. Our study not only identified the critical transition states of ten cancers but also provides two types of new prognostic biomarkers, O-LNE and P-LNE biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03445-0.

Development of a kind of RG108-Fluorescein conjugates for detection of DNA methyltransferase 1 (DNMT1) in living cells

DNA methyltransferase 1 (DNMT1) is one of the most essential proteins in propagating DNA methylation patterns during replication. Developing methods to assess the expression level of DNMT1 will enable study of gene methylation abnormalities. Thus, a series of fluorescein-conjugated RG108 derivatives were designed and synthesized in the current study. The affinity of the derivatives with DNMT1 was evaluated using surface plasmon resonance. Permeability of the derivatives through the cytomembrane and nuclear envelope was evaluated via confocal imaging. Probe 8a was found to compete with RG108 binding to DNMT1 in the nucleus of HeLa cells, suggesting that probe 8a and RG108 share the same binding site. A HeLa cell model with 4.05-fold overexpression of DNMT1 was constructed and used to evaluate probe 8a. Probe 8a was found to be significantly increased in the nucleus of DNMT1 overexpressing cells. These results indicate that fluorescent probes derived from RG108 have the potential to be used for evaluating the expression level of DNMT1 in living cells.

The MicroRNA-23b/27b/24 Cluster Facilitates Colon Cancer Cell Migration by Targeting FOXP2

Acquisition of cell migration capacity is an early and essential process in cancer development. The aim of this study was to identify microRNA gene expression networks that induced high migration capacity. Using colon cancer HCT116 cells subcloned by transwell-based migrated cell selection, microRNA array analysis was performed to examine the microRNA expression profile. Promoter activity and microRNA targets were assessed with luciferase reporters. Cell migration capacity was assessed by either the transwell or scratch assay. In isolated subpopulations with high migration capacity, the expression levels of the miR-23b/27b/24 cluster increased in accordance with the increased expression of the short C9orf3 transcript, a host gene of the miR-23b/27b/24 cluster. E2F1-binding sequences were involved in the basic transcription activity of the short C9orf3 expression, and E2F1-small-interfering (si)RNA treatment reduced the expression of both the C9orf3 and miR-23b/27b/24 clusters. Overexpression experiments showed that miR-23b and miR-27b promoted cell migration, but the opposite effect was observed with miR-24. Forkhead box P2 (FOXP2) mRNA and protein levels were reduced by both/either miR-23b and miR-27b. Furthermore, FOXP2 siRNA treatment significantly promoted cell migration. Our findings demonstrated a novel role of the miR-23b/27b/24 cluster in cell migration through targeting FOXP2, with potential implications for the development of microRNA-based therapy targeted at inhibiting cancer migration.

Identification of differentially expressed genes regulated by methylation in colon cancer based on bioinformatics analysis

BACKGROUND

DNA methylation, acknowledged as a key modification in the field of epigenetics, regulates gene expression at the transcriptional level. Aberrant methylation in DNA regulatory regions could upregulate oncogenes and downregulate tumor suppressor genes without changing the sequences. However, studies of methylation in the control of gene expression are still inadequate. In the present research, we performed bioinformatics analysis to clarify the function of methylation and supply candidate methylation-related biomarkers and drivers for colon cancer.

AIM

To identify and analyze methylation-regulated differentially expressed genes (MeDEGs) in colon cancer by bioinformatics analysis.

METHODS

We downloaded RNA expression profiles, Illumina Human Methylation 450K BeadChip data, and clinical data of colon cancer from The Cancer Genome Atlas project. MeDEGs were identified by analyzing the gene expression and methylation levels using the edgeR and limma package in R software. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the DAVID database and KEGG Orthology-Based Annotation System 3.0, respectively. We then conducted Kaplan–Meier survival analysis to explore the relationship between methylation and expression and prognosis. Gene set enrichment analysis (GSEA) and investigation of protein-protein interactions (PPI) were performed to clarify the function of prognosis-related genes.

RESULTS

A total of 5 up-regulated and 81 down-regulated genes were identified as MeDEGs. GO and KEGG pathway analyses indicated that MeDEGs were enriched in multiple cancer-related terms. Furthermore, Kaplan–Meier survival analysis showed that the prognosis was negatively associated with the methylation status of glial cell-derived neurotrophic factor (GDNF) and reelin (RELN). In PPI networks, GDNF and RELN interact with neural cell adhesion molecule 1. Besides, GDNF can interact with GDNF family receptor alpha (GFRA1), GFRA2, GFRA3, and RET. RELN can interact with RAFAH1B1, disabled homolog 1, very low-density lipoprotein receptor, lipoprotein receptor-related protein 8, and NMDA 2B. Based on GSEA, hypermethylation of GDNF and RELN were both significantly associated with pathways including “RNA degradation,” “ribosome,” “mismatch repair,” “cell cycle” and “base excision repair.”

CONCLUSION

Aberrant DNA methylation plays an important role in colon cancer progression. MeDEGs that are associated with the overall survival of patients may be potential targets in tumor diagnosis and treatment.