scholarly journals Immune Condition of Colorectal Cancer Patients Featured by Serum Chemokines and Gene Expressions of CD4+ Cells in Blood

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Takuya Komura ◽  
Masaaki Yano ◽  
Akimitsu Miyake ◽  
Hisashi Takabatake ◽  
Masaki Miyazawa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Parameters ◽  
Healthy Controls ◽  
Cd4 Cells ◽  
Gene Expressions ◽  
Expression Levels ◽  
Cd8 Cells

Background. Colorectal cancer (CRC), the most common malignancy worldwide, causes inflammation. We explored the inflammatory pathophysiology of CRC by assessing the peripheral blood parameters. Methods. The differences in gene expression profiles of whole blood cells and cell subpopulations between CRC patients and healthy controls were analyzed using DNA microarray. Serum cytokine/chemokine concentrations in CRC patients and healthy controls were measured via multiplex detection immunoassays. In addition, we explored correlations between the expression levels of certain genes of peripheral CD4+ cells and serum chemokine concentrations. Results. The gene expression profiles of peripheral CD4+ cells of CRC patients differed from those of healthy controls, but this was not true of CD8+ cells, CD14+ cells, CD15+ cells, or CD19+ cells. Serum IL-8 and eotaxin-1 levels were significantly elevated in CRC patients, and the levels substantially correlated with the expression levels of certain genes of CD4+ cells. Interestingly, the relationships between gene expression levels in peripheral CD4+ cells and serum IL-8 and eotaxin-1 levels resembled those of monocytes/macrophages, not T cells. Conclusions. Serum IL-8 and eotaxin-1 concentrations increased and were associated with changes in the gene expression of peripheral CD4+ cells in CRC patients.

scholarly journals Bioinformatics Analysis of Genes Upregulating Poor Prognosis In Colorectal Cancer And Gastric Cancer

2021 ◽  
Author(s):  
Ruizhi Dong ◽  
Shaodong Li ◽  
Bin Liang ◽  
Zhenhua Kang
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Gastric Cancer ◽  
Survival Rate ◽  
Poor Prognosis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Venn Diagram ◽  
Expression Levels ◽  
Significant Difference

Abstract Purpose : To analyze the up-regulated genes of poor prognosis in colorectal cancer and gastric cancer by bioinformatics. Methods: We searched the gene expression profiles GSE156355 and GSE64916 in colorectal cancer and gastric cancer tissues in NCBI-GEO. With P value < 0.05 and log2>1 as the standard, Venn diagram software was used to identify the common DEGs in the two data sets. Kaplan Meier plotter was used to analyze the survival rate data of common differentially expressed genes, draw and select survival curves, and analyze their expression levels. Results: A total of 97 genes were detected to be up-regulated in the two gene expression profiles. There were 19 genes in the prognosis of gastric cancer and 15 genes in the prognosis of colorectal cancer that had significant differences in the survival rate. Among them, KCNQ1, TRIM29, GART, MSX1, SNAI1, SUV39H2, LOXL2 and KCTD14 significantly decreased the survival rate of gastric cancer and colorectal cancer. The expression of MSX1 was the highest in gastric cancer. The expression level of KCTD14 was the highest in colorectal cancer, and there was no significant difference in the expression levels of other genes. Conclusion: There are 19 and 15 genes with significantly different prognostic viability in gastric cancer and colorectal cancer, respectively. The survival rates of KCNQ1, TRIM29, GART, Msx1, SNAI1, SUV39H2, LOXL2 and KCTD14 were significantly decreased in gastric cancer and colorectal cancer. The expression of MSX1 was the highest in gastric cancer. The expression of KCTD14 was the highest in colorectal cancer.

Gene Expression Profiles as Predictors of Poor Outcomes in Stage II Colorectal Cancer: A Systematic Review and Meta-analysis

2009 ◽  
Vol 8 (4) ◽  
pp. 207-214 ◽  
Author(s):  
An-Ting T. Lu ◽  
Shelley R. Salpeter ◽  
Anthony E. Reeve ◽  
Steven Eschrich ◽  
Patrick G. Johnston ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Stage Ii ◽  
Stage Ii Colorectal Cancer ◽  
Poor Outcomes

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

scholarly journals Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 584
Author(s):  
Sergii Babichev ◽  
Jiří Škvor
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Fuzzy Inference ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Quality Criterion ◽  
Gene Expressions ◽  
Inference System ◽  
Stepwise Procedure ◽  
Binary Classifiers

In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients’ health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient’s health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients’ gene expression profiles—the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps—in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers—logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient’s health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient’s health.

scholarly journals Granulosa-Lutein Cell Sirtuin Gene Expression Profiles Differ between Normal Donors and Infertile Women

2019 ◽  
Vol 21 (1) ◽  
pp. 295
Author(s):  
Rebeca González-Fernández ◽  
Rita Martín-Ramírez ◽  
Deborah Rotoli ◽  
Jairo Hernández ◽  
Frederick Naftolin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Localization ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cellular Protein ◽  
Control Group ◽  
Poor Responders ◽  
Male Factor ◽  
Expression Levels ◽  
Genes Expression

Sirtuins are a family of deacetylases that modify structural proteins, metabolic enzymes, and histones to change cellular protein localization and function. In mammals, there are seven sirtuins involved in processes like oxidative stress or metabolic homeostasis associated with aging, degeneration or cancer. We studied gene expression of sirtuins by qRT-PCR in human mural granulosa-lutein cells (hGL) from IVF patients in different infertility diagnostic groups and in oocyte donors (OD; control group). Study 1: sirtuins genes’ expression levels and correlations with age and IVF parameters in women with no ovarian factor. We found significantly higher expression levels of SIRT1, SIRT2 and SIRT5 in patients ≥40 years old than in OD and in women between 27 and 39 years old with tubal or male factor, and no ovarian factor (NOF). Only SIRT2, SIRT5 and SIRT7 expression correlated with age. Study 2: sirtuin genes’ expression in women poor responders (PR), endometriosis (EM) and polycystic ovarian syndrome. Compared to NOF controls, we found higher SIRT2 gene expression in all diagnostic groups while SIRT3, SIRT5, SIRT6 and SIRT7 expression were higher only in PR. Related to clinical parameters SIRT1, SIRT6 and SIRT7 correlate positively with FSH and LH doses administered in EM patients. The number of mature oocytes retrieved in PR is positively correlated with the expression levels of SIRT3, SIRT4 and SIRT5. These data suggest that cellular physiopathology in PR’s follicle may be associated with cumulative DNA damage, indicating that further studies are necessary.

Analysis of Gene Expression Profiles Reveals Novel Correlations With the Clinical Course of Colorectal Cancer

2007 ◽  
Vol 16 (11) ◽  
pp. 535-548 ◽  
Author(s):  
Duccio Cavalieri ◽  
Piero Dolara ◽  
Enrico Mini ◽  
Cristina Luceri ◽  
Cinzia Castagnini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Clinical Course ◽  
Expression Profiles ◽  
Gene Expression Profiles

Abstract 5711: Gene expression profiles inBRAF V600Emutant colorectal cancer and association withSFRP2methylation status

2017 ◽  
Author(s):  
Kazuya Yasui ◽  
Takeshi Nagasaka ◽  
Toshiaki Toshima ◽  
Takashi Kawai ◽  
Kunitoshi Shigeyasu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles

scholarly journals Comparison of Stemness and Gene Expression between Gingiva and Dental Follicles in Children

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Chung-Min Kang ◽  
Seong-Oh Kim ◽  
Mijeong Jeon ◽  
Hyung-Jun Choi ◽  
Han-Sung Jung ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pcr Analysis ◽  
Expression Levels ◽  
Qrt Pcr ◽  
Stem Cell Source ◽  
Regenerative Dentistry ◽  
Induced Pluripotent

The aim of this study was to compare the differential gene expression and stemness in the human gingiva and dental follicles (DFs) according to their biological characteristics. Gingiva (n=9) and DFs (n=9) were collected from 18 children. Comparative gene expression profiles were collected using cDNA microarray. The expression of development, chemotaxis, mesenchymal stem cells (MSCs), and induced pluripotent stem cells (iPSs) related genes was assessed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Histological analysis was performed using hematoxylin-eosin and immunohistochemical staining. Gingiva had greater expression of genes related to keratinization, ectodermal development, and chemotaxis whereas DFs exhibited higher expression levels of genes related to tooth and embryo development. qRT-PCR analysis showed that the expression levels of iPSc factors includingSOX2,KLF4, andC-MYCwere58.5±26.3,12.4±3.5, and12.2±1.9times higher in gingiva andVCAM1(CD146) andALCAM(CD166) were33.5±6.9and4.3±0.8times higher in DFs. Genes related to MSCs markers includingCD13,CD34,CD73,CD90, andCD105were expressed at higher levels in DFs. The results of qRT-PCR and IHC staining supported the microarray analysis results. Interestingly, this study demonstrated transcription factors of iPS cells were expressed at higher levels in the gingiva. Given the minimal surgical discomfort and simple accessibility, gingiva is a good candidate stem cell source in regenerative dentistry.

Molecular Determinants of Lymph Node Microenvironment Induced Host Immune Tolerance In CLL: Role for CAV1, PTPN6, and PKCβ In the Process

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 1367-1367
Author(s):  
Christine Gilling ◽  
Amit Mittal ◽  
Vincent Nganga ◽  
Vicky Palmer ◽  
Dennis D. Weisenburger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Model ◽  
Immune Tolerance ◽  
Conflicts Of Interest ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Receptor ◽  
Differentially Expressed ◽  
Expression Levels ◽  
Aggressive Disease

Abstract Abstract 1367 Previously, we have shown that gene expression profiles (GEP) of CLL cells from lymph nodes (LN), bone marrow (BM), and peripheral blood (PB) are significantly different from each other. Among the major pathways associated with differential gene expression, a “tolerogenic signature” involved in host immune tolerance is significant in regulating CLL progression. The genes associated with the tolerogenic signature are significantly differentially expressed in patient LN-CLL compared to BM-CLL and PB-CLL, suggesting that LN-CLL cells induce this immune tolerance. From 83 differentially expressed genes identified by GEP that are associated with immune dysregulation, we selected eleven genes (CAV1, PTPN6, PKCb, ZWINT, IL2Ra, CBLC, CDC42, ZNF175, ZNF264, IL10, and HLA-G) for validation studies to determine whether these genes are also dysregulated in the Emu-TCL1 mouse model of CLL. The results demonstrate a trend of upregulation of these genes as determined by qRT-PCR in the LN-tumor microenvironment. To further evaluate the kinetics of selected gene expression during tumor progression, we determined the expression levels of Cav1, Ptpn6, and Pkcb at 12, 24, and 36 weeks of CLL development in the Em-TCL1 mouse model. We found that the expression of all three genes increased as a function of age, indicating a correlation of gene expression with disease progression. In addition, as CLL progressed in these mice there was a marked decrease in CD4+ and CD8+ T cells. The murine data were further validated using CLL cells from the same patients with indolent versus aggressive disease indicating a similar trend in expression as CLL progressed (n=4). Furthermore, patient data analyzed by Kaplan Meier analyses of the expression levels of the selected genes indicated a significant association between down-regulation of PTPN6 (p=0.031) and up-regulation of ZWINT (p<0.001) with clinical outcome as determined by a shorter time to treatment (p<0.05). Functional analysis by knockdown of CAV1 and PKCb in primary patient CLL cells determined by MTT assay showed a decrease in proliferation following knockdown of these genes (p<0.005). Protein-interaction modeling revealed regulation of CAV1 and PTPN6 by one another. Additionally, the PTPN6 protein regulates B cell receptor (BCR) signaling and subsequently the BCR regulates PKCb. Therefore, these data from both mice and humans with CLL, argue that an aggressive disease phenotype is paralleled by expression of genes associated with immune suppression. In particular, evidence presented here suggests, dysregulation of CAV1, PTPN6, ZWINT, and PKCb expression promotes CLL progression. Disclosures: No relevant conflicts of interest to declare.

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