scholarly journals Unsupervised Hierarchical Clustering Identifies Immune Gene Subtypes in Gastric Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Cao ◽  
Jiao Gong ◽  
Xinhua Li ◽  
Zhaoxia Hu ◽  
Yingjun Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
T Cells ◽  
Hierarchical Clustering ◽  
Expression Patterns ◽  
Training Group ◽  
High Expression ◽  
Gene Expression Patterns ◽  
Validation Group ◽  
High Infiltration

Objectives: The pathogenesis of heterogeneity in gastric cancer (GC) is not clear and presents as a significant obstacle in providing effective drug treatment. We aimed to identify subtypes of GC and explore the underlying pathogenesis.Methods: We collected two microarray datasets from GEO (GSE84433 and GSE84426), performed an unsupervised cluster analysis based on gene expression patterns, and identified related immune and stromal cells. Then, we explored the possible molecular mechanisms of each subtype by functional enrichment analysis and identified related hub genes.Results: First, we identified three clusters of GC by unsupervised hierarchical clustering, with average silhouette width of 0.96, and also identified their related representative genes and immune cells. We validated our findings using dataset GSE84426. Subtypes associated with the highest mortality (subtype 2 in the training group and subtype C in the validation group) showed high expression of SPARC, COL3A1, and CCN. Both subtypes also showed high infiltration of fibroblasts, endothelial cells, hematopoietic stem cells, and a high stromal score. Furthermore, subtypes with the best prognosis (subtype 3 in the training group and subtype A in the validation group) showed high expression of FGL2, DLGAP1-AS5, and so on. Both subtypes also showed high infiltration of CD4+ T cells, CD8+ T cells, NK cells, pDC, macrophages, and CD4+ T effector memory cells.Conclusion: We found that GC can be classified into three subtypes based on gene expression patterns and cell composition. Findings of this study help us better understand the tumor microenvironment and immune milieu associated with heterogeneity in GC and provide practical information to guide personalized treatment.

scholarly journals Unique Gene Expression Patterns in Human T-cell Lines Generated from Multiple Sclerosis Patients by Stimulation with a Synthetic MOG Peptide

2005 ◽  
Vol 12 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Mathilda Mandel ◽  
Michael Gurevich ◽  
Gad Lavie ◽  
Irun R. Cohen ◽  
Anat Achiron
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Cell Lines ◽  
Healthy Subjects ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
T Cell Lines ◽  
Myelin Antigens

Multiple sclerosis (MS) is an autoimmune disease where T-cells activated against myelin antigens are involved in myelin destruction. Yet, healthy subjects also harbor T-cells responsive to myelin antigens, suggesting that MS patient-derived autoimmune T-cells might bear functional differences from T-cells derived from healthy individuals. We addressed this issue by analyzing gene expression patterns of myelin oligodendrocytic glycoprotein (MOG) responsive T-cell lines generated from MS patients and healthy subjects. We identified 150 transcripts that were differentially expressed between MS patients and healthy controls. The most informative 43 genes exhibited >1.5-fold change in expression level. Eighteen genes were up-regulated including BCL2, lifeguard, IGFBP3 and VEGF. Twenty five genes were down-regulated, including apoptotic activators like TNF and heat shock protein genes. This gene expression pattern was unique to MOG specific T-cell lines and was not expressed in T-cell lines reactive to tetanus toxin (TTX). Our results indicate that activation in MS that promotes T-cell survival and expansion, has its own state and that the unique gene expression pattern that characterize autoreactive T-cells in MS represent a constellation of factors in which the chronicity, timing and accumulation of damage make the difference between health and disease.

Gene Expression Patterns Identify Novel Biologically Relevant Signaling and Transcriptional Pathways Involved in Terminal Erythroid Differentiation and Polycythemia Vera.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3524-3524
Author(s):  
Anil Potti ◽  
Holly K. Dressman ◽  
Murat O. Arcasoy
Keyword(s):  
Gene Expression ◽  
Polycythemia Vera ◽  
Hierarchical Clustering ◽  
Expression Patterns ◽  
Terminal Differentiation ◽  
Erythroid Differentiation ◽  
Gene Expression Patterns ◽  
Biologically Relevant ◽  
Distinct Gene ◽  
Study Gene Expression

Abstract Hematopoietic proliferation, lineage commitment, and terminal differentiation are characterized by the emergence of a cell type-specific gene expression and transcriptional programs that determine the specific phenotype and function of cells in the erythroid lineage. Our objectives in this study were to identify unique gene expression patterns that characterize the transcriptional program of normal primary human erythroid precursors during terminal differentiation, and define the gene expression patterns seen in erythroblasts (EBL) of patients with polycythemia vera (PV). Homogenous populations of primary proEBL were generated from purified liquid cultures of CD34+ cells collected from healthy volunteers and PV patients. All patients with PV were diagnosed based on established criteria and had the JAK2-V617F mutation. Morphologic examination and surface expression of CD71 confirmed the purity of proEBL cell populations. ProEBL from normal individuals were induced to terminally differentiate generating orthochromatic EBL. RNA was extracted from normal proEBL, PV proEBL, and normal orthochromatic EBL. Affymetrix U133 Plus 2.0 arrays representing approximately 39,000 human genes were used for gene expression analysis. Four replicates from four independent primary cell cultures were analyzed for each comparison group (e.g. undifferentiated proEBL versus terminally differentiated orthochromatic EBL). Unsupervised hierarchical clustering showed distinct gene expression profiles in the proEBL and terminally differentiated EBL lineages. 1109 genes (2.0 fold change, P<0.01) were found to be differentially expressed. Numerous erythroid genes were found to be upregulated during terminal differentiation [e.g. globin genes, erythropoietin receptor, heme synthesis enzymes (ferrochelatase, ALAS2) erythrocyte membrane proteins (band 3, ankyrin, protein 4.1) and transcription factors (NFE2, Kruppel-like factors, myb, GATA2)]. As a proof of validation, the differential expression of 7 genes was verified by Northern blotting. To better understand the biologic role of the gene sets identified, using Ingenuity pathway analysis, individual genes were integrated into specific regulatory and signaling pathway networks. A total of 19 networks with significant scores (>23) were identified. Biological functions of the identified networks included RNA post-transcriptional regulation, cell cycle control, translational regulation, DNA replication and repair and cellular assembly/organization. In a proof of principle study, gene expression patterns in PV proEBL (n=6) were compared to normal proEBL (n=5). Unsupervised hierarchical clustering showed a distinct gene expression profile for PV. A binary regression predictive model was also developed to find gene expression patterns predictive for PV. Using this model a 150 gene predictor was found that could predict PV patients from control at 100% accuracy. Ingenuity pathways analysis of a subset of gene subsets demonstrated several biologically relevant networks that were distinct in patients with PV, including myc, CDC2, and JAK2. Deregulation of normal transcriptional mechanisms in hematopoietic cells is associated with the pathogenesis of PV. Further, our data shows that genomic studies provide new insights into transcriptional programs that govern erythroid differentiation, and identify biologically relevant deregulated pathways as potential targets for therapy in PV.

scholarly journals Age-related alterations of gene expression patterns in human CD8+T cells

Aging Cell ◽  
2010 ◽  
Vol 9 (1) ◽  
pp. 19-31 ◽  
Author(s):  
Jia-Ning Cao ◽  
Sastry Gollapudi ◽  
Edward H. Sharman ◽  
Zhenyu Jia ◽  
Sudhir Gupta
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Cd8 T Cells ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Age Related

Gene Expression Patterns Associated With Tumor-infiltrating CD4+  and CD8+ T Cells in Invasive Breast Carcinomas 

2020 ◽  
Author(s):  
Zhanwei Wang ◽  
Xi Yang ◽  
Jiamin Xu ◽  
Yuefen Pan ◽  
Junjun Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Cd8 T Cells ◽  
Expression Patterns ◽  
Cd8 T Cell ◽  
Cd4 T Cell ◽  
Gene Expression Patterns ◽  
Cerna Network ◽  
Immune Score

Abstract Objective: This study investigated the gene expression patterns associated with tumor-infiltrating CD4+ and CD8+ T cells in invasive breast carcinomas.Methods: The gene expression data and corresponding clinical phenotype data from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) were downloaded. The stromal and immune score were calculated using ESTIMATE. The differentially expressed genes (DEGs) with a high vs. low stromal score and a high vs. low immune score were screened and then functionally enriched. The tumor-infiltrating immune cells were investigated using the Cibersort algorithm, and the CD4+ and CD8+ T cell-related genes were identified using a Spearman correlation test of infiltrating abundance with the DEGs. Moreover, the miRNA-mRNA pairs and lncRNA-miRNA pairs were predicted to construct the competing endogenous RNAs (ceRNA) network. Kaplan-Meier (K-M) survival curves were also plotted.Results: In total, 478 DEGs with a high vs. low stromal score and 796 DEGs with a high vs. low immune score were identified. In addition, 39 CD4+ T cell-related genes and 78 CD8+ T cell-related genes were identified; of these, 14 genes were significantly associated with the prognosis of BRCA patients. Moreover, for CD4+ T cell-related genes, the chr22-38_28785274-29006793.1-–miR-34a/c-5p–CAPN6 axis was identified from the ceRNA network, whereas the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis was identified for CD8+ T cell-related genes.Conclusions: The chr22-38_28785274-29006793.1-–miR-34a/c-5p–CAPN6 axis and the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis might regulate cellular activities associated with CD4+ and CD8+ T cell infiltration, respectively, in BRCA.

scholarly journals Variation in Gene Expression Patterns in Human Gastric Cancers

2003 ◽  
Vol 14 (8) ◽  
pp. 3208-3215 ◽  
Author(s):  
Xin Chen ◽  
Suet Y. Leung ◽  
Siu T. Yuen ◽  
Kent-Man Chu ◽  
Jiafu Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Patient Survival ◽  
Expression Patterns ◽  
Therapeutic Strategies ◽  
Gene Expression Patterns ◽  
Intrinsic Properties ◽  
Normal Cells ◽  
Common Cause ◽  
Gastric Cancers

Gastric cancer is the world's second most common cause of cancer death. We analyzed gene expression patterns in 90 primary gastric cancers, 14 metastatic gastric cancers, and 22 nonneoplastic gastric tissues, using cDNA microarrays representing ∼30,300 genes. Gastric cancers were distinguished from nonneoplastic gastric tissues by characteristic differences in their gene expression patterns. We found a diversity of gene expression patterns in gastric cancer, reflecting variation in intrinsic properties of tumor and normal cells and variation in the cellular composition of these complex tissues. We identified several genes whose expression levels were significantly correlated with patient survival. The variations in gene expression patterns among cancers in different patients suggest differences in pathogenetic pathways and potential therapeutic strategies.

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