Integrative DNA Methylation and Gene Expression Analysis Reveals Candidate Biomarkers Associated with Dichotomized Response to Chemoimmunotherapy in Diffuse Large B-Cell Lymphoma

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. While 60% of DLBCL patients achieve complete remission with frontline therapy, relapsed/refractory (R/R) DLBCL patients have a poor prognosis with median overall survival below one year, necessitating investigation into the biological principles that distinguish cured from R/R DLBCL. Recent analyses have identified unfavorable molecular signatures when accounting for gene expression, copy number alterations and mutational profiles in R/R DLBCL. However, an integrative analysis of the relationship between epigenetic and transcriptomic changes has yet to be described. In this study, we compared baseline methylation and gene expression profiles of DLBCL patients with dichotomized clinical outcomes. Methods: Diagnostic DLBCL biopsies were obtained from two patient cohorts: patients who relapsed or were refractory following chemoimmunotherapy ("R/R"), and patients who entered durable clinical remission following therapy ("cured"). The median age for R/R and cured cohorts were 62 (range 35-86) years vs. 64 (range 28-83) years (P= 0.27). High-intermediate or high IPI scores were present in 14 vs. 6 patients (P= 0.08) in the R/R and cured cohorts, respectively. All patients were treated with frontline R-CHOP or R-EPOCH. DNA and RNA were extracted simultaneously from formalin-fixed, paraffin embedded biopsy samples. An Illumina 850k Methylation Array was used to identify DNA methylation levels in 29 R/R patients and 20 cured patients. RNA sequencing was performed on 9 R/R patients and 7 cured patients at diagnosis using Illumina HiSeq4000. Differentially methylated probes were identified using the DMRcate package, and differentially expressed genes were identified using the DESeq2 package. Gene set enrichment analysis was performed using canonical pathway gene sets from MSigDB. Results: At the time of diagnosis, we found significant epigenetic and transcriptomic differences between cured and R/R patients. Comparing cured to R/R samples, there were 8,159 differentially methylated probes (FDR<0.05). Differentially methylated regions between R/R and cured cohorts overlap with genes previously identified as mutation hotspots in DLBCL. Upon comparing transcriptomic profiles between R/R and cured, 267 genes were found to be differentially expressed (Log2FC>|1| and FDR<0.05). Gene set enrichment analysis revealed gene sets related to cell cycle, membrane trafficking, Rho and Rab family GTPase function, and transcriptional regulation were upregulated in the R/R samples. Gene sets related to innate immune signaling, Type I and II interferon signaling, fatty acid and carbohydrate metabolism were upregulated in the cured samples. To identify genes likely to be regulated by specific changes in methylation, we selected genes that were both differentially expressed and differentially methylated between the R/R and cured cohorts. In the R/R samples, 13 genes (ARMC5, ARRDC1, C12orf57, CCSER1, D2HGDH, DUOX2, FAM189B, FKBP2, KLF5, MFSD10, NEK8, NT5C, and WDR18) were significantly hypermethylated and underexpressed when compared to cured specimens, suggesting that epigenetic silencing of these genes is associated with lack of response to chemoimmunotherapy. In contrast, 12 genes (ATP2B1, C15orf41, FAM102B, FAM3C, FHOD3, FYTTD1, GPR180, KIAA1841, LRMP, MEF2A, RRAS2, and TPD52) were significantly hypermethylated and underexpressed in cured patients, suggesting that epigenetic silencing of these genes is favorable for treatment response. Many of these epigenetically modified genes have been previously implicated in cancer biology, including roles in NOTCH signaling, chromosomal instability, and biomarkers of prognosis. Conclusions: This is the first integrative epigenetic and transcriptomic analysis of diagnostic biopsies from cured and R/R DLBCL patients following chemoimmunotherapy. At the time of diagnosis, both the methylation and gene expression profiles significantly differ between patients that enter durable remission as opposed to those who are R/R to therapy. Soon, the hypomethylating agent CC-486 (i.e. oral azacitidine) will be explored in combination with mini-R-CHOP for older DLBCL patients in whom DNA methylation is likely increased. These data support the use of hypomethylating agents to potentially restore sensitivity of DLBCL to chemoimmunotherapy. Disclosures Hsi: Eli Lilly: Research Funding; Abbvie: Research Funding; Miltenyi: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; CytomX: Consultancy, Honoraria. Hill:Celgene: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Takeda: Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding.

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Gene Expression Profiles Document That Recently- and Previously-Divided CLL Fractions Represent a Continuum but Suggest Differing Modes of Activation for These Fractions in U-CLL and M-CLL

Blood ◽

10.1182/blood.v120.21.317.317 ◽

2012 ◽

Vol 120 (21) ◽

pp. 317-317

Author(s):

Xiao J. Yan ◽

Wentian Li ◽

Sophia Yancopoulos ◽

Igor Dozmorov ◽

Carlo Calissano ◽

...

Keyword(s):

Gene Expression ◽

Cell Death ◽

Cell Signaling ◽

Lymphoid Tissue ◽

Research Funding ◽

Expression Profiles ◽

Lymphocytic Leukemia ◽

Gene Set Enrichment Analysis ◽

P Value ◽

Gene Sets

Abstract Abstract 317 By using reciprocal densities of surface membrane CXCR4 and CD5, chronic lymphocytic leukemia (CLL) B cells can be divided into 3 fractions indicating time since last division (proliferative, intermediate, and resting). It has been suggested that cells in these fractions represent a continuum from resting to intermediate to proliferative. In this study, we made intraclonal gene expression profile (GEP) comparisons of these fractions from 17 CLL patients to try to confirm this notion and interclonal comparisons between U-CLL and M-CLL patients to determine if pathways involved in the actions of these fractions differed between patient subgroups. PBMCs from 8 U-CLL and 9 M-CLL patients were sorted into 3 fractions (CD19+CD3−CD5hiCXCR4lo, PROLIF), (CD19+CD3−CD5intCXCR4int, INTERM), and (CD19+CD3−CD5loCXCR4hi, REST); RNA was purified from each, and gene expression microarrays using Illumina HumanHT12 beadchips performed. To determine differentially expressed genes in intraclonal comparisons, expression value ratios for fractions from each patient were computed, log-transformed, and Student t-test performed using R (www.r-project.org); for interclonal comparisons, raw GEP data between subpopulations were compared: U-PROLIF and M-PROLIF, and U-REST and M-REST. Sets of significant genes (≥1.5 fold change and P<0.01) were analyzed using Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment Analysis (GSEA). Upon plotting intraclonal average log ratios of PROLIF/INTERM vs INTERM/REST, it was clear that gene expression levels changed in the same direction, i.e. PROLIF>INTERM>REST, or PROLIF<INTERM<REST, consistent with a continuum between the 3 fractions. Within this pattern, 36 genes were significant for both plotted ratios. Of these, 29 were overexpressed, along with CD5; CD68, ITGAX, CCND2, CRIP1 and LGALS1 were the highest. Functional analysis using IPA showed these genes to be related to NFkB signaling and cell trafficking. Seven genes (ADARB1, BACH2, CNTNAP2, HRK, RHPN2, PRPML, and RXPA) were significantly downregulated, along with CXCR4. Next we characterized GEP differences between the PROLIF and REST fractions, identifying 390 genes up-regulated in PROLIF and 244 in REST. The top 5 upregulated PROLIF genes were CD68, LY96, ITGAX, CCND2 and CRIP1, and the top 5 REST genes were BACH2, CXCR4, ADARB1, RHPN2 and HRK. Functionally, the upregulated PROLIF genes were related to BCR signaling, cytokines (IFNa, IL12), NFkB, and Akt, whereas the upregulated REST genes related to BCL2, cell death and cell movement. By GSEA, 813/881 gene sets, defined by expression neighborhoods centered on cancer associated genes, were upregulated in the PROLIF with 436 gene sets significant at a false discovery rate (FDR) <10%; 206 sets were significantly enriched with p value <0.01. For the REST, 68/881 gene sets were upregulated, with none significant even at FDR <25%. Finally, we examined PROLIF and REST fractions from U-CLL vs M-CLL patients. In this interclonal analysis, 93 genes were significantly different between U-PROLIF and M-PROLIF. The top 5 in U-PROLIF were MSI2, TGFBR3, TP53I3, RGCC and IGSF3, and the top 5 in M-PROLIF were MTSS1, BACE2, BRI3BP, AP3B1 and UBE2G2. Similarly, there were 125 genes that were significantly different between U-REST and M-REST. The top 5 in U-REST were DUSP26, CLEC2B, MDK, and EGR2 and in M-REST were NAPSA, RAB24, TARDBP, KCNN4 and ADD3. Interestingly, U-PROLIF and M-PROLIF differed in pathway assignments, with upregulated genes in U-PROLIF contributing to cell signaling and activation, particularly implicating Akt, ERK and P38MAPK. The intraclonal gene GEP analysis on these 3 fractions confirms that CLL clones contain a spectrum of cells that transition in a sequential manner from PROLIF to INTERM to REST fractions. Functional analyses show that genes upregulated in PROLIF correlate with cell signaling and proliferation, while genes upregulated in REST relate to cell death. Thus the PROLIF fraction is enriched in recently divided cells that likely exit from lymphoid tissue and the REST in older, less vital cells that either traffic to lymphoid tissue or die. The interclonal analysis implies that the stimuli and/or the responses of cells in the PROLIF and REST fractions differ between U-CLL and M-CLL. This last novel finding suggests either distinct cells of origin or distinct activation pathways for the IGHV-defined CLL subsets. Disclosures: Barrientos: gilead and pharmacyclics research funding: Research Funding.

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Relationship Between San-Huang-Xie-Xin-Tang and Its Herbal Components on the Gene Expression Profiles in HepG2 Cells

The American Journal of Chinese Medicine ◽

10.1142/s0192415x08006235 ◽

2008 ◽

Vol 36 (04) ◽

pp. 783-797 ◽

Cited By ~ 43

Author(s):

Wen-Yu Cheng ◽

Shih-Lu Wu ◽

Chien-Yun Hsiang ◽

Chia-Cheng Li ◽

Tung-Yuan Lai ◽

...

Keyword(s):

Gene Expression ◽

Hepg2 Cells ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Major Effect ◽

Gene Set Enrichment Analysis ◽

P53 Signaling ◽

The Relationship ◽

Herbal Components

Traditional Chinese medicine (TCM) has been used for thousands of years. Most Chinese herbal formulae consist of several herbal components and have been used to treat various diseases. However, the mechanisms of most formulae and the relationship between formulae and their components remain to be elucidated. Here we analyzed the putative mechanism of San-Huang-Xie-Xin-Tang (SHXXT) and defined the relationship between SHXXT and its herbal components by microarray technique. HepG2 cells were treated with SHXXT or its components and the gene expression profiles were analyzed by DNA microarray. Gene set enrichment analysis indicated that SHXXT and its components displayed a unique anti-proliferation pattern via p53 signaling, p53 activated, and DNA damage signaling pathways in HepG2 cells. Network analysis showed that most genes were regulated by one molecule, p53. In addition, hierarchical clustering analysis showed that Rhizoma Coptis shared a similar gene expression profile with SHXXT. These findings may explain why Rhizoma Coptis is the principle herb that exerts the major effect in the herbal formula, SHXXT. Moreover, this is the first report to reveal the relationship between formulae and their herbal components in TCM by microarray and bioinformatics tools.

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A Nomogram Based on Glycolysis-related Gene Expression Profilings Serve as a Novel Prognosis Risk Classifiers for Human Hepatocellular Carcinoma

10.21203/rs.3.rs-324874/v1 ◽

2021 ◽

Author(s):

Lingyu Zhang ◽

Yu Li ◽

Yibei Dai ◽

Danhua Wang ◽

Xuchu Wang ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Expression Profiles ◽

Area Under The Curve ◽

Prognostic Index ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Metabolic Pattern

Abstract Metabolic pattern reconstruction is an important element in tumor progression. The metabolism of tumor cells is characterized by the abnormal increase of anaerobic glycolysis, regardless of the higher oxygen concentration, resulting in a large accumulation of energy from glucose sources, and contributes to rapid cell proliferation and tumor growth which is further referenced as the Warburg effect. We tried to reconstruct the metabolic pattern in the progression of cancer to screen which genetic changes are specific in cancer cells. A total of 12 common types of solid tumors were enrolled in the prospective study. Gene set enrichment analysis (GSEA) was implemented to analyze 9 glycolysis-related gene sets, which are closely related to the glycolysis process. Univariate and multivariate analyses were used to identify independent prognostic variables for the construction of a nomogram based on clinicopathological characteristics and a glycolysis-related gene prognostic index (GRGPI). The prognostic model based on glycolysis genes has the highest area under the curve (AUC) in LIHC (Liver hepatocellular carcinoma). 8-gene signatures (AURKA, CDK1, CENPA, DEPDC1, HMMR, KIF20A, PFKFB4, STMN1) were related to overall survival (OS) and recurrence-free survival (RFS). Further analysis demonstrates that the prediction model can accurately distinguish between high- and low-risk cancer patients among patients in different clusters in LIHC. A nomogram with a well-fitted calibration curve based on gene expression profiles and clinical characteristics improves discrimination in internal and external cohorts. Furthermore, the altering expression of metabolic genes related to glycolysis may contribute to the reconstruction of the tumor-related microenvironment.

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DNA Methylation Profiling of Myeloma Trial Patients Reveals Specific Epigenetic Changes Associated with Outcome

Blood ◽

10.1182/blood.v128.22.804.804 ◽

2016 ◽

Vol 128 (22) ◽

pp. 804-804

Author(s):

David C Johnson ◽

Dil B Begum ◽

Sidra Ellis ◽

Amy L Sherborne ◽

Amy Price ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Research Funding ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Differentially Methylated Regions ◽

Disease Biology ◽

High Risk Disease ◽

Pathway Analyses ◽

Support Research

Abstract Introduction Epigenetic dysregulation is a hallmark of cancer and has significant impact on disease biology. The epigenetic structure of myeloma is heterogeneous and we previously demonstrated that gene specific DNA methylation changes are associated with outcome, using low-resolution arrays. We now performed a high-resolution genome wide DNA methylation analysis of a larger group of patients from a UK national phase III study to further define the role of epigenetic modifications in disease behaviour and outcome. Patients and Methods Highly purified (>95%) CD138+ myeloma bone marrow cells from 465 newly diagnosed patients enrolled in the UK NCRI Myeloma XI study were analysed. The extracted DNA was bisulfite-converted using the EZ DNA methylation kit (Zymo) and hybridized to Infinium HumanMethylation450 BeadChip arrays. Raw data was processed using the R Bioconductor package "minfi". SNP containing probes and probes on the sex chromosomes were removed. 464 samples and 441293 probes were retained following inspection of quality control metrics. Beta values were summarized across functional genomic units or differentially methylated regions (DMRs) that included: gene bodies, promoters, insulators, CpG-islands and enhancers. K-means was applied to each DMR to cluster patients into 2 groups (high or low methylation) per region. Filters were applied to define a clinically meaningful minimum group size and methylation differences between the groups. Overall survival (OS) and progression free survival (PFS) were assessed by a Cox proportional hazards regression model fitted to each DMR with a time-dependent covariate of the trial pathway. Pathway analyses were performed using GREAT (Stanford University) and GSEA (Broad Institute). Results We identified 589 differentially methylated regions that were significantly associated with PFS and OS when using a cut-off of P<0.01 (log-rank). Of these, 114 DMRs were located within 10kb of a gene transcription start site (TSS). Among these, several genes implicated in myeloma disease biology, such as immune cell-cell interaction genes (e.g. CD226) or stemness-associated transcription factors (e.g. PAX4) were identified to be differentially methylated. Using pathway analysis on all 589 DMRs, Gene Ontology biologic groups were enriched for positive regulation of proliferation, cell migration and cytoskeleton organisation (FDR P<0.05). This was further supported by enrichment of proliferative E2F1 transcription factor target structures (FDR P<0.05). Matched gene expression profiles have been generated and integrated analyses correlating epigenetic with GEP and genetic risk data and individual gene level methylation-expression associations will be presented at the meeting. This data is also being integrated with drug resistance profiles from the Cancer Cell Line Encyclopedia (CCLE; Barretina, et al, 2016). Conclusion Epigenetic mechanisms play a significant role in influencing tumour cell behaviour. We have identified here differentially methylated regions that are significantly associated with patient outcome. Pathway analyses suggest an epigenetic regulation of biologic mechanisms involved in high risk disease, such as proliferation and migration. Integration of epigenetic data with matched gene expression profiles is currently ongoing to delineate independent epigenetic biomarkers associated with high risk disease behaviour. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Takeda Oncology: Consultancy; Celgene: Consultancy, Honoraria, Other: Travel Support. Jenner:Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: Travel support; Takeda: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding. Cook:Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria. Jackson:MSD: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau. Kaiser:Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Other: Travel Support; BMS: Consultancy, Other: Travel Support; Chugai: Consultancy.

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Identification of crucial genes and pathways associated with prostate cancer in multiple databases

Journal of International Medical Research ◽

10.1177/03000605211016624 ◽

2021 ◽

Vol 49 (6) ◽

pp. 030006052110166

Author(s):

Hanxu Guo ◽

Zhichao Zhang ◽

Yuhang Wang ◽

Sheng Xue

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Expression Profiles ◽

Malignant Neoplasm ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Core ◽

Ppi Networks ◽

Core Genes

Objective Prostate cancer (PCa) is a malignant neoplasm of the urinary system. This study aimed to use bioinformatics to screen for core genes and biological pathways related to PCa. Methods The GSE5957 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were constructed by R language. Furthermore, protein–protein interaction (PPI) networks were generated to predict core genes. The expression levels of core genes were examined in the Tumor Immune Estimation Resource (TIMER) and Oncomine databases. The cBioPortal tool was used to study the co-expression and prognostic factors of the core genes. Finally, the core genes of signaling pathways were determined using gene set enrichment analysis (GSEA). Results Overall, 874 DEGs were identified. Hierarchical clustering analysis revealed that these 24 core genes have significant association with carcinogenesis and development . LONRF1, CDK1, RPS18, GNB2L1 ( RACK1), RPL30, and SEC61A1 directly related to the recurrence and prognosis of PCa. Conclusions This study identified the core genes and pathways in PCa and provides candidate targets for diagnosis, prognosis, and treatment.

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Identification and Verification of Core Genes in Colorectal Cancer

BioMed Research International ◽

10.1155/2020/8082697 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Houxi Xu ◽

Yuzhu Ma ◽

Jinzhi Zhang ◽

Jialin Gu ◽

Xinyue Jing ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Malignant Neoplasm ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Core Genes

Colorectal cancer, a malignant neoplasm that occurs in the colorectal mucosa, is one of the most common types of gastrointestinal cancer. Colorectal cancer has been studied extensively, but the molecular mechanisms of this malignancy have not been characterized. This study identified and verified core genes associated with colorectal cancer using integrated bioinformatics analysis. Three gene expression profiles (GSE15781, GSE110223, and GSE110224) were downloaded from the Gene Expression Omnibus (GEO) databases. A total of 87 common differentially expressed genes (DEGs) among GSE15781, GSE110223, and GSE110224 were identified, including 19 upregulated genes and 68 downregulated genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for common DEGs using clusterProfiler. These common DEGs were significantly involved in cancer-associated functions and signaling pathways. Then, we constructed protein-protein interaction networks of these common DEGs using Cytoscape software, which resulted in the identification of the following 10 core genes: SST, PYY, CXCL1, CXCL8, CXCL3, ZG16, AQP8, CLCA4, MS4A12, and GUCA2A. Analysis using qRT-PCR has shown that SST, CXCL8, and MS4A12 were significant differentially expressed between colorectal cancer tissues and normal colorectal tissues (P<0.05). Gene Expression Profiling Interactive Analysis (GEPIA) overall survival (OS) has shown that low expressions of AQP8, ZG16, CXCL3, and CXCL8 may predict poor survival outcome in colorectal cancer. In conclusion, the core genes identified in this study contributed to the understanding of the molecular mechanisms involved in colorectal cancer development and may be targets for early diagnosis, prevention, and treatment of colorectal cancer.

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Maternal Obesity Promotes a Proinflammatory Signature in Rat Uterus and Blastocyst

Endocrinology ◽

10.1210/en.2010-1078 ◽

2011 ◽

Vol 152 (11) ◽

pp. 4158-4170 ◽

Cited By ~ 74

Author(s):

Kartik Shankar ◽

Ying Zhong ◽

Ping Kang ◽

Franchesca Lau ◽

Michael L. Blackburn ◽

...

Keyword(s):

Gene Expression ◽

Maternal Obesity ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Nuclear Factor Κb ◽

Gene Set Enrichment Analysis ◽

Nuclear Factor ◽

Fat Diets ◽

The Impact

Maternal obesity at conception increases the risk of offspring obesity, thus propagating an intergenerational vicious cycle. Male offspring born to obese dams are hyperresponsive to high fat-diets, gaining greater body weight, fat mass, and additional metabolic sequelae compared to lean controls. In this report, we identify the impact of maternal obesity before conception, on the embryo, and intrauterine milieu during the periimplantation period. We conducted global transcriptomic profiling in the uterus and periimplantation blastocyst, gene/protein expression analyses of inflammatory pathways in conjunction with endocrine and metabolic characterization in the dams at implantation. Uterine gene expression profiles of lean and obese dams revealed distinct signatures for genes regulating inflammation and lipid metabolism. Both pathway and gene-set enrichment analysis revealed uterine nuclear factor-κB and c-Jun N-terminal kinase signaling to be up-regulated in the uterus of obese dams, which was confirmed via immunoblotting. Obese uteri also evidenced an inflammatory secretome with higher chemokine mRNA abundance (CCL2, CCL5, CCL7, and CxCL10) and related regulators (TLR2, CD14, and Ccr1). Increased inflammation in the uterus was associated with ectopic lipid accumulation and expression of lipid metabolic genes. Gene expression in sex-identified male periimplantation blastocyst at day postcoitum 4.5 was clearly influenced by maternal obesity (359 transcripts, ±1.4-fold), including changes in developmental and epigenetic regulators. Akin to the uterus, nuclear factor-κB-regulated proinflammatory genes (CCL4 and CCL5) increased and expression of antioxidant (GPx3) and mitochondrial (TFAM and NRF1) genes decreased in the obese embryos. Our results suggest that ectopic lipid and inflammation may link maternal obesity to increased predisposition of offspring to obesity later in life.

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Identification of Candidate Biomarkers Correlated Poorer Prognosis of Breast Cancer via Bioinformatics Method

10.21203/rs.3.rs-402557/v1 ◽

2021 ◽

Author(s):

Gang Chen ◽

Mingwei Yu ◽

Jianqiao Cao ◽

Huishan Zhao ◽

Yuanping Dai ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Kegg Pathway ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Molecular Therapy ◽

Hub Genes

Abstract Background: Breast cancer (BC) is a malignancy with a high incidence among women in the world, and it is very urgent to identify significant biomarkers and molecular therapy methods.Methods: Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed. Additionally, hub genes were screened by constructing a protein-protein interaction (PPI) network. Then, we explored the prognostic values and molecular mechanism of these hub genes Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). Results: 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. GO and KEGG pathway analysis revealed that DEGs were mainly related to cell cycles and cell proliferation. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree of genes were selected, among which, 11 hub gene were significantly correlated with the prognosis of patients with BC. From GSEA reviewed correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. Conclusion: this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

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Gene Expression and Epigenetic Analysis in Relapsed/Refractory Diffuse Large B Cell Lymphoma Provides Insights into Evolution of Treatment Resistance to R-CHOP

Blood ◽

10.1182/blood-2020-138645 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 26-26

Author(s):

Manishkumar S. Patel ◽

Ellen K. Kendall ◽

Sarah Ondrejka ◽

Agrima Mian ◽

Yazeed Sawalha ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Differentially Expressed Genes ◽

Research Funding ◽

B Cell Lymphoma ◽

Cell Receptor ◽

B Cell Receptor ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

P Value

Background Diffuse large B cell lymphoma (DLBCL) is curable in ~60-70% of patients using standard chemoimmunotherapy, but the prognosis is poor for relapsed/refractory (R/R) DLBCL. Therefore, understanding the underlying molecular mechanisms will facilitate early prediction and effective management of resistance to therapy. Recent studies of paired diagnostic-relapse biopsies from patients have relied on a single "omics" approach, examining either gene expression or epigenetic evolution. Here we present a combined analysis of gene expression and DNA methylation profiles of paired diagnostic-relapse DLBCL biopsies to identify changes responsible for relapse after R-CHOP. Methods Biopsies from 23 DLBCL patients were obtained at the time of diagnosis and relapse following frontline R-CHOP chemoimmunotherapy. The cohort had 18 (78.3%) male patients with median age of 62 (range, 35-86) years and median IPI of 2.5 (range, 1-5). The median time from diagnosis to relapse was 7 (range, 0-57) months. DNA and RNA were extracted simultaneously from formalin-fixed paraffin embedded (FFPE) biopsy samples. DNA methylation levels were measured through Illumina 850k Methylation Array for 22 pairs of diagnostic-relapse biopsies. RNA from diagnostic-relapse paired biopsies from 6 patients was sequenced using Illumina HiSeq4000. Differentially methylated probes were identified using the DMRcate package, and differentially expressed genes were identified using the DESeq2 package. Gene set enrichment analysis was performed using canonical pathway gene sets from MSigDB. Pearson's correlation with a Bonferroni correction to the p-value was used to calculate the correlation between regularized log transformed gene expression counts and methylation beta values. Results In a pairwise comparison of gene expression between diagnostic and R/R biopsy pairs, we found 14 differentially expressed genes (FDR<0.1 & Log2FC>|1|) consistent across all pairs. Compared to gene expression at diagnosis, five genes (CYP1B1, LGR4, ATXN1, CTSC, ZMAT3) were downregulated, and eight genes (ERBB3, CD19, CARD11, MT-RNR2, IGHG3, CCDC88C, ATP2A3, CENPE, and PCNT) were up-regulated in the R/R samples. Many of these genes have been previously implicated in oncogenesis, such as ERBB3, a member of the epidermal growth receptor family. Importantly, some of these genes have known roles in DLBCL biology, such as CD19, a member of the B-cell receptor complex, and CARD11, a gene in which several oncogenic mutations have been identified in DLBCL as a mediator of NF-KB activation. Gene set enrichment analysis revealed overexpression of immune signatures such as cytokine-cytokine receptor interaction, chemokine receptor-chemokine binding, and the IL-12-STAT4 pathway at diagnosis. At relapse, cell cycle, B-cell receptor, and NOTCH signaling pathways were overexpressed. Interestingly, in a pairwise comparison of methylation between diagnostic and R/R biopsy pairs, there were no differentially methylated probes (FDR<0.05), suggesting no coordinated epigenetic evolution between diagnostic and R/R pairs. For biopsy pairs that had both gene expression and methylation data (5 pairs), we correlated gene expression and methylation values. We found that none of the differentially expressed genes between the diagnostic and R/R biopsies were significantly correlated with methylation status (adjusted p-value<0.05). Conclusions By analyzing paired diagnostic and relapse DLBCL biopsies, we found that at the time of relapse, there are significant transcriptomic changes but no significant epigenetic changes when compared to diagnostic biopsies. Activation of B-cell receptor and NOTCH signaling, as well as the loss of immune signaling at relapse, cannot be attributed to coordinated epigenetic changes in methylation. As the epigenetic profile of the biopsies did not consistently evolve, these data emphasize the need for better understanding of the baseline methylation profiles at the time of diagnosis, as well as acquired somatic mutations that may contribute to the emergence of therapeutic resistance. Future studies are needed to focus on how activation of signaling pathways triggered by genomic alterations can be targeted in relapsed/refractory DLBCL. Disclosures Hsi: Seattle Genetics: Consultancy, Honoraria; Miltenyi: Consultancy, Honoraria; Abbvie: Research Funding; Eli Lilly: Research Funding; CytomX: Consultancy, Honoraria. Hill:Takeda: Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding.

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Gene Expression Profiling of Structural and Functional High-Risk Multiple Myeloma

Blood ◽

10.1182/blood-2019-124637 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 3061-3061

Author(s):

Moritz Binder ◽

S. Vincent Rajkumar ◽

Martha Q. Lacy ◽

Jessica L. Haug ◽

Angela Dispenzieri ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

High Risk ◽

Research Funding ◽

Expression Profiles ◽

Prognostic Significance ◽

Gene Expression Profiles ◽

Differentially Expressed ◽

Cell Labeling ◽

Kaplan Meier

Introduction: High-risk multiple myeloma can be defined by the presence of specific cytogenetic abnormalities (structural) or by characteristic changes in bone marrow and peripheral blood biomarkers (functional). While both entities are characterized by therapeutic resistance, frequent disease relapses, and adverse survival outcomes, the underlying molecular mechanisms remain incompletely understood. Methods: We performed gene expression profiling (GEP) using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from 137 patients diagnosed with multiple myeloma between 2004 and 2012. All patients underwent Fluorescence In-situ Hybridization (FISH) evaluation, plasma cell labeling, International Staging System (ISS) risk stratification, and GEP prior to initiating treatment with novel agents. The presence of del(17p), t(4;14), t(14;16), and t(14;20) on FISH, a plasma cell labeling index (PCLI) > 2%, and ISS stage III were considered high-risk abnormalities: FISH-HR (n = 15, structural high-risk, at least one high-risk FISH lesion), PCLI-HR (n = 20; functional high-risk, PCLI > 2%), and ISS-HR (n = 12; functional high-risk, ISS stage III). For each HR group we sampled standard risk (SR) controls in a 4:1 ratio. After data quality control and normalization, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple comparisons using a false discovery rate-based approach for genome-wide experiments (q-value). We employed PANTHER pathway analysis for the differentially expressed genes in each HR group. We implemented a simple gene expression score (GES) by calculating the sum of quartiles of the normalized gene expression values for genes differentially expressed in more than one HR group (GES = ΣUP(quartile - 1) + ΣDN(4 - quartile)) and externally validated its prognostic significance (UAMS TT2 / TT3, GSE24080). Survival outcomes were analyzed using the methods described by Kaplan, Meier, and Cox. Computation and visualization were performed in R. Results: Median age at diagnosis was 63 years (32 - 87), 53% of the patients were male. High-risk disease was associated with inferior overall survival, regardless of the used definition (left Kaplan-Meier plots): FISH-HR (HR 4.3, 95% CI 1.9 - 9.8, p < 0.001), PCLI-HR (HR 2.7, 95% CI 1.4 - 5.3, p = 0.004), and ISS-HR (HR 2.8, 95% CI 1.2 - 6.5, p = 0.015). There were 59 (FISH-HR), 424 (ISS-HR), and 507 (PCLI-HR) differentially expressed genes (q < 0.050 for all genes, volcano plots). PCLI-HR and FISH-HR demonstrated a predominance of transcriptional up-regulation while ISS-HR had a balanced gene expression profile with a similar number of genes being up- and down-regulated. The involved cellular pathways were different across the HR groups except for anti-apoptotic signaling (bar graphs). All HR groups had distinct gene expression profiles with no complete overlap between all HR groups. There were 71 genes with overlap between two HR groups (69 up-regulated, 2 down-regulated, Venn diagrams). The median GES was 97 (18 - 206, higher numbers indicating higher expression of up-regulated and lower numbers of down-regulated high-risk genes) in 559 patients treated on UAMS TT2 / TT3 (GSE24080). Tertiles of the GES were associated with event-free survival (HR 1.4, 95% CI 1.2 - 1.6, p < 0.001) and remained independently prognostic after adjusting for age, sex, and ISS stage (HR 1.3, 95% CI 1.1 - 1.5, p < 0.001). Conclusions: High-risk multiple myeloma remains associated with inferior overall survival, regardless of the used definition (structural or functional). The subtypes of high-risk disease have distinct gene expression profiles and involve different cellular pathways, providing important clues to the underlying biology. A 71 gene signature derived from the different high-risk subtypes was of prognostic significance in a clinical trial population after adjusting for known prognostic factors. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Stewart:Takeda: Consultancy; Seattle Genetics: Consultancy; Roche: Consultancy; Ono: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

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