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

2008 ◽  
Vol 36 (04) ◽  
pp. 783-797 ◽  
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.

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

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.

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

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 22-22
Author(s):  
Ellen K. Kendall ◽  
Manishkumar S. Patel ◽  
Sarah Ondrejka ◽  
Agrima Mian ◽  
Yazeed Sawalha ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Gene Sets

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.

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

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.

scholarly journals Maternal Obesity Promotes a Proinflammatory Signature in Rat Uterus and Blastocyst

Endocrinology ◽  
2011 ◽  
Vol 152 (11) ◽  
pp. 4158-4170 ◽  
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.

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

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.

scholarly journals Expression Profile Analysis Identifies Key Genes as Prognostic Markers for Metastasis of Osteosarcoma

2020 ◽  
Author(s):  
Xiaoqing Guan ◽  
Zhiyuan Guan ◽  
Jiafu Ji ◽  
Chunli Song
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Prognostic Markers ◽  
Profile Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Key Genes

Abstract Background : Osteosarcoma (OS) is the most common malignant tumor of bone which was featured with osteoid or immature bone produced by the malignant cells, and biomarkers are urgently needed to identify patients with this aggressive disease. Methods : We downloaded gene expression profiles from Gene Expression Omnibus (GEO) and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets for OS, respectively, and performed weighted gene co-expression network analysis (WGCNA) to identify the key module. Whereafter, functional annotation and Gene Set Enrichment Analysis (GSEA) demonstrated the relationships between target genes and OS. Results : In this study, we discovered four key genes – ALOX5AP, HLA-DMB, HLA-DRA and SPINT2 as new prognostic markers and confirmed their relationship with OS metastasis in the validation set. Conclusions : Overall, our work may shed light on the roles of ALOX5AP, HLA-DMB, HLA-DRA and SPINT2, thus providing valuable clues to investigate the metastasis of OS and corroborating the potential clinical application value of the 4-gene signature to some extent.

scholarly journals Glycolysis-related gene expression profiling serves as a novel prognosis risk predictor for human hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
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

AbstractMetabolic pattern reconstruction is an important factor in tumor progression. Metabolism of tumor cells is characterized by abnormal increase in anaerobic glycolysis, regardless of high oxygen concentration, resulting in a significant accumulation of energy from glucose sources. These changes promotes rapid cell proliferation and tumor growth, which is further referenced a process known as the Warburg effect. The current study reconstructed the metabolic pattern in progression of cancer to identify genetic changes specific in cancer cells. A total of 12 common types of solid tumors were included in the current study. Gene set enrichment analysis (GSEA) was performed to analyze 9 glycolysis-related gene sets, which are implicated in the glycolysis process. Univariate and multivariate analyses were used to identify independent prognostic variables for construction of a nomogram based on clinicopathological characteristics and a glycolysis-related gene prognostic index (GRGPI). The prognostic model based on glycolysis genes showed high area under the curve (AUC) in LIHC (Liver hepatocellular carcinoma). The findings of the current study showed that 8 genes (AURKA, CDK1, CENPA, DEPDC1, HMMR, KIF20A, PFKFB4, STMN1) were correlated with overall survival (OS) and recurrence-free survival (RFS). Further analysis showed that the prediction model accurately distinguished 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 showed good discrimination based on internal and external cohorts. These findings indicate that changes in expression level of metabolic genes implicated in glycolysis can contribute to reconstruction of tumor-related microenvironment.

scholarly journals Sex differences in expression of immune elements emerge in children, young adults and mice with osteosarcoma

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lauren J. Mills ◽  
Logan G. Spector ◽  
David A. Largaespada ◽  
Lindsay A. Williams
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Sleeping Beauty ◽  
Gene Set Enrichment Analysis ◽  
M2 Macrophage ◽  
Cancer Therapies ◽  
Underlying Mechanisms

Abstract Background Males < 40 years old are more likely to be diagnosed with and die from osteosarcoma (OS). The underlying mechanisms may depend on sex differences in immune response. Methods We used SEER data to estimate survival differences between males and females aged < 40 years at OS diagnosis. In NCI TARGET-OS cases, we determined sex differences in gene expression, conducted Gene Set Enrichment Analysis (GSEA), and applied the LM22 signature to identify biologic sex differences. We compared sex differences in gene expression profiles in TARGET-OS to those observed in Sleeping Beauty (SB) transposon mutagenesis accelerated Trp53R270H-mutant mouse-OS and healthy adult osteoblasts. Results Males had worse 17-year overall survival than females (SEER p < 0.0001). From 87 TARGET-OS cases, we observed 1018 genes and 69 pathways that differed significantly by sex (adjusted p < 0.05). Pathway and gene lists overlapped with those from mice (p = 0.03) and healthy osteoblasts (p = 0.017), respectively. Pathways that differed significantly by sex were largely immune-based and included the PD-1/PD-L1 immunotherapy pathway. We observed sex differences in M2 macrophages (LM22; p = 0.056) and M1-M2 macrophage transition (GSEA; p = 0.037) in TARGET-OS. LM22 trends were similar in mice. Twenty-four genes differentially expressed by sex in TARGET-OS had existing cancer therapies. Conclusions Sex differences in OS gene expression were similar across species and centered on immune pathways. Identified sex-specific therapeutic targets may improve outcomes in young individuals with OS.

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