scholarly journals Integrative Analysis Reveals Comprehensive Altered Metabolic Genes Linking with Tumor Epigenetics Modification in Pan-Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yahui Shi ◽  
Jinfen Wei ◽  
Zixi Chen ◽  
Yuchen Yuan ◽  
Xingsong Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Histone Acetylation ◽  
Epigenetic Modification ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Altered Expression ◽  
Metabolic Genes ◽  
Cancer Types ◽  
Pan Cancer

Background. Cancer cells undergo various rewiring of metabolism and dysfunction of epigenetic modification to support their biosynthetic needs. Although the major features of metabolic reprogramming have been elucidated, the global metabolic genes linking epigenetics were overlooked in pan-cancer. Objectives. Identifying the critical metabolic signatures with differential expressions which contributes to the epigenetic alternations across cancer types is an urgent issue for providing the potential targets for cancer therapy. Method. The differential gene expression and DNA methylation were analyzed by using the 5726 samples data from the Cancer Genome Atlas (TCGA). Results. Firstly, we analyzed the differential expression of metabolic genes and found that cancer underwent overall metabolism reprogramming, which exhibited a similar expression trend with the data from the Gene Expression Omnibus (GEO) database. Secondly, the regulatory network of histone acetylation and DNA methylation according to altered expression of metabolism genes was summarized in our results. Then, the survival analysis showed that high expression of DNMT3B had a poorer overall survival in 5 cancer types. Integrative altered methylation and expression revealed specific genes influenced by DNMT3B through DNA methylation across cancers. These genes do not overlap across various cancer types and are involved in different function annotations depending on the tissues, which indicated DNMT3B might influence DNA methylation in tissue specificity. Conclusions. Our research clarifies some key metabolic genes, ACLY, SLC2A1, KAT2A, and DNMT3B, which are most disordered and indirectly contribute to the dysfunction of histone acetylation and DNA methylation in cancer. We also found some potential genes in different cancer types influenced by DNMT3B. Our study highlights possible epigenetic disorders resulting from the deregulation of metabolic genes in pan-cancer and provides potential therapy in the clinical treatment of human cancer.

scholarly journals Pan-Cancer Genome-Wide DNA Methylation Analyses Revealed That Hypermethylation Influences 3D Architecture and Gene Expression Dysregulation in HOXA Locus During Carcinogenesis of Cancers

2021 ◽  
Vol 9 ◽  
Author(s):  
Gang Liu ◽  
Zhenhao Liu ◽  
Xiaomeng Sun ◽  
Xiaoqiong Xia ◽  
Yunhe Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cell Lines ◽  
Dna Interaction ◽  
Gene Expression Omnibus ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Topological Domains ◽  
Cancer Types ◽  
Pan Cancer

DNA methylation dysregulation during carcinogenesis has been widely discussed in recent years. However, the pan-cancer DNA methylation biomarkers and corresponding biological mechanisms were seldom investigated. We identified differentially methylated sites and regions from 5,056 The Cancer Genome Atlas (TCGA) samples across 10 cancer types and then validated the findings using 48 manually annotated datasets consisting of 3,394 samples across nine cancer types from Gene Expression Omnibus (GEO). All samples’ DNA methylation profile was evaluated with Illumina 450K microarray to narrow down the batch effect. Nine regions were identified as commonly differentially methylated regions across cancers in TCGA and GEO cohorts. Among these regions, a DNA fragment consisting of ∼1,400 bp detected inside the HOXA locus instead of the boundary may relate to the co-expression attenuation of genes inside the locus during carcinogenesis. We further analyzed the 3D DNA interaction profile by the publicly accessible Hi-C database. Consistently, the HOXA locus in normal cell lines compromised isolated topological domains while merging to the domain nearby in cancer cell lines. In conclusion, the dysregulation of the HOXA locus provides a novel insight into pan-cancer carcinogenesis.

Regulation of gene expression by DNA methylation with cytotoxic T lymphocytes evaluation in consensus molecular subtypes of colorectal cancer.

2020 ◽  
Vol 38 (5_suppl) ◽  
pp. 25-25
Author(s):  
Yuanyuan Shen ◽  
Justin Hummel ◽  
Isabel Cristina Trindade ◽  
Christos Papageorgiou ◽  
Chi-Ren Shyu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Epigenetic Modification ◽  
Pearson Correlation ◽  
Critical Role ◽  
Regulation Of Gene Expression ◽  
The Cancer Genome Atlas ◽  
Highly Correlated

25 Background: Low cytotoxic T lymphocyte (CTLs) infiltration in colorectal cancer (CRC) tumors is a challenge to treatment with immune checkpoint inhibitors. Consensus molecular subtypes (CMS) classify patients based on tumor attributes, and CMS1 patients include the majority of patients with high CTL infiltration and “inflamed” tumors. Epigenetic modification plays a critical role in gene expression and therapy resistance. Therefore, in this study we compared DNA methylation, gene expression, and CTL infiltration of CMS1 patients to other CMS groups to determine targets for improving immunotherapy in CRC. Methods: RNA-seq (n = 511) and DNA methylation (n = 316) from The Cancer Genome Atlas databases were used to determine gene expression and methylation profiles based on CMSs. CMS1 was used as a reference and compared to other subtypes (CMS2-4). Microenvironment Cell Populations- counter (MCPcounter) was used to determine tumor CTL infiltration. Genes with significantly different expression (p < 0.01, LogFC≥|1.5|) and difference of mean methylation β value ≥|0.25| were integrated for Pearson correlation coefficient analysis with MCPcounter score (r > |0.7|). Results: Comparing CMS1 and CMS2, ARHGAP9, TBX21, and LAG3 were differentially methylated and correlated with CTL scores. ARHGAP9 and TBX21 were decreased and hypomethylated in CMS2. Comparing CMS1 and CMS3, ARHGAP9, TBX21, FMNL1, HLA-DPB1, and STX11 were downregulated in CMS3 and highly correlated with CTL scores. ARHGAP9, FMNL1, HLA-DPB1, and STX11 were hypomethylated in CMS3 and TBX21 was methylated in both, but had a higher methylation ratio in CMS1. Comparing CMS1 and CMS4, TBX21 was the only gene downregulated, hypomethylated, and highly correlated with CTL scores in CMS4 patients. Conclusions: We found six genes differentially expressed, differentially methylated, and highly correlated with CTL infiltration when comparing CMS1 to other CMS groups. Specifically, TBX21 was the only gene highly correlated with CTL scores with differential gene expression and methylation in CMS2-4 when compared to CMS1. Thus, T-bet may be a critical regulator of T cell responses in CRC.

scholarly journals Systematic Pan-Cancer Analysis Identifies TREM2 as an Immunological and Prognostic Biomarker

2021 ◽  
Vol 12 ◽  
Author(s):  
Xin Cheng ◽  
Xiaowei Wang ◽  
Kechao Nie ◽  
Lin Cheng ◽  
Zheyu Zhang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Cell ◽  
Lung Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Immunoglobulin Superfamily ◽  
Cancer Types ◽  
Pan Cancer

Triggering receptor expressed on myeloid cells-2 (TREM2) is a transmembrane receptor of the immunoglobulin superfamily and a crucial signaling hub for multiple pathological pathways that mediate immunity. Although increasing evidence supports a vital role for TREM2 in tumorigenesis of some cancers, no systematic pan-cancer analysis of TREM2 is available. Thus, we aimed to explore the prognostic value, and investigate the potential immunological functions, of TREM2 across 33 cancer types. Based on datasets from The Cancer Genome Atlas, and the Cancer Cell Line Encyclopedia, Genotype Tissue-Expression, cBioPortal, and Human Protein Atlas, we employed an array of bioinformatics methods to explore the potential oncogenic roles of TREM2, including analyzing the relationship between TREM2 and prognosis, tumor mutational burden (TMB), microsatellite instability (MSI), DNA methylation, and immune cell infiltration of different tumors. The results show that TREM2 is highly expressed in most cancers, but present at low levels in lung cancer. Further, TREM2 is positively or negatively associated with prognosis in different cancers. Additionally, TREM2 expression was associated with TMB and MSI in 12 cancer types, while in 20 types of cancer, there was a correlation between TREM2 expression and DNA methylation. Six tumors, including breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, kidney renal clear cell carcinoma, lung squamous cell carcinoma, skin cutaneous melanoma, and stomach adenocarcinoma, were screened out for further study, which demonstrated that TREM2 gene expression was negatively correlated with infiltration levels of most immune cells, but positively correlated with infiltration levels of M1 and M2 macrophages. Moreover, correlation with TREM2 expression differed according to T cell subtype. Our study reveals that TREM2 can function as a prognostic marker in various malignant tumors because of its role in tumorigenesis and tumor immunity.

scholarly journals Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Gaojianyong Wang ◽  
Dimitris Anastassiou
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
The Cancer Genome Atlas ◽  
Copy Number Alterations ◽  
Multiple Cancer ◽  
Cancer Driver ◽  
Large Gene ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Pan Cancer

Abstract Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.

scholarly journals Glutamine Metabolism Regulators Associated with Cancer Development and the Tumor Microenvironment: A Pan-Cancer Multi-Omics Analysis

Genes ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1305
Author(s):  
Jingwen Zou ◽  
Kunpeng Du ◽  
Shaohua Li ◽  
Lianghe Lu ◽  
Jie Mei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Drug Sensitivity ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Metabolic Reprogramming ◽  
Glutamine Metabolism ◽  
Molecular Networks ◽  
Cancer Types ◽  
Pan Cancer

Background: In recent years, metabolic reprogramming has been identified as a hallmark of cancer. Accumulating evidence suggests that glutamine metabolism plays a crucial role in oncogenesis and the tumor microenvironment. In this study, we aimed to perform a systematic and comprehensive analysis of six key metabolic node genes involved in the dynamic regulation of glutamine metabolism (referred to as GLNM regulators) across 33 types of cancer. Methods: We analyzed the gene expression, epigenetic regulation, and genomic alterations of six key GLNM regulators, including SLC1A5, SLC7A5, SLC3A2, SLC7A11, GLS, and GLS2, in pan-cancer using several open-source platforms and databases. Additionally, we investigated the impacts of these gene expression changes on clinical outcomes, drug sensitivity, and the tumor microenvironment. We also attempted to investigate the upstream microRNA–mRNA molecular networks and the downstream signaling pathways involved in order to uncover the potential molecular mechanisms behind metabolic reprogramming. Results: We found that the expression levels of GLNM regulators varied across cancer types and were related to several genomic and immunological characteristics. While the immune scores were generally lower in the tumors with higher gene expression, the types of immune cell infiltration showed significantly different correlations among cancer types, dividing them into two clusters. Furthermore, we showed that elevated GLNM regulators expression was associated with poor overall survival in the majority of cancer types. Lastly, the expression of GLNM regulators was significantly associated with PD-L1 expression and drug sensitivity. Conclusions: The elevated expression of GLNM regulators was associated with poorer cancer prognoses and a cold tumor microenvironment, providing novel insights into cancer treatment and possibly offering alternative options for the treatment of clinically refractory cancers.

scholarly journals Pan-Cancer Analysis of DNA Methylation Identifies Genes and Biological Functions Associated with Overall Survival

2021 ◽  
Author(s):  
Romola Grace Cavet ◽  
Peng Yue ◽  
Guy Lawrence Cavet
Keyword(s):  
Dna Methylation ◽  
Overall Survival ◽  
Cancer Biology ◽  
Gene Clusters ◽  
The Cancer Genome Atlas ◽  
Biological Functions ◽  
Cancer Types ◽  
Genomic Regions ◽  
The Relationship ◽  
Pan Cancer

DNA methylation influences gene expression and is altered in many cancers, but the relationship between DNA methylation and cancer outcomes is not yet fully understood. If methylation of specific genes is associated with better or worse outcomes, it could implicate genes in driving cancer and suggest therapeutic strategies. To advance our understanding of DNA methylation in cancer biology, we conducted a pan-cancer analysis of the relationship between methylation and overall survival. Using data on 28 tumor types from The Cancer Genome Atlas (TCGA), we identified genes and genomic regions whose methylation was recurrently associated with survival across multiple cancer types. While global DNA methylation levels are associated with outcome in some cancers, we found that the gene-specific associations were largely independent of these global effects. Genes with recurrent associations across cancer types were enriched for certain biological functions, such as immunity and cell-cell adhesion. While these recurrently associated genes were found throughout the genome, they were enriched in certain genomic regions, which may further implicate certain gene families and gene clusters in affecting survival. By finding common features across cancer types, our results link DNA methylation to patient outcomes, identify biological mechanisms that could explain survival differences, and support the potential value of treatments that modulate the methylation of tumor DNA.

scholarly journals Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaonan Liu ◽  
Pei Wang ◽  
Xufei Teng ◽  
Zhang Zhang ◽  
Shuhui Song
Keyword(s):  
Dna Methylation ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Critical Role ◽  
Expression Regulation ◽  
The Cancer Genome Atlas ◽  
Aberrant Expression ◽  
Human Cancers ◽  
Cancer Types ◽  
Pan Cancer

BackgroundN6-methyladenosine (m6A), the most abundant chemical modification on eukaryotic messenger RNA (mRNA), is modulated by three class of regulators namely “writers,” “erasers,” and “readers.” Increasing studies have shown that aberrant expression of m6A regulators plays broad roles in tumorigenesis and progression. However, it is largely unknown regarding the expression regulation for RNA m6A regulators in human cancers.ResultsHere we characterized the expression profiles of RNA m6A regulators in 13 cancer types with The Cancer Genome Atlas (TCGA) data. We showed that METTL14, FTO, and ALKBH5 were down-regulated in most cancers, whereas YTHDF1 and IGF2BP3 were up-regulated in 12 cancer types except for thyroid carcinoma (THCA). Survival analysis further revealed that low expression of several m6A regulators displayed longer overall survival times. Then, we analyzed microRNA (miRNA)-regulated and DNA methylation-regulated expression changes of m6A regulators in pan-cancer. In total, we identified 158 miRNAs and 58 DNA methylation probes (DMPs) involved in expression regulation for RNA m6A regulators. Furthermore, we assessed the survival significance of those regulatory pairs. Among them, 10 miRNAs and 7 DMPs may promote cancer initiation and progression; conversely, 3 miRNA/mRNA pairs in kidney renal clear cell carcinoma (KIRC) may exert tumor-suppressor function. These findings are indicative of their potential prognostic values. Finally, we validated two of those miRNA/mRNA pairs (hsa-miR-1307-3p/METTL14 and hsa-miR-204-5p/IGF2BP3) that could serve a critical role for potential clinical application in KIRC patients.ConclusionsOur findings highlighted the importance of upstream regulation (miRNA and DNA methylation) governing m6A regulators’ expression in pan-cancer. As a result, we identified several informative regulatory pairs for prognostic stratification. Thus, our study provides new insights into molecular mechanisms of m6A modification in human cancers.

scholarly journals Prognostic Value and Immune Characteristics of RUNX Family in Human Cancers: A Pan-cancer Analysis

2021 ◽  
Author(s):  
Han Zhao ◽  
Yun Chen ◽  
Peijun Shen ◽  
Lan Gong
Keyword(s):  
Gene Expression ◽  
Gene Family ◽  
Immune Cell ◽  
Malignant Tumors ◽  
Genetic Alterations ◽  
Genetic Mutation ◽  
The Cancer Genome Atlas ◽  
Different Types ◽  
Cancer Types ◽  
Pan Cancer

Abstract Background: Runt‑related transcription factors (RUNX) are involved in numerous fundamental biological processes and play crucial parts in tumorigenesis and metastasis both directly and indirectly. However, the pan-cancer evidence of RUNX gene family is no available. Methods: In this study, we analyzed the potential association between RUNX gene family expression and patient’s prognosis, immune cell infiltration, drug response, and genetic mutation data across different types of tumors using based on The Cancer Genome Atlas, Gene Expression Omnibus, and Oncomine database. Results: The results showed that the expression of the RUNX family varied among different cancer types, revealing its heterogeneity in cancers, and that expression of RUNX2 was lower than that of RUNX1 and RUNX3 across all cancer types. RUNX family gene expression was related to prognosis in several cancers. Furthermore, our study revealed a clear association between RUNX family expression and ESTIMATE score, RNA stemness, and DNA stemness scores. Compared with RUNX1 and RUNX2, RUNX3 showed relatively low levels of genetic alterations. RUNX family genes had clear associations with immune infiltrate subtypes, and their expression was positively related to immune checkpoint genes and drug sensitivity in most cases. Conclusions: These findings will help to elucidate the potential oncogenic roles of RUNX family genes in different types of cancer and it can function as a prognostic marker in various malignant tumors.

scholarly journals Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning–based neural network

GigaScience ◽  
2020 ◽  
Vol 9 (7) ◽  
Author(s):  
Xiang Zhou ◽  
Hua Chai ◽  
Huiying Zhao ◽  
Ching-Hsing Luo ◽  
Yuedong Yang
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Dna Methylation ◽  
Transfer Learning ◽  
Epigenetic Modification ◽  
Imputation Accuracy ◽  
Methylation Data ◽  
Rna Seq ◽  
Cancer Dataset ◽  
Pan Cancer

Abstract Background Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality of DNA methylation data. Because DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on a single cancer dataset and do not fully utilize information from large pan-cancer datasets. Results Here, we have developed a novel method to impute missing gene expression data from DNA methylation data through a transfer learning–based neural network, namely, TDimpute. In the method, the pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized for training a general model, which was then fine-tuned on the specific cancer dataset. By testing on 16 cancer datasets, we found that our method significantly outperforms other state-of-the-art methods in imputation accuracy with a 7–11% improvement under different missing rates. The imputed gene expression was further proved to be useful for downstream analyses, including the identification of both methylation–driving and prognosis-related genes, clustering analysis, and survival analysis on the TCGA dataset. More importantly, our method was indicated to be useful for general purposes by an independent test on the Wilms tumor dataset from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. Conclusions TDimpute is an effective method for RNA-seq imputation with limited training samples.

scholarly journals Imputing missing RNA-seq data from DNA methylation by using transfer learning based neural network

2019 ◽  
Author(s):  
Xiang Zhou ◽  
Hua Chai ◽  
Huiying Zhao ◽  
Ching-Hsing Luo ◽  
Yuedong Yang
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Dna Methylation ◽  
Transfer Learning ◽  
Epigenetic Modification ◽  
Imputation Accuracy ◽  
Methylation Data ◽  
Rna Seq ◽  
Cancer Dataset ◽  
Pan Cancer

AbstractBackgroundGene expression plays a key intermediate role in linking molecular features at DNA level and phenotype. However, due to various limitations in experiments, the RNA-seq data is missing in many samples while there exists high-quality of DNA methylation data. As DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on single cancer dataset, and do not fully utilize information from large pan-cancer dataset.ResultsHere, we have developed a novel method to impute missing gene expression data from DNA methylation data through transfer learning-based neural network, namely TDimpute. In the method, the pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized for training a general model, which was then fine-tuned on the specific cancer dataset. By testing on 16 cancer datasets, we found that our method significantly outperforms other state-of-the-art methods in imputation accuracy with 7%-11% increase under different missing rates. The imputed gene expression was further proved to be useful for downstream analyses, including the identification of both methylation-driving and prognosis-related genes, clustering analysis, and survival analysis on the TCGA dataset. More importantly, our method was indicated to be useful for general purpose by the independent test on the Wilms tumor dataset from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) project.ConclusionsTDimpute is an effective method for RNA-seq imputation with limited training samples.

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