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

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.

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Immune-Related DNA Methylation Data-Based Molecular Classification Associated with the Prognosis of Patients with Hepatocellular Carcinoma

10.21203/rs.3.rs-531042/v1 ◽

2021 ◽

Author(s):

Xiong-Wen Wang ◽

Qian Yan ◽

Bao-Qian Ye ◽

Bo-Qing Wang ◽

Wen-Jiang Zheng

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Dna Methylation ◽

Immune Cells ◽

Molecular Subtypes ◽

Molecular Subtype ◽

Expression Profiles ◽

Training Set ◽

Cpg Sites ◽

Immune Related Genes

Abstract Background: The combination of epigenetic drugs and immunotherapy should be able to develop an optimal treatment plan for hepatocellular carcinoma (HCC), yet its mechanism is still in the preliminary exploration stage. 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. Methods: 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. Univariate and multivariate survival analysis was used for selecting the significant methylation sites, and the consensus clustering was performed to find the best molecular subtype associated with the survival of HCC. Next, we used the least absolute shrinkage and selection operator (LASSO) algorithm to construct a prognostic-related model and performed internal verification. Finally, we explored the levels of 16 immune-related genes expression correlate with the infiltration levels of immune cells in HCC. Results: By performing consistent clustering analysis on 830 immune-related CpG sites in 231 samples of a training set, 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. Conclusions: 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.

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Age-related differences in monocyte DNA methylation and immune function in healthy Kenyan adults and children

Immunity & Ageing ◽

10.1186/s12979-021-00223-2 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Katherine R. Dobbs ◽

Paula Embury ◽

Emmily Koech ◽

Sidney Ogolla ◽

Stephen Munga ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Young Adults ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Innate Immune ◽

Inflammatory Gene Expression ◽

Cpg Sites ◽

Age Related ◽

Adults And Children

Abstract Background Age-related changes in adaptive and innate immune cells have been associated with a decline in effective immunity and chronic, low-grade inflammation. Epigenetic, transcriptional, and functional changes in monocytes occur with aging, though most studies to date have focused on differences between young adults and the elderly in populations with European ancestry; few data exist regarding changes that occur in circulating monocytes during the first few decades of life or in African populations. We analyzed DNA methylation profiles, cytokine production, and inflammatory gene expression profiles in monocytes from young adults and children from western Kenya. Results We identified several hypo- and hyper-methylated CpG sites in monocytes from Kenyan young adults vs. children that replicated findings in the current literature of differential DNA methylation in monocytes from elderly persons vs. young adults across diverse populations. Differentially methylated CpG sites were also noted in gene regions important to inflammation and innate immune responses. Monocytes from Kenyan young adults vs. children displayed increased production of IL-8, IL-10, and IL-12p70 in response to TLR4 and TLR2/1 stimulation as well as distinct inflammatory gene expression profiles. Conclusions These findings complement previous reports of age-related methylation changes in isolated monocytes and provide novel insights into the role of age-associated changes in innate immune functions.

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Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

10.21203/rs.3.rs-59448/v1 ◽

2020 ◽

Author(s):

Rui Zhang ◽

Chen Chen ◽

Qi Li ◽

Jialu Fu ◽

Dong Zhang ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

High Risk ◽

Risk Score ◽

Risk Model ◽

Predictive Accuracy ◽

Expression Profiles ◽

Gene Expression Profiles ◽

The Cancer Genome Atlas ◽

Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were signiﬁcantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

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Identification of distinctive patterns in cell signaling pathways in glioblastoma multiforme subtypes using gene expression TCGA data sets.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.e23162 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. e23162-e23162

Author(s):

Konstantin Volyanskyy ◽

Minghao Zhong ◽

Payal Keswarpu ◽

John T Fallon ◽

Michael Paul Fanucchi ◽

...

Keyword(s):

Gene Expression ◽

Cell Signaling ◽

Signaling Pathways ◽

Normal Tissue ◽

Gene Selection ◽

Molecular Subtype ◽

Expression Profiles ◽

Gene Expression Profiles ◽

The Cancer Genome Atlas ◽

Cell Signaling Pathways

e23162 Background: Cancer is characterized by a variety of heterogeneous genomic and transcriptomic patterns involving highly complex signaling biological pathways. The problem of identification of the factors driving tumor progression becomes even more challenging due to intricate interaction mechanisms between these pathways. Using novel approaches in machine learning, we demonstrate the ability to quantitatively describe characteristic signaling patterns in cancer based on transcriptomic data Methods: We used RNASeq data from 20531 genes in 174 samples of GBM from The Cancer Genome Atlas including 5 major histological subtypes – Classical, G-CIMP, Mesenchymal, Neural, and Proneural, anddeveloped predictive computational framework for molecular subtype differentiation from normal tissue relying on variance based gene selection and random forest algorithm. Results: We obtained a few key findings – (1) genes from cell signaling pathways alone differentiate each subtype from normal tissue with 100% accuracy; (2) predictive genes are specific to each subtype; (3) inferred pathway interactions are also specific to each subtype; (4) typically most of the predictive genes involved in signaling are down-regulated in tumor compared to normal tissue (MAPT, PRKCG, PDE2A, RYR2, ATP1B1, GRN1, GNAO1), however, in each subtype we observed a smaller subset of predictive genes which are highly up-regulated in tumor (ID3, FN1, JAG1, F2R, COL4A1, EDAR, CDK2, CDK4, MFNG, BIRC5, CCNB2). We detected and quantitatively evaluated characteristic signaling pathway involvement across the GBM subtypes for MAPK, RAP1, RAS, Notch, PI3K-Akt, mTOR, FoxO, Jak-STAT, Wnt, cAMP, and Calcium Signaling, providing a unique approximation for each subtype signaling profile. Conclusions: In this study, we identified gene expression profiles and associated signaling pathways for distinguishing GBM Multiforme subtypes from normal tissue. We observed and described a dense complex picture of interacting signaling pathways. The detected interactions may provide clinical insights and could be used to identify potential therapeutic targets, however, more research is needed to confirm this.

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

Bioscience Reports ◽

10.1042/bsr20201053 ◽

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.

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Integrative Analysis of DNA Methylation and Gene Expression Profiles Identifies Colorectal Cancer-Related Diagnostic Biomarkers

Pathology & Oncology Research ◽

10.3389/pore.2021.1609784 ◽

2021 ◽

Vol 27 ◽

Author(s):

Mingyue Xu ◽

Lijun Yuan ◽

Yan Wang ◽

Shuo Chen ◽

Lin Zhang ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Dna Methylation ◽

Logistic Regression Model ◽

Cpg Island ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Training Set ◽

Diagnostic Biomarkers ◽

Cpg Sites

Background: Colorectal cancer (CRC) is a common human malignancy worldwide. The prognosis of patients is largely frustrated by delayed diagnosis or misdiagnosis. DNA methylation alterations have been previously proved to be involved in CRC carcinogenesis.Methods: In this study, we proposed to identify CRC-related diagnostic biomarkers by analyzing DNA methylation and gene expression profiles. TCGA-COAD datasets downloaded from the Cancer Genome Atlas (TCGA) were used as the training set to screen differential expression genes (DEGs) and methylation CpG sites (dmCpGs) in CRC samples. A logistic regression model was constructed based on hyper-methylated CpG sites which were located in downregulated genes for CRC diagnosis. Another two independent datasets from the Gene Expression Omnibus (GEO) were used as a testing set to evaluate the performance of the model in CRC diagnosis.Results: We found that CpG island methylator phenotype (CIMP) was a potential signature of poor prognosis by dividing CRC samples into CIMP and noCIMP groups based on a set of CpG sites with methylation standard deviation (sd) > 0.2 among CRC samples and low methylation levels (mean β < 0.05) in adjacent samples. Hyper-methylated CpGs tended to be more closed to CpG island (CGI) and transcription start site (TSS) relative to hypo-methylated CpGs (p-value < 0.05, Fisher exact test). A logistic regression model was finally constructed based on two hyper-methylated CpGs, which had an area under receiver operating characteristic curve of 0.98 in the training set, and 0.85 and 0.95 in the two independent testing sets.Conclusions: In conclusion, our study identified promising DNA methylation biomarkers for CRC diagnosis.

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Identification and Validation of a Predictive Immune-Related Genes Signature for the Prognosis of Cholangiocarcinoma

10.21203/rs.3.rs-31517/v1 ◽

2020 ◽

Author(s):

Rui Zhang ◽

Chen Chen ◽

Qi Li ◽

Jialu Fu ◽

Dong Zhang ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

High Risk ◽

Risk Score ◽

Risk Model ◽

Predictive Accuracy ◽

Expression Profiles ◽

Gene Expression Profiles ◽

The Cancer Genome Atlas ◽

Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data. Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score. Results: We identified that 223 IRGs were signiﬁcantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells. Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

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Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer

Nucleic Acids Research ◽

10.1093/nar/gkt643 ◽

2013 ◽

Vol 41 (18) ◽

pp. 8464-8474 ◽

Cited By ~ 40

Author(s):

J.-K. Rhee ◽

K. Kim ◽

H. Chae ◽

J. Evans ◽

P. Yan ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Dna Methylation ◽

Molecular Subtypes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Integrated Analysis ◽

Genome Wide

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Integrating Genetic and Transcriptomic Data to Reveal Pathogenesis and Prognostic Markers of Pancreatic Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.747270 ◽

2021 ◽

Vol 12 ◽

Author(s):

Kaisong Bai ◽

Tong Zhao ◽

Yilong Li ◽

Xinjian Li ◽

Zhantian Zhang ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Adenocarcinoma ◽

Somatic Mutation ◽

Somatic Mutations ◽

Prognostic Markers ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Genomic Variation ◽

The Cancer Genome Atlas ◽

Driver Genes

Pancreatic adenocarcinoma (PAAD) is one of the deadliest malignancies and mortality for PAAD have remained increasing under the conditions of substantial improvements in mortality for other major cancers. Although multiple of studies exists on PAAD, few studies have dissected the oncogenic mechanisms of PAAD based on genomic variation. In this study, we integrated somatic mutation data and gene expression profiles obtained by high-throughput sequencing to characterize the pathogenesis of PAAD. The mutation profile containing 182 samples with 25,470 somatic mutations was obtained from The Cancer Genome Atlas (TCGA). The mutation landscape was generated and somatic mutations in PAAD were found to have preference for mutation location. The combination of mutation matrix and gene expression profiles identified 31 driver genes that were closely associated with tumor cell invasion and apoptosis. Co-expression networks were constructed based on 461 genes significantly associated with driver genes and the hub gene FAM133A in the network was identified to be associated with tumor metastasis. Further, the cascade relationship of somatic mutation-Long non-coding RNA (lncRNA)-microRNA (miRNA) was constructed to reveal a new mechanism for the involvement of mutations in post-transcriptional regulation. We have also identified prognostic markers that are significantly associated with overall survival (OS) of PAAD patients and constructed a risk score model to identify patients’ survival risk. In summary, our study revealed the pathogenic mechanisms and prognostic markers of PAAD providing theoretical support for the development of precision medicine.

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