Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
2.8
Journals
Genes
Special Issues
DNA and RNA Epigenetics and Transcriptomics Research
share announcement

DNA and RNA Epigenetics and Transcriptomics Research

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 34307

Special Issue Editors

Prof. Dr. Rujiao Li

E-Mail Website
Guest Editor
National Genomics Data Center, Beijing Institute of Genomics (BIG), Chinese Academy of Science (CAS) & China National Center for Bioinformation (CNCB), Beijing 100101, China
Interests: genomics; epigenomes; bioinformatics; big data integration; database development; algorithm development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yan Zhang

E-Mail Website
Guest Editor
School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: computational biology; epigenomes; tumor bioinformatics; tumor biomarker; database; deep learning; histone modification; chromatin accessibility
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianzhong Su

E-Mail Website
Guest Editor
School of Ophthalmology and Optometry, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, China
Interests: DNA methylation editing; single-cell epigenomes; genetic diagnosis; tumor evolution; organoid regeneration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Epigenomic regulation refers to the heritable alterations in gene expression that do not involve changes in the DNA sequences themselves. DNA modifications were proposed as a carrier of epigenetic information early on, with subsequent work revealing that histone modifications, chromatin accessibility, noncoding RNAs, and RNA modifications are also important for this process. The rapidly accelerated epigenomics research, resulting in it becoming a cutting-edged field of research that drives spectacular progresses in our understanding of the related molecular mechanisms, regulatory functions, and interaction with other biological process.

This Special Issue will focus on the interplay between regulatory function and epigenomics, including cellular and reprogramming events, epigenetic inheritance across generations and response to physiological stimuli and in disease. The non-coding RNAs, DNA and RNA modifications, and chromatin accessibility that can regulate both inheritance and gene expression plasticity will all be discussed.

Dr. Rujiao Li
Prof. Dr. Yan Zhang
Prof. Dr. Jianzhong Su
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • epigenetics regulation
  • transcriptional regulation
  • DNA modification
  • RNA modification
  • noncoding RNA regulation
  • omics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (12 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

16 pages, 3223 KiB  
Article
Integrative Analysis of miRNAs Involved in Fat Deposition in Different Pig Breeds
by Xiuxiu Zhang, Wanlong Huang, Yuntao Guo and Xiangyang Miao
Genes 2023, 14(1), 94; https://doi.org/10.3390/genes14010094 - 28 Dec 2022
Cited by 2 | Viewed by 1921
Abstract
Background: miRNAs are a set of small, noncoding RNAs that bind to partially complementary sequences on target mRNAs. This leads to the post-transcriptional regulation of gene expression. Many studies have shown that microRNAs play critical roles in adipose cell differentiation and fat metabolism. [...] Read more.
Background: miRNAs are a set of small, noncoding RNAs that bind to partially complementary sequences on target mRNAs. This leads to the post-transcriptional regulation of gene expression. Many studies have shown that microRNAs play critical roles in adipose cell differentiation and fat metabolism. The aim of this study was to explore the regulatory functions of miRNAs in fat deposition for the prevention and therapy of lipid metabolism-related diseases. Methods: The significant differences in the fat deposition of Laiwu (LW) pigs and Large White (LY) pigs were studied. To investigate the genetic relationships of miRNAs that regulate fat deposition, we performed a genome-wide analysis of miRNAs derived from subcutaneous adipose tissue of LW and LY pigs using RNA-seq. Results: There were 39 known miRNAs and 56 novel miRNAs significantly differential expressed between the two breeds of pigs. In the analysis of the Gene Ontology and KEGG pathways, predicted targets of these differentially expressed miRNAs were involved in several fat-associated pathways, such as the peroxisome proliferator-activated receptor (PPAR), mitogen-activated protein kinases (MAPK) and Wnt signaling pathways. In addition, ssc-miR-133a-3p, ssc-miR-486 and ssc-miR-1 each had a great impact on the development of porcine subcutaneous fat through the PPAR signaling pathway. Conclusions: We explored the role of differentially expressed miRNAs and studied the mechanisms of adipogenesis and fat deposition between two different pig breeds. In addition, these results also contribute to research relevant to human obesity. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

18 pages, 3177 KiB  
Article
Analysis of Tumor Microenvironment Heterogeneity among Breast Cancer Subtypes to Identify Subtype-Specific Signatures
by Ji Li, Jiashuo Wu and Junwei Han
Genes 2023, 14(1), 44; https://doi.org/10.3390/genes14010044 - 23 Dec 2022
Cited by 10 | Viewed by 2439
Abstract
Breast cancer is one of the most frequent malignancies in women worldwide. According to 50-gene signature, Prediction Analysis of Microarray 50 (PAM50), breast cancer can be categorized into five molecular subtypes, and these subtypes are highly heterogeneous in different molecular characteristics. However, the [...] Read more.
Breast cancer is one of the most frequent malignancies in women worldwide. According to 50-gene signature, Prediction Analysis of Microarray 50 (PAM50), breast cancer can be categorized into five molecular subtypes, and these subtypes are highly heterogeneous in different molecular characteristics. However, the landscape of their tumor microenvironment (TME) heterogeneity has not been fully researched. Using the multi-omics dataset of breast cancer from the METABRIC cohort (n = 1699), we conducted extensive analyses of TME-related features to investigate TME heterogeneity in each breast cancer subtype. We then developed a cell-based subtype set enrichment analysis to identify the subtype-specific TME cells, and further evaluate their prognostic effects. Our results illustrate that different breast cancer subtypes exhibit different TME patterns. Basal-like and HER2-enriched subtypes are associated with high immune scores, expression of most immune regulatory targets, and immune cell infiltration, suggesting that these subtypes could be defined as “immune hot” tumors and suitable for immune checkpoint blockade (ICB) therapy. In contrast, Luminal A and Luminal B subtypes are associated with low immune scores and immune cell infiltration, suggesting that these subtypes could be defined as “immune cold” tumors. Additionally, the Normal-like subtype has relatively high levels of both immune and stromal features, which indicates that the Normal-like subtype may be suitable for more diverse treatment strategies. Our study reveals the breast cancer tumor microenvironment heterogeneity across subtypes. The comprehensive analysis of breast cancer TME-related characteristics may help us to adopt a tailored treatment strategy for different subtypes of patients. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

19 pages, 2302 KiB  
Article
Identifying Associations between DCE-MRI Radiomic Features and Expression Heterogeneity of Hallmark Pathways in Breast Cancer: A Multi-Center Radiogenomic Study
by Wenlong Ming, Yanhui Zhu, Fuyu Li, Yunfei Bai, Wanjun Gu, Yun Liu, Xiao Sun, Xiaoan Liu and Hongde Liu
Genes 2023, 14(1), 28; https://doi.org/10.3390/genes14010028 - 22 Dec 2022
Cited by 2 | Viewed by 2547
Abstract
Background: To investigate the relationship between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic features and the expression activity of hallmark pathways and to develop prediction models of pathway-level heterogeneity for breast cancer (BC) patients. Methods: Two radiogenomic cohorts were analyzed (n = [...] Read more.
Background: To investigate the relationship between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic features and the expression activity of hallmark pathways and to develop prediction models of pathway-level heterogeneity for breast cancer (BC) patients. Methods: Two radiogenomic cohorts were analyzed (n = 246). Tumor regions were segmented semiautomatically, and 174 imaging features were extracted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to identify significant imaging-pathway associations. Random forest regression was used to predict pathway enrichment scores. Five-fold cross-validation and grid search were used to determine the optimal preprocessing operation and hyperparameters. Results: We identified 43 pathways, and 101 radiomic features were significantly related in the discovery cohort (p-value < 0.05). The imaging features of the tumor shape and mid-to-late post-contrast stages showed more transcriptional connections. Ten pathways relevant to functions such as cell cycle showed a high correlation with imaging in both cohorts. The prediction model for the mTORC1 signaling pathway achieved the best performance with the mean absolute errors (MAEs) of 27.29 and 28.61% in internal and external test sets, respectively. Conclusions: The DCE-MRI features were associated with hallmark activities and may improve individualized medicine for BC by noninvasively predicting pathway-level heterogeneity. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

17 pages, 4055 KiB  
Article
Revealing the Potential Markers of N(4)-Acetylcytidine through acRIP-seq in Triple-Negative Breast Cancer
by Xingda Zhang, Jiaqi Zeng, Jianyu Wang, Zihan Yang, Song Gao, Honghao Liu, Guozheng Li, Xin Zhang, Yue Gu and Da Pang
Genes 2022, 13(12), 2400; https://doi.org/10.3390/genes13122400 - 18 Dec 2022
Cited by 11 | Viewed by 2518
Abstract
Understanding the causes of tumorigenesis and progression in triple-receptor negative breast cancer (TNBC) can help the design of novel and personalized therapies and prognostic assessments. Abnormal RNA modification is a recently discovered process in TNBC development. TNBC samples from The Cancer Genome Atlas [...] Read more.
Understanding the causes of tumorigenesis and progression in triple-receptor negative breast cancer (TNBC) can help the design of novel and personalized therapies and prognostic assessments. Abnormal RNA modification is a recently discovered process in TNBC development. TNBC samples from The Cancer Genome Atlas database were categorized according to the expression level of NAT10, which drives acetylation of cytidine in RNA to N(4)-acetylcytidine (ac4C) and affects mRNA stability. A total of 703 differentially expressed long non-coding RNAs (lncRNAs) were found between high- and low-expressed NAT10 groups in TNBC. Twenty of these lncRNAs were significantly associated with prognosis. Two breast cancer tissues and their paired normal tissues were sequenced at the whole genome level using acetylated RNA immunoprecipitation sequencing (acRIP-seq) technology to identify acetylation features in TNBC, and 180 genes were significantly differentially ac4c acetylated in patients. We also analyzed the genome-wide lncRNA expression profile and constructed a co-expression network, containing 116 ac4C genes and 1080 lncRNAs. Three of these lncRNAs were prognostic risk lncRNAs affected by NAT10 and contained in the network. The corresponding reciprocal pairs were “LINC01614-COL3A1”, “OIP5-AS1-USP8”, and “RP5-908M14.9-TRIR”. These results indicate that RNA ac4c acetylation involves lncRNAs and affects the tumor process and prognosis of TNBC. This will aid the prediction of drug targets and drug sensitivity. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

15 pages, 5240 KiB  
Article
Excavation of Molecular Subtypes of Endometrial Cancer Based on DNA Methylation
by Yujie Liu, Yue Gu, Mengyan Zhang, Jiaqi Zeng, Yangyang Wang, Hongli Wang, Xueting Liu, Sijia Liu, Zhao Wang, Yuan Wang, Le Wang and Yunyan Zhang
Genes 2022, 13(11), 2106; https://doi.org/10.3390/genes13112106 - 13 Nov 2022
Viewed by 1856
Abstract
Tumor heterogeneity makes the diagnosis and treatment of endometrial cancer difficult. As an important modulator of gene expression, DNA methylation can affect tumor heterogeneity and, therefore, provide effective information for clinical treatment. In this study, we explored specific prognostic clusters based on 482 [...] Read more.
Tumor heterogeneity makes the diagnosis and treatment of endometrial cancer difficult. As an important modulator of gene expression, DNA methylation can affect tumor heterogeneity and, therefore, provide effective information for clinical treatment. In this study, we explored specific prognostic clusters based on 482 examples of endometrial cancer methylation data in the TCGA database. By analyzing 4870 CpG clusters, we distinguished three clusters with different prognostics. Differences in DNA methylation levels are associated with differences in age, grade, clinical pathological staging, and prognosis. Subsequently, we screened out 264 specific hypermethylation and hypomethylation sites and constructed a prognostic model for Bayesian network classification, which corresponded to the classification of the test set to the classification results of the train set. Since the tumor microenvironment plays a key role in determining immunotherapy responses, we conducted relevant analyses based on clusters separated from DNA methylation data to determine the immune function of each cluster. We also predicted their sensitivity to chemotherapy drugs. Specific classifications of DNA methylation may help to address the heterogeneity of previously existing molecular clusters of endometrial cancer, as well as to develop more effective, individualized treatments. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

15 pages, 3010 KiB  
Article
Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic Pathway Signatures
by Ji Li, Jiayue Qiu, Junwei Han, Xiangmei Li and Ying Jiang
Genes 2022, 13(11), 1976; https://doi.org/10.3390/genes13111976 - 29 Oct 2022
Cited by 5 | Viewed by 1993
Abstract
Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In [...] Read more.
Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

14 pages, 5279 KiB  
Article
Classification and Prognosis Analysis of Pancreatic Cancer Based on DNA Methylation Profile and Clinical Information
by Xin Li, Xuan Zhang, Xiangyu Lin, Liting Cai, Yan Wang and Zhiqiang Chang
Genes 2022, 13(10), 1913; https://doi.org/10.3390/genes13101913 - 21 Oct 2022
Cited by 6 | Viewed by 2260
Abstract
Pancreatic adenocarcinoma (PAAD) has a poor prognosis with high individual variation in the treatment response among patients; however, there is no standard molecular typing method for PAAD prognosis in clinical practice. We analyzed DNA methylation data from The Cancer Genome Atlas database, which [...] Read more.
Pancreatic adenocarcinoma (PAAD) has a poor prognosis with high individual variation in the treatment response among patients; however, there is no standard molecular typing method for PAAD prognosis in clinical practice. We analyzed DNA methylation data from The Cancer Genome Atlas database, which identified 1235 differentially methylated DNA genes between PAAD and adjacent tissue samples. Among these, 78 methylation markers independently affecting PAAD prognosis were identified after adjusting for significant clinical factors. Based on these genes, two subtypes of PAAD were identified through consistent clustering. Fourteen specifically methylated genes were further identified to be associated with survival. Further analyses of the transcriptome data identified 301 differentially expressed cancer driver genes between the two PAAD subtypes and the degree of immune cell infiltration differed significantly between the subtypes. The 14 specific genes characterizing the unique methylation patterns of the subtypes were used to construct a Bayesian network-based prognostic prediction model for typing that showed good predictive value (area under the curve value of 0.937). This study provides new insight into the heterogeneity of pancreatic tumors from an epigenetic perspective, offering new strategies and targets for personalized treatment plan evaluation and precision medicine for patients with PAAD. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

13 pages, 2710 KiB  
Article
DNA Methylation-Specific Analysis of G Protein-Coupled Receptor-Related Genes in Pan-Cancer
by Mengyan Zhang, Jiyun Zhao, Huili Dong, Wenhui Xue, Jie Xing, Ting Liu, Xiuwen Yu, Yue Gu, Baoqing Sun, Haibo Lu and Yan Zhang
Genes 2022, 13(7), 1213; https://doi.org/10.3390/genes13071213 - 7 Jul 2022
Cited by 4 | Viewed by 2583
Abstract
Tumor heterogeneity presents challenges for personalized diagnosis and treatment of cancer. The identification method of cancer-specific biomarkers has important applications for the diagnosis and treatment of cancer types. In this study, we analyzed the pan-cancer DNA methylation data from TCGA and GEO, and [...] Read more.
Tumor heterogeneity presents challenges for personalized diagnosis and treatment of cancer. The identification method of cancer-specific biomarkers has important applications for the diagnosis and treatment of cancer types. In this study, we analyzed the pan-cancer DNA methylation data from TCGA and GEO, and proposed a computational method to quantify the degree of specificity based on the level of DNA methylation of G protein-coupled receptor-related genes (GPCRs-related genes) and to identify specific GPCRs DNA methylation biomarkers (GRSDMs) in pan-cancer. Then, a ridge regression-based method was used to discover potential drugs through predicting the drug sensitivities of cancer samples. Finally, we predicted and verified 8 GRSDMs in adrenocortical carcinoma (ACC), rectum adenocarcinoma (READ), uveal Melanoma (UVM), thyroid carcinoma (THCA), and predicted 4 GRSDMs (F2RL3, DGKB, GRK5, PIK3R6) which were sensitive to 12 potential drugs. Our research provided a novel approach for the personalized diagnosis of cancer and informed individualized treatment decisions. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Graphical abstract

12 pages, 2440 KiB  
Article
Identification of COVID-19-Associated DNA Methylation Variations by Integrating Methylation Array and scRNA-Seq Data at Cell-Type Resolution
by Guoliang Wang, Zhuang Xiong, Fei Yang, Xinchang Zheng, Wenting Zong, Rujiao Li and Yiming Bao
Genes 2022, 13(7), 1109; https://doi.org/10.3390/genes13071109 - 21 Jun 2022
Cited by 10 | Viewed by 2977
Abstract
Single-cell transcriptome studies have revealed immune dysfunction in COVID-19 patients, including lymphopenia, T cell exhaustion, and increased levels of pro-inflammatory cytokines, while DNA methylation plays an important role in the regulation of immune response and inflammatory response. The specific cell types of immune [...] Read more.
Single-cell transcriptome studies have revealed immune dysfunction in COVID-19 patients, including lymphopenia, T cell exhaustion, and increased levels of pro-inflammatory cytokines, while DNA methylation plays an important role in the regulation of immune response and inflammatory response. The specific cell types of immune responses regulated by DNA methylation in COVID-19 patients will be better understood by exploring the COVID-19 DNA methylation variation at the cell-type level. Here, we developed an analytical pipeline to explore single-cell DNA methylation variations in COVID-19 patients by transferring bulk-tissue-level knowledge to the single-cell level. We discovered that the methylation variations in the whole blood of COVID-19 patients showed significant cell-type specificity with remarkable enrichment in gamma-delta T cells and presented a phenomenon of hypermethylation and low expression. Furthermore, we identified five genes whose methylation variations were associated with several cell types. Among them, S100A9, AHNAK, and CX3CR1 have been reported as potential COVID-19 biomarkers previously, and the others (TRAF3IP3 and LFNG) are closely associated with the immune and virus-related signaling pathways. We propose that they might serve as potential epigenetic biomarkers for COVID-19 and could play roles in important biological processes such as the immune response and antiviral activity. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

13 pages, 6365 KiB  
Article
Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases
by Dongli Zhao, Zhe Zhang, Zhonghuang Wang, Zhenglin Du, Meng Wu, Tingting Zhang, Jialu Zhou, Wenming Zhao and Yuanguang Meng
Genes 2022, 13(6), 935; https://doi.org/10.3390/genes13060935 - 24 May 2022
Cited by 5 | Viewed by 3344
Abstract
Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, [...] Read more.
Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

13 pages, 1314 KiB  
Article
Tissue-Specific Variations in Transcription Factors Elucidate Complex Immune System Regulation
by Hengwei Lu, Yi-Ching Tang and Assaf Gottlieb
Genes 2022, 13(5), 929; https://doi.org/10.3390/genes13050929 - 23 May 2022
Cited by 1 | Viewed by 3000
Abstract
Gene expression plays a key role in health and disease. Estimating the genetic components underlying gene expression can thus help understand disease etiology. Polygenic models termed “transcriptome imputation” are used to estimate the genetic component of gene expression, but these models typically consider [...] Read more.
Gene expression plays a key role in health and disease. Estimating the genetic components underlying gene expression can thus help understand disease etiology. Polygenic models termed “transcriptome imputation” are used to estimate the genetic component of gene expression, but these models typically consider only the cis regions of the gene. However, these cis-based models miss large variability in expression for multiple genes. Transcription factors (TFs) that regulate gene expression are natural candidates for looking for additional sources of the missing variability. We developed a hypothesis-driven approach to identify second-tier regulation by variability in TFs. Our approach tested two models representing possible mechanisms by which variations in TFs can affect gene expression: variability in the expression of the TF and genetic variants within the TF that may affect the binding affinity of the TF to the TF-binding site. We tested our TF models in whole blood and skeletal muscle tissues and identified TF variability that can partially explain missing gene expression for 1035 genes, 76% of which explains more than the cis-based models. While the discovered regulation patterns were tissue-specific, they were both enriched for immune system functionality, elucidating complex regulation patterns. Our hypothesis-driven approach is useful for identifying tissue-specific genetic regulation patterns involving variations in TF expression or binding. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Figure 1

Review

Jump to: Research

21 pages, 1830 KiB  
Review
Nucleosome-Omics: A Perspective on the Epigenetic Code and 3D Genome Landscape
by Siyuan Kong, Yuhui Lu, Shuhao Tan, Rongrong Li, Yan Gao, Kui Li and Yubo Zhang
Genes 2022, 13(7), 1114; https://doi.org/10.3390/genes13071114 - 22 Jun 2022
Cited by 8 | Viewed by 5409
Abstract
Genetic information is loaded on chromatin, which involves DNA sequence arrangement and the epigenetic landscape. The epigenetic information including DNA methylation, nucleosome positioning, histone modification, 3D chromatin conformation, and so on, has a crucial impact on gene transcriptional regulation. Out of them, nucleosomes, [...] Read more.
Genetic information is loaded on chromatin, which involves DNA sequence arrangement and the epigenetic landscape. The epigenetic information including DNA methylation, nucleosome positioning, histone modification, 3D chromatin conformation, and so on, has a crucial impact on gene transcriptional regulation. Out of them, nucleosomes, as basal chromatin structural units, play an important central role in epigenetic code. With the discovery of nucleosomes, various nucleosome-level technologies have been developed and applied, pushing epigenetics to a new climax. As the underlying methodology, next-generation sequencing technology has emerged and allowed scientists to understand the epigenetic landscape at a genome-wide level. Combining with NGS, nucleosome-omics (or nucleosomics) provides a fresh perspective on the epigenetic code and 3D genome landscape. Here, we summarized and discussed research progress in technology development and application of nucleosome-omics. We foresee the future directions of epigenetic development at the nucleosome level. Full article
(This article belongs to the Special Issue DNA and RNA Epigenetics and Transcriptomics Research)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-12
Back to TopTop