scholarly journals Comprehensive analysis based on DNA methylation and RNA-seq reveals hypermethylation of the up-regulated WT1 gene with potential mechanisms in PAM50 subtypes of breast cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11377
Author(s):  
Chongyang Ren ◽  
Xiaojiang Tang ◽  
Haitao Lan
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Mechanisms ◽  
Molecular Subtypes ◽  
Wilms Tumor ◽  
Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Body Region ◽  
Rna Seq

Background Breast cancer (BC), one of the most widespread cancers worldwide, caused the deaths of more than 600,000 women in 2018, accounting for about 15% of all cancer-associated deaths in women that year. In this study, we aimed to discover potential prognostic biomarkers and explore their molecular mechanisms in different BC subtypes using DNA methylation and RNA-seq. Methods We downloaded the DNA methylation datasets and the RNA expression profiles of primary tissues of the four BC molecular subtypes (luminal A, luminal B, basal-like, and HER2-enriched), as well as the survival information from The Cancer Genome Atlas (TCGA). The highly expressed and hypermethylated genes across all the four subtypes were screened. We examined the methylation sites and the downstream co-expressed genes of the selected genes and validated their prognostic value using a different dataset (GSE20685). For selected transcription factors, the downstream genes were predicted based on the Gene Transcription Regulation Database (GTRD). The tumor microenvironment was also evaluated based on the TCGA dataset. Results We found that Wilms tumor gene 1 (WT1), a transcription factor, was highly expressed and hypermethylated in all the four BC subtypes. All the WT1 methylation sites exhibited hypermethylation. The methylation levels of the TSS200 and 1stExon regions were negatively correlated with WT1 expression in two BC subtypes, while that of the gene body region was positively associated with WT1 expression in three BC subtypes. Patients with low WT1 expression had better overall survival (OS). Five genes including COL11A1, GFAP, FGF5, CD300LG, and IGFL2 were predicted as the downstream genes of WT1. Those five genes were dysregulated in the four BC subtypes. Patients with a favorable 6-gene signature (low expression of WT1 and its five predicted downstream genes) exhibited better OS than that with an unfavorable 6-gene signature. We also found a correlation between WT1 and tamoxifen using STITCH. Higher infiltration rates of CD8 T cells, plasma cells, and monocytes were found in the lower quartile WT1 group and the favorable 6-gene signature group. In conclusion, we demonstrated that WT1 is hypermethylated and up-regulated in the four BC molecular subtypes and a 6-gene signature may predict BC prognosis.

scholarly journals Identification and Validation of a Five-gene Signature Associated with Overall Survival in Breast Cancer Patients

2020 ◽  
Author(s):  
Xiaolong Wang ◽  
Chen Li ◽  
Tong Chen ◽  
Hanwen Zhang ◽  
Ying Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Individual Treatment ◽  
Breast Cancer Patients ◽  
Internal Validation ◽  
Novel Biomarkers

Abstract Background Recent years, attributed to early detection and new therapies, the mortality rates of breast cancer (BC) decreased. Nevertheless, the global prevalence was still high and the underlying molecular mechanisms were remained largely unknown. The investigation of prognosis-related genes as the novel biomarkers for diagnosis and individual treatment had become an urgent demand for clinical practice. Methods Gene expression profiles and clinical information of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training (n = 514) and internal validation (n = 562) cohort by using a random number table. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In the training set, the gene signature was constructed by the least absolute shrinkage and selection operator (LASSO) method based on DEGs screened by R packages. The results were further tested in the internal validation cohort and the entire cohort. Moreover, functions of five genes were explored by MTT, Colony-Formation, scratch and transwell assays. Western blot analysis was used to explore the mechanisms. Results In the training cohort, a total of 2805 protein coding DEGs were acquired through comparing breast cancer tissues (n = 514) with normal tissues (n = 113). A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1 and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the internal validation set and the entire set. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression. Conclusion We established a predictive five-gene signature, which could be helpful for prognosis assessment and personalized management in breast cancer patients.

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

Genetic and epigenetic modifications of HPDL and SOX17 associated with breast cancer prognosis: a study based on The Cancer Genome Atlas

2020 ◽  
Author(s):  
Chundi Gao ◽  
Huayao Li ◽  
Cun Liu ◽  
Jibiao Wu ◽  
Chao Zhou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Mechanisms ◽  
Cpg Islands ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Expression Of Genes ◽  
Number Variation ◽  
Patient Prognosis

Abstract Background：The high heterogeneity of breast cancer (BRCA) makes it more challenging to interpret the genetic variation mechanisms involved in BRCA pathogenesis and prognosis. Areas with high DNA methylation (such as CpG islands) were accompanied by copy number variation (CNV), and these genomic variations affected the level of DNA methylation. Methods: In this study, we characterized inter-tumor heterogeneity and analyzed the effects of CNV on DNA methylation and gene expression. In addition, we performed a Genetic Set Enrichment Analysis (GSEA) to identify key pathways for changes between patients with low and high expression of genes. Results: Our analysis found that the CNV of HPDL and SOX17 is not only related to the patient's prognosis, but also related to gene methylation and expression levels affecting the patient's survival time. Conclusion: This study provided an effective bioinformatics basis for further exploration of molecular mechanisms related to BRCA and assessment of patient prognosis, but the development of biomarkers for diagnosis and treatment still requires further clinical data validation.

scholarly journals Development and validation of prognostic markers in sarcomas base on a multi-omics analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yongchun Song ◽  
Kui Yang ◽  
Tuanhe Sun ◽  
Ruixiang Tang
Keyword(s):  
Dna Methylation ◽  
Copy Number ◽  
Molecular Mechanisms ◽  
Prognostic Markers ◽  
Molecular Subtypes ◽  
Comprehensive Analysis ◽  
The Cancer Genome Atlas ◽  
Molecular Characteristics ◽  
Dna Copy Number ◽  
Gain Loss

Abstract Background In sarcomas, the DNA copy number and DNA methylation exhibit genomic aberrations. Transcriptome imbalances play a driving role in the heterogeneous progression of sarcomas. However, it is still unclear whether abnormalities of DNA copy numbers are systematically related to epigenetic DNA methylation, thus, a comprehensive analysis of sarcoma occurrence and development from the perspective of epigenetic and genomics is required. Methods RNASeq, copy number variation (CNV), methylation data, clinical follow-up information were obtained from The Cancer Genome Atlas (TCGA) and GEO database. The association between methylation and CNV was analyzed to further identify methylation-related genes (MET-Gs) and CNV abnormality-related genes (CNV-Gs). Subsequently DNA copy number, methylation, and gene expression data associated with the MET-Gs and CNV-Gs were integrated to determine molecular subtypes and clinical and molecular characteristics of molecular subtypes. Finally, key biomarkers were determined and validated in independent validation sets. Results A total of 5354 CNV-Gs and 4042 MET-Gs were screened and showed a high degree of consistency. Four molecular subtypes (iC1, iC2, iC3, and iC4) with different prognostic significances were identified by multiomics cluster analysis, specifically, iC2 had the worst prognosis and iC4 indicated an immune-enhancing state. Three potential prognostic markers (ENO1, ACVRL1 and APBB1IP) were determined after comparing the molecular characteristics of the four molecular subtypes. The expression of ENO1 gene was significantly correlated with CNV, and was noticeably higher in iC2 subtype with the worst prognosis than any other subtypes. The expressions of ACVRL1 and APBB1IP were negatively correlated with methylation, and were high-expressed in the iC4 subtype with the most favorable prognosis. In addition, the number of silent/nonsilent mutations and neoantigens in iC2 subtype were significantly more than those in iC1/iC3/iC4 subtype, and the same trend was also observed in CNV Gain/Loss. Conclusion The current comprehensive analysis of genomic and epigenomic regulation provides new insights into multilayered pathobiology of sarcomas. Four molecular subtypes and three prognostic markers developed in this study improve the current understanding of the molecular mechanisms underlying sarcoma.

scholarly journals Integrative analysis of DNA methylation and gene expression profiles identified potential breast cancer-specific diagnostic markers

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xinhua Liu ◽  
Yonglin Peng ◽  
Ju Wang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Breast Cancer Patients ◽  
Gene Expressions ◽  
Normal Tissues

Abstract Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.

scholarly journals Analysis of LAGEs Family Gene Signature and Prognostic Relevance in Breast Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 726
Author(s):  
Hoang Dang Khoa Ta ◽  
Wan-Chun Tang ◽  
Nam Nhut Phan ◽  
Gangga Anuraga ◽  
Sz-Ying Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Family Members ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Breast Cancer Patients ◽  
Kaplan Meier

Breast cancer (BRCA) is one of the most complex diseases and involves several biological processes. Members of the L-antigen (LAGE) family participate in the development of various cancers, but their expressions and prognostic values in breast cancer remain to be clarified. High-throughput methods for exploring disease progression mechanisms might play a pivotal role in the improvement of novel therapeutics. Therefore, gene expression profiles and clinical data of LAGE family members were acquired from the cBioportal database, followed by verification using the Oncomine and The Cancer Genome Atlas (TCGA) databases. In addition, the Kaplan-Meier method was applied to explore correlations between expressions of LAGE family members and prognoses of breast cancer patients. MetaCore, GlueGo, and GluePedia were used to comprehensively study the transcript expression signatures of LAGEs and their co-expressed genes together with LAGE-related signal transduction pathways in BRCA. The result indicated that higher LAGE3 messenger (m)RNA expressions were observed in BRCA tissues than in normal tissues, and they were also associated with the stage of BRCA patients. Kaplan-Meier plots showed that overexpression of LAGE1, LAGE2A, LAGE2B, and LAGE3 were highly correlated to poor survival in most types of breast cancer. Significant associations of LAGE family genes were correlated with the cell cycle, focal adhesion, and extracellular matrix (ECM) receptor interactions as indicated by functional enrichment analyses. Collectively, LAGE family members’ gene expression levels were related to adverse clinicopathological factors and prognoses of BRCA patients; therefore, LAGEs have the potential to serve as prognosticators of BRCA patients.

scholarly journals Unraveling Immune-Related lncRNAs in Breast Cancer Molecular Subtypes

2021 ◽  
Vol 11 ◽  
Author(s):  
Carolina Mathias ◽  
João Carlos Degraf Muzzi ◽  
Bruna Borba Antunes ◽  
Daniela F. Gradia ◽  
Mauro A. A. Castro ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Response ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Therapy Response ◽  
The Cancer Genome Atlas ◽  
Luminal A ◽  
Specific Expression ◽  
Luminal B ◽  
Response Activation

Breast cancer (BRCA) is the most leading cause of cancer worldwide. It is a heterogeneous disease with at least five molecular subtypes including luminal A, luminal B, basal-like, HER2-enriched, and normal-like. These five molecular subtypes are usually stratified according to their mRNA profile patterns; however, ncRNAs are increasingly being used for this purpose. Among the ncRNAs class, the long non-coding RNAs (lncRNAs) are molecules with more than 200 nucleotides with versatile regulatory roles; and high tissue-specific expression profiles. The heterogeneity of BRCA can also be reflected regarding tumor microenvironment immune cells composition, which can directly impact a patient’s prognosis and therapy response. Using BRCA immunogenomics data from a previous study, we propose here a bioinformatics approach to include lncRNAs complexity in BRCA molecular and immune subtype. RNA-seq data from The Cancer Genome Atlas (TCGA) BRCA cohort was analyzed, and signal-to-noise ratio metrics were applied to create these subtype-specific signatures. Five immune-related signatures were generated with approximately ten specific lncRNAs, which were then functionally analyzed using GSEA enrichment and survival analysis. We highlighted here some lncRNAs in each subtype. LINC01871 is related to immune response activation and favorable overall survival in basal-like samples; EBLN3P is related to immune response suppression and progression in luminal B, MEG3, XXYLT1-AS2, and LINC02613 were related with immune response activation in luminal A, HER2-enriched and normal-like subtypes, respectively. In this way, we emphasize the need to know better the role of lncRNAs as regulators of immune response to provide new perspectives regarding diagnosis, prognosis and therapeutical targets in BRCA molecular subtypes.

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 Identifying Methylation-Driven Immune-related Molecular Subtypes Predicts the Prognosis of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Qian Yan ◽  
Baoqian Ye ◽  
Boqing Wang ◽  
Wenjiang Zheng ◽  
Xiongwen Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Cpg Sites ◽  
Prognosis Model ◽  
Immune Related Genes

Abstract The purpose of this study is to analyze the DNA methylation and gene expression profiles of immune-related CpG sites to identify the molecular subtypes and CpG sites related to the prognosis of HCC. In this study, the DNA methylation and gene expression datasets were downloaded from The Cancer Genome Atlas database, together with immune-related genes downloaded from the immunology database and analysis portal database to explore the prognostic molecular subtypes of HCC. By performing consistent clustering analysis on 830 immune-related CpG sites, we identified seven subgroups with significant differences in overall survival. Finally, 16 classifiers of immune-related CpG sites were constructed and used in the testing set to verify the prognosis of DNA methylation subgroups, and the results were consistent with the training set. Using the TIMER database, we analyzed 16 immune-related CpG sites expression with the abundance of six types of immune infiltrating cells and found that most are positively correlated with the level of infiltration of multiple immune cells in HCC. This study screened potential immune-related prognostic methylation sites and established a new prognosis model of HCC based on DNA methylation molecular subtype, which may help in the early diagnosis of HCC and developing more effective personalized treatments.

scholarly journals Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaolong Wang ◽  
Chen Li ◽  
Tong Chen ◽  
Wenhao Li ◽  
Hanwen Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Expression Profiles ◽  
External Validation ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Study Gene Expression

BackgroundRecent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.ResultsIn this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression.ConclusionsWe established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.

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