Gene Expression Analysis of CML Patients across the Age Spectrum

Abstract Chronic myeloid leukemia (CML) accounts for 2-9% of leukemias in children and adolescents, and occurs with much greater frequency in adults. Compared to adults, children with CML tend to present with higher white blood cell counts and larger spleens, suggesting that the biology of pediatric CML is different from adult CML. We hypothesize that the differences in clinical presentation of pediatric CML are due to unique molecular characteristics that differ from adult CML. To test this hypothesis, we compared the transcriptomic signature of pediatric and adult CML CD34+ cells and healthy age-matched CD34+ cells. CD34+ cells were isolated by FACS from pediatric CML (n=9), adult CML (n=10), pediatric healthy (n=10), and adult healthy (n=10) bone marrow samples. Prepared libraries were sequenced on the Illumina HiSeq 4000 instrument. Raw sequences were trimmed and aligned to the hg38 reference genome with STAR/2.5.1b aligner. Gene level counts were determined with STAR -quantMode option using gene annotations from GENCODE (p5). Differential gene expression and pathway analysis were conducted with R/3.5.3. Counts were normalized with trimmed mean of M-values from the EdgeR/ 3.24.3 package and further transformed with VOOM from the Limma/ 3.38.3 package. A linear model using the empirical Bayes analysis pipeline also from Limma was then used to obtain p-values, adjusted p-values and log-fold changes. Four comparisons were performed: (1) pediatric CML vs pediatric healthy, (2) adult CML vs adult healthy, (3) pediatric CML vs adult CML, and (4) pediatric healthy vs adult healthy. A False Discovery Rate of ≤ .05 and absolute log2 fold-change > 1 was used to define differentially expressed genes (DEGs) in each comparison. To identify potentially unique pathways based on DEG, pathway over-representation was calculated with either goana from the limma package or clueGO. At diagnosis, pediatric patients had higher platelet counts (p=0.001) and larger spleen sizes (p=0.010) than adult patients. Median WBC counts were 273,000 and 143,000 in pediatric and adult patients respectively. A total of 1352 genes were differentially expressed in either adult or pediatric CML CD34+ cells compared to healthy CD34+ cells, 174 of which were expressed similarly in pediatric and adult CML CD34+ cells (54 up- and 120 down-regulated). There were 746 differentially expressed genes (325 up- and 421 down-regulated) in adult CML CD34+ cells compared to adult healthy CD34+ cells, and 432 differentially expressed genes (156 up- and 276 down-regulated) in pediatric CML CD34+ cells compared to pediatric healthy CD34+ cells. In direct comparison of pediatric and adult CML CD34+ cells, 446 genes (270 up and 176 down) were dysregulated in pediatric CML CD34+ cells. Pathway analysis showed that Rho signaling pathway was downregulated in pediatric CML CD34+ cells and several genes in Rho pathway were uniquely dysregulated. ARHGAP27 and VAV2 were significantly upregulated in adult CML CD34+ cells by 3.7-fold (p=0.0453) and 11-fold (p=0.0072), respectively, compared to pediatric CML CD34+ cells. In addition, several genes involved in the NADPH oxidase pathway, one of the best-characterized Rho GTPase-regulated systems, were differently expressed in CML. NCF1, CYBB, and S100A8 were significantly upregulated in adult CML CD34+ cells by 4-fold (p=0.0045), 3.26-fold (p<0.0001), and 3.09-fold (p<0.0001), respectively, compared to pediatric CML CD34+ cells. Furthermore, DLC1, which is known as a negative regulator of Rho pathway, was significantly upregulated in pediatric CML CD34+ cells by 2.47-fold (p=0.0493) compared to adult CML CD34+ cells. These results demonstrate unique molecular characteristics of pediatric CML that may contribute to the clinical differences at presentation between adult and pediatric disease. A better understanding of the molecular biology of CML across the ages will provide new insights into the pathogenesis of pediatric CML and potentially inform future treatment decisions. Disclosures Davis: Jazz Pharmaceuticals: Research Funding; Novartis Pharmaceuticals: Honoraria. Hijiya: Novartis: Consultancy; Stemline Therapeutics: Consultancy.

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Digital Gene Expression Profiling Analysis of Aged Mice under Moxibustion Treatment

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2018/4767328 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Nan Liu ◽

Yunyao Jiang ◽

Min Xing ◽

Baixiao Zhao ◽

Jincai Hou ◽

...

Keyword(s):

Gene Expression ◽

Gene Ontology ◽

Gene Expression Profiling ◽

Differentially Expressed Genes ◽

Pathway Analysis ◽

Expression Profiling ◽

Digital Gene Expression ◽

Differentially Expressed ◽

Aged Mice ◽

Digital Gene Expression Profiling

Aging is closely connected with death, progressive physiological decline, and increased risk of diseases, such as cancer, arteriosclerosis, heart disease, hypertension, and neurodegenerative diseases. It is reported that moxibustion can treat more than 300 kinds of diseases including aging related problems and can improve immune function and physiological functions. The digital gene expression profiling of aged mice with or without moxibustion treatment was investigated and the mechanisms of moxibustion in aged mice were speculated by gene ontology and pathway analysis in the study. Almost 145 million raw reads were obtained by digital gene expression analysis and about 140 million (96.55%) were clean reads. Five differentially expressed genes with an adjusted P value < 0.05 and |log⁡2(fold change)| > 1 were identified between the control and moxibustion groups. They were Gm6563, Gm8116, Rps26-ps1, Nat8f4, and Igkv3-12. Gene ontology analysis was carried out by the GOseq R package and functional annotations of the differentially expressed genes related to translation, mRNA export from nucleus, mRNA transport, nuclear body, acetyltransferase activity, and so on. Kyoto Encyclopedia of Genes and Genomes database was used for pathway analysis and ribosome was the most significantly enriched pathway term.

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Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

10.21203/rs.2.11118/v1 ◽

2019 ◽

Author(s):

Jiasheng Xu ◽

Kaili Liao ◽

ZHONGHUA FU ◽

ZHENFANG XIONG

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Gene Ontology ◽

Differentially Expressed Genes ◽

Pathway Analysis ◽

Differentially Expressed ◽

Cancer Tissue ◽

Pancreatic Cancer Tissue ◽

Tissue Samples ◽

Multiple Difference

Abstract Objective To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. Methods: Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University during March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. Results Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients;while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene had the highest multiple of differential expression (difference multiple: 31.76). The Pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the highest and COL11A1 gene had the highest multiple difference (multiple difference: 5.02). The expressions of differentially expressed genes CPB1, COL11A1、ITGA4、BIRC3、PAK4、CPA1、CLPS、PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene chip analysis. Conclusions The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples compared between Mongolian and Han populations. These genes are closely related to the proliferation, differentiation, invasion and metastasis and multi-drug resistance of pancreatic cancer and are involved in the regulation of multiple important signaling pathways in organisms.

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Individual Gene Expression Profiling of Bone Marrow CD34 Cells in Acquired Severe Aplastic Anemia (aSAA) in Children.

Blood ◽

10.1182/blood.v108.11.978.978 ◽

2006 ◽

Vol 108 (11) ◽

pp. 978-978 ◽

Cited By ~ 1

Author(s):

Bernd Hubner ◽

Sylvia Merk ◽

Sonja Rauhut ◽

Martin Dugas ◽

Torsten Haferlach ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Bone Marrow ◽

Differentially Expressed Genes ◽

Expression Profiling ◽

Cell Metabolism ◽

Cell Communication ◽

Differentially Expressed ◽

Cd34 Cells ◽

The Individual

Abstract Acquired SAA in children is a rare, life-threatening disease characterized by pancytopenia and bone marrow hypocellularity. There is good clinical and laboratory evidence that a T-cell mediated immune attack against stem and progenitor cells plays an important role in the pathogenesis of SAA. However, due to the paucity of residual CD34 positive cells at diagnosis still only little is known about the stem cells and their response to the autoimmune attack in SAA in children. To further investigate the characteristics of CD34 cells in SAA we compared the individual transcriptomes of CD34 cells of 9 newly diagnosed, untreated pediatric SAA patients with 8 pediatric healthy controls. Hematopoietic stem cells were isolated with high efficiency from bone marrow by Ficoll density centrifugation and subsequent affinity purification using Dynabeads (Dynal, Invitrogen). Expression profiling experiments were performed using the two cycle amplification system and the HG-U133 plus 2.0 array (Affymetrix). Gene expression data were analyzed using R 2.3.0 and Bioconductor 1.8. packages (Affymetrix, multtest). Raw data were normalized using robust multiarray average (RMA) algorithm. Probe sets with “absent” calls in more than 50% of samples in the smaller group were identified and omitted from further analysis. To determine differentially expressed genes, t-test was applied. P value adjustments for multiple comparisons were done using the step-up false discovery rate (FDR) controlling method proposed by Benjamini and Hochberg. Overall 402 genes were differentially expressed in children with SAA compared to controls (p < 0.05), 288 genes were downregulated and 114 were upregulated. Gene ontology analyses (FatiGO) indicated that biological processes in CD34 cells are significantly affected in pediatric SAA by mainly downregulation of genes for cell metabolism (78 down, 30 up), cell communication/adhesion (48 down, 25 up), growth and differentiation (15 down, 4 up) and stress response (16 down, 3 up). Unexpectedly only very few genes involved in cell death/apoptosis (5 down, 4 up) were differentially expressed. Genes encoding for DNA/RNA binding proteins (28 down, 14 up) and ion binding proteins (47 down, 18 up) were also mainly downregulated. Despite the extremely low numbers of residual CD34 cells present in the bone marrow of children with untreated SAA we were able to analyze the individual transcriptome pattern of single patients. These patterns showed homogeneously and significantly different gene expressions in the group of affected children when compared to controls. Genes involved in apoptosis seem to be less altered in there expression than expected from adult data. These observation might be consistent with the major clinical finding in these children of almost empty bone marrows where most of the apoptotic cell death has already taken place. In the tiny population of “survivors” most of the differentially expressed genes are involved in cell metabolism and cell communication or adhesion. These unexpected results provide new hints for further investigations regarding the involvement of CD34 cells in the pathogenesis of childhood aSAA.

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Gene Expression Profiles of CD34+ Cells in Myelodysplastic Syndrome

Blood ◽

10.1182/blood.v112.11.5078.5078 ◽

2008 ◽

Vol 112 (11) ◽

pp. 5078-5078

Author(s):

Monika Belickova ◽

Alzbeta Vasikova ◽

Eva Budinska ◽

Jaroslav Cermak

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Statistical Testing ◽

Differentially Expressed ◽

Control Group ◽

Regulation Of Transcription ◽

Cd34 Cells

Abstract Myelodysplatic syndrome (MDS) represents a heterogeneous group of clonal disorders with ineffective hematopoiesis that is characterized by dysplasia and peripheral cytopenia of one or more cell lineages. We studied gene expression profiles in CD34+ cells of 42 MDS patients and 6 healthy controls using Illumina cDNA microarray technology. Nine patients had RA, 7 patients had RCMD, 17 patients had RAEB and 9 had RAEB-T. CD34+ cells were isolated from bone marrow samples using MACS magnetic columns. The quality of total extracted RNA was confirmed with the Agilent Bioanalyzer 2100. 200ng of total RNA was amplified using Illumina RNA amplification kit. cRNA targets were hybridized on the Sentrix HumanRef-8 BeadChips (> 24 000 probes), which were scanned on the Illumina BeadStation 500. The data were pre-processed and normalized by lumi R package designed to preprocess the Illumina microarray data. Normalized data were filtered by detection p-value <0.01, resulting in total number of 10 091 genes. This gene set was tested for differential expression between clinical groups and control group. For this purpose, statistical testing by ANOVA with correction for multiple testing problem by Bayesian thresholding was performed. Additionally, analysis by random-forests (RAFT) was performed. Significant genes from both analyses were merged resulting in 332 differentially expressed genes detected. Out of these, 79 genes showed ≥2.5 fold changes in gene expression between controls and all MDS groups (22 up-regulated and 57 down-regulated). Our findings were confirmed by real-time quantitative PCR for several genes (TaqMan Gene Expression Assays). We used DAVID database to annotate 79 selected genes: 8 of 22 up-regulated genes in MDS patients were recognized to play a role in regulation of transcription (LEO1, E2F6 and several zing finger proteins). A half of these over-expressed genes could not be annotated due to still unknown biological function. Within the set of the down-regulated genes in MDS patients those biological processes were predominantly detected: cell differentiation (KLF4, FOSL2, STK17B, BCL3, SNF1LK, ID2 etc.), response to stress (CXCL12, SMAD7, CYGB, etc.) and cell proliferation (MXD1, OSM, FTH1, KLF10 etc.). In the set of 31 genes with 5 fold decreased expression, we identified 8 genes involved in B-cell development. (VPREB1, VPREB3, CD79A, EBI2, LEF1, CXCL12, CTGF, GALNAC4S-6ST). RAFT analysis was performed also in the set of 332 statistically differentially expressed genes in order to evaluate accuracy of grouping the patients according their diagnosis. We detected strong heterogeneity in gene expression patterns within the MDS patients, especially in the RAEB group reflecting clinical diversity of MDS. Clustering analysis (Spearman correlation) showed that most of the RAEB-2 patients (7 out of 9) were clustered together with REAB-T whereas RAEB-1 clustered with RCMD or RA. These results underline the need of distinguishing RAEB-1 and RAEB-2 diagnosis according to WHO classification system, since their expression profiles are significantly different.

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A Distinct Expression of Various Gene Subsets in CD34+ Cells Obtained From Patients with Early and Advanced Myelodysplastic Syndrome.

Blood ◽

10.1182/blood.v114.22.3795.3795 ◽

2009 ◽

Vol 114 (22) ◽

pp. 3795-3795

Author(s):

Monika Belickova ◽

Jaroslav Cermak ◽

Alzbeta Vasikova ◽

Eva Budinska

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Differentially Expressed Genes ◽

Conflicts Of Interest ◽

Expression Profiles ◽

Differentially Expressed ◽

Abnormal Development ◽

Statistical Hypothesis ◽

P Value ◽

Cd34 Cells

Abstract Abstract 3795 Poster Board III-731 Gene expression profiles of CD34+ cells were compared between a cohort of 51 patients with MDS or AML from MDS and 7 healthy controls. The patients were classified according to the WHO criteria as follows: 5q- syndrome (n=7), RA (n=3), RARS (n=2), RCMD (n=10), RAEB-1 (n=7), RAEB-2 (n=15), and AML with MLD (multilineage dysplasia) (n=7). HumanRef-8 v2 Expression Bead Chips (Illumina) were used to generate expression profiles of the samples for >22,000 transcripts. The raw data were normalized data with the R software, lumi package. Normalized data were filtered by detection p-value <0.01, resulting in total number of 9811genes. To identify differentially expressed genes we performed two parallel statistical hypothesis testings: Analysis of Variance (ANOVA) together with Tukey test and empirical bayesian thresholding correction for multiple testing problem; and Significance Analysis of Microarrays (SAM). The results were confirmed by real-time quantitative PCR for six genes (TaqMan Gene Expression Assays). Hierarchical clustering of significantly differentially expressed genes clearly separated patients and controls, 5q-syndrome and RAEB-1 as a separate entities confirming usefulness of WHO classification subgroups. The most up-regulated genes in all patients included HBG2, HBG1, CYBRD1, HSPA1B, ANGPT1, and MYC. We assume that expression changes in globin genes, both fetal and adult globins (HBG2, HBG1 and HBA1, HBB) may play role not only in dysregulation of erythropoiesis but also in the disease progression or leukemic transformation of MDS. Among the most down-regulated genes, 13 genes related to B-lymphopoiesis (e.g. POU2AF1, VPREB1, VPREB3, CD79A, EBF1, LEF1, BCL3, IRF8 & IRF4) were detected, suggesting the abnormal development of B-cell progenitors in all MDS patients. Some of these genes (e.g. VPREB3, LEF1) showed decreasing trend in expression level from early to advanced MDS with the lowest expression in AML with MLD. Patients with advanced MDS had significantly decreased expression of genes involved in in the mitotic cell cycle, DNA replication, and chromosome segregation compared to early MDS where these gene subsets were up-regulated. The DAVID database also identified de-regulation in the cell cycle pathway through its 7 genes (CDC25C, CDC7, CDC20, ORC1L, CCNB2, BUB1, & CCNA2). On the other hand, advanced MDS patients showed significant up-regulation of proto-oncogenes (BMI1, MERTK) and genes related to angiogenesis (ANGPT1), anti-apoptosis (VNN1). The results confirm on molecular basis that increased cell proliferation and resistance to apoptosis together with a loss of cell cycle control, damaged DNA repair and altered immune response may play an important role in the expansion of malignant clone in MDS patients. The study was supported by Grant NR-9235 obtained from the Ministry of Health, Czech Republic. Disclosures: No relevant conflicts of interest to declare.

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Gene Expression Profile of HCAEC Cells Exposed to Serum From Chronic Kidney Disease Patients: Role of MAPK Signaling Pathway

10.21203/rs.3.rs-669206/v1 ◽

2021 ◽

Author(s):

Angélica Rangel-López ◽

Oscar Pérez-González ◽

Sergio Juárez-Méndez ◽

Ricardo López-Romero ◽

Minerva Mata-Rocha ◽

...

Keyword(s):

Gene Expression ◽

Endothelial Dysfunction ◽

Signaling Pathway ◽

Gene Expression Profile ◽

Differentially Expressed Genes ◽

Pathway Analysis ◽

Expression Profile ◽

Differentially Expressed ◽

Uremic Serum ◽

Esrd Patients

Abstract End-stage renal disease (ESRD) patients have an elevated risk of cardiovascular (CV) complications including acute myocardial infarction (AMI); endothelial dysfunction and accumulation of uremic toxins have been associated with such CV-events. To explore which molecular pathways are involved in this CV-complication and the effects of the uremic serum on gene expression, an endothelial dysfunction model was studied through microarrays and pathway analysis. mRNA was isolated of human coronary arterial endothelial cells (HCAEC) primary cultures supplemented with 20% uremic serum from two groups of patients, USI: ESRD-patients; UCI: ESRD-AMI-patients. Affymetrix GeneChip® microarray and the LIMMA-package (Linear Models for Microarray Data) of the Bioconductor sofware17 was implemented to identify relevant DEGs between the two groups of uremic patients. Protein-protein interaction networks and pathway analysis were made to analyze the interaction and expression tendency of differentially expressed genes. 100 differentially expressed genes were identified from two data sets triggered by uremic state using bioinformatics, from 16,607. After in a new cohort, 30 genes were overexpressed in UCI group, which we identified 500 ontological genetic terms and one KEGG-pathway with p < 0.05. The metabolic pathway significantly represented was the MAPK signaling pathway. Network analysis showed six genes (PTGS2, SELE, ICAM1, HMOX1, EGR1, and TLR2) that represent potential markers for ESRD with AMI, as an approximation to their underlying mechanisms. The results obtained suggest that uremic toxins in patients with ESRD can alter HCAEC and modify the gene expression profile, which could have an impact on the development of cardiovascular complications in these patients.

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Gene Expression Profiling Differentiates Childhood Acute Lymphoblastic Leukemia in Down Syndrome Versus Non-Down Syndrome Patients.

Blood ◽

10.1182/blood.v112.11.1203.1203 ◽

2008 ◽

Vol 112 (11) ◽

pp. 1203-1203

Author(s):

Karen R. Rabin ◽

Jinhua Wang ◽

Anna Tsimelzon ◽

Debra Morrison ◽

Amos S. Gaikwad ◽

...

Keyword(s):

Gene Expression ◽

Down Syndrome ◽

Acute Lymphoblastic Leukemia ◽

Oxidative Phosphorylation ◽

Differentially Expressed Genes ◽

Expression Profiling ◽

Lymphoblastic Leukemia ◽

Gene Expression Signature ◽

Differentially Expressed ◽

P Values

Abstract Children with Down syndrome (DS) and acute lymphoblastic leukemia (ALL) form a unique biological subset. These patients have generally inferior outcomes in many studies, and an increased incidence of treatment-related toxicities. Cases of DS ALL have a much lower frequency of recurrent prognostically significant chromosomal abnormalities than cases of ALL in the general pediatric population. Global gene expression profiling provides an opportunity to gain insights into pathogenesis and potential therapeutic targets in DS ALL. We performed microarray analysis of RNA from bone marrow samples obtained at diagnosis in 30 DS ALL and 24 non-DS ALL cases using the Affymetrix Human Genome U133 Plus 2.0 array. Unsupervised hierarchical clustering separated cases into two main groups, one of which included 21 of 30 DS samples (Fisher’s exact test, p = 0.013), suggesting inherent biologic similarities. Non-DS samples clustered according to known cytogenetic features. Consistent with recently published data, a subset (13%) of our DS ALL cases were found to have JAK2 mutations. Cases of DS ALL bearing JAK2 mutations did not form a distinct subcluster, suggesting that the JAK2 pathway may be dysregulated via other events in cases of DS ALL with wild-type JAK2. Two-sample comparison of DS versus non-DS ALL cases demonstrated differential expression of 513 genes with p values <0.001 (Figure 1). Oxidative phosphorylation pathway genes were most over-represented among differentially expressed genes, with 35 of 115 genes in this pathway demonstrating down-regulation in DS compared to non-DS ALL (Bonferroni corrected p value < 1 ×10−9), including several cytochrome c oxidase and ubiquinone subunits. Our data indicate that DS ALL blasts may utilize oxidative phosphorylation to a lesser extent than non-DS ALL, a feature which could be exploited therapeutically. The top 513 genes differentially expressed in DS versus non-DS ALL (Benjamini-Hochberg corrected p values < 0.001) are displayed in a heatmap where genes relatively overexpressed in DS ALL are depicted in yellow, and relatively underexpressed in DS ALL in red. DS ALL cases are indicated by red circles and non-DS ALL cases by white circles. Four DS ALL cases bearing a JAK2 mutation at arginine 683 are indicated by black stars. Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) versus non-Down syndrome acute lymphoblastic leukemia (ALL). Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) versus non-Down syndrome acute lymphoblastic leukemia (ALL).

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Comparison of the Transcriptomic Signatures in Pediatric and Adult CML

Cancers ◽

10.3390/cancers13246263 ◽

2021 ◽

Vol 13 (24) ◽

pp. 6263

Author(s):

Minyoung Youn ◽

Stephanie M. Smith ◽

Alex Gia Lee ◽

Hee-Don Chae ◽

Elizabeth Spiteri ◽

...

Keyword(s):

Chronic Myeloid Leukemia ◽

Rna Sequencing ◽

Differentially Expressed Genes ◽

High Throughput ◽

Myeloid Leukemia ◽

Differentially Expressed ◽

Molecular Characteristics ◽

Healthy Controls ◽

Cd34 Cells ◽

Transcriptomic Signature

Children with chronic myeloid leukemia (CML) tend to present with higher white blood counts and larger spleens than adults with CML, suggesting that the biology of pediatric and adult CML may differ. To investigate whether pediatric and adult CML have unique molecular characteristics, we studied the transcriptomic signature of pediatric and adult CML CD34+ cells and healthy pediatric and adult CD34+ control cells. Using high-throughput RNA sequencing, we found 567 genes (207 up- and 360 downregulated) differentially expressed in pediatric CML CD34+ cells compared to pediatric healthy CD34+ cells. Directly comparing pediatric and adult CML CD34+ cells, 398 genes (258 up- and 140 downregulated), including many in the Rho pathway, were differentially expressed in pediatric CML CD34+ cells. Using RT-qPCR to verify differentially expressed genes, VAV2 and ARHGAP27 were significantly upregulated in adult CML CD34+ cells compared to pediatric CML CD34+ cells. NCF1, CYBB, and S100A8 were upregulated in adult CML CD34+ cells but not in pediatric CML CD34+ cells, compared to healthy controls. In contrast, DLC1 was significantly upregulated in pediatric CML CD34+ cells but not in adult CML CD34+ cells, compared to healthy controls. These results demonstrate unique molecular characteristics of pediatric CML, such as dysregulation of the Rho pathway, which may contribute to clinical differences between pediatric and adult patients.

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Comparison of the Transcriptomic Signatures in Pediatric and Adult CML

Blood ◽

10.1182/blood-2020-142270 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 39-40

Author(s):

Minyoung Youn ◽

Hee-Don Chae ◽

Stephanie M. Smith ◽

Alex Gia Lee ◽

Lara C. Murphy ◽

...

Keyword(s):

Bone Marrow ◽

Blood Cell ◽

Board Of Directors ◽

Research Funding ◽

Pediatric Patients ◽

Differentially Expressed ◽

Molecular Characteristics ◽

Advisory Committees ◽

P Values ◽

Cd34 Cells

Introduction Pediatric chronic myeloid leukemia (CML) accounts for 10-15% of pediatric myeloid leukemias and 2-9% of all pediatric leukemias. There are several unique characteristics of CML diagnosed in children, adolescents, and young adults, compared to adults. They present with higher white blood counts and larger spleens, suggesting that the biology of pediatric CML is different from adult CML. We hypothesize that the differences in clinical presentation of pediatric CML patients are due to unique molecular characteristics that differ from adult CML patients. To test this hypothesis, we studied the transcriptomic signature of pediatric CD34+ CML cells compared to adult CML and normal age-matched bone marrow CD34+ cells. Methods CD34+ cells were isolated by FACS from pediatric CML (n=9), adult CML (n=10), pediatric normal (n=10) and adult normal (n=10) bone marrow samples. Total RNA was isolated from cells, and cDNA libraries were generated. Prepared libraries were sequenced on the Illumina HiSeq 4000 instrument. Raw sequences were trimmed and aligned to the hg38 reference genome with STAR/2.5.1b aligner. Gene level counts were determined with STAR -quantMode option using gene annotations from GENCODE (p5). Differential gene expression and pathway analysis were conducted with R/3.5.3. Counts were normalized with trimmed mean of M-values (TMM) from the EdgeR/ 3.24.3 package and further transformed with VOOM from the Limma/ 3.38.3 package. A linear model using the empirical Bayes analysis pipeline also from Limma was then used to obtain p-values, adjusted p-values and log-fold changes (LogFC). We performed three comparisons: (1) Pediatric CML vs Normal, (2) Adult CML vs Normal, and (3) Pediatric CML vs Adult CML. A False Discovery Rate (FDR) of £ .05 and absolute log2 fold-change > 1 was used to define differentially expressed genes in each comparison. Over-representation analysis was used to identify potentially unique pathways based on differentially expressed genes. Clinical and demographic features at diagnosis were extracted for pediatric and adult CML patients and compared using Fisher's exact test (categorical variables) or Wilcoxon rank sum test (continuous variables). Results Pediatric patients were diagnosed with CML at a median of 11 years (interquartile range (IQR): 10-14) compared to 54 years (IQR: 33-62) for adult patients. At diagnosis, pediatric patients had higher platelet counts (p=0.001) and larger spleen sizes (p=0.010) than adult patients, whereas the white blood cell count and phase at diagnosis did not differ. We found 606 genes (210 up- and 396 down-regulated) differentially expressed in pediatric CML CD34+ cells compared to pediatric normal controls. Interestingly, transcriptional regulators involved in blood cell differentiation including GATA1, TAL1, and KLF1 were differentially enriched in pediatric CML. In comparing adult CML patients to normal adult CD34+ cells, we found 920 genes (379 up- and 541 down-regulated) differentially expressed. Among all dysregulated genes we identified (1352 genes), 174 genes (54 up- and 120-down-regulated) overlapped when comparing pediatric and adult CML patients. Significantly enriched pathways in both adult and pediatric CML cells included PI3K/AKT signaling, MAPK signaling, and Notch/Wnt signaling, which have been previously reported. We found 437 unique genes that were dysregulated only in pediatric CML (270 up- and 167 down-regulated). Notch/Wnt signaling and Rho signaling pathways were significantly enriched. DLC1, a tumor suppressor gene that encodes a RhoGTPase-activating protein, has been known to be downregulated in solid tumors and hematologic malignancies. Interestingly, our data showed that DLC1 is significantly upregulated by 3-fold (p=0.0238) in pediatric CML, but not adult CML CD34+ cells. In addition, we observed that ABR, an inducer of C/EBPa that encodes an activator of RhoGEF and GTPase, was significantly downregulated by 2-fold (p=0.0119) in pediatric but not in adult CML CD34+ cells. Conclusion These results demonstrate unique molecular characteristics of pediatric CML that may contribute to the clinical differences at presentation between adult and pediatric disease. A better understanding of the particular biology of pediatric CML might impact the treatment of those patients in the future. Disclosures Gotlib: Deciphera: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: co-chair of the Study Steering Committee and Research Funding; Blueprint Medicines Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Chair of the Response Adjudication Committee and Research Funding, Research Funding.

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Identification of Key Modules and Hub Genes of Annulus Fibrosus in Intervertebral Disc Degeneration

Frontiers in Genetics ◽

10.3389/fgene.2020.596174 ◽

2021 ◽

Vol 11 ◽

Author(s):

Hantao Wang ◽

Wenhui Liu ◽

Bo Yu ◽

Xiaosheng Yu ◽

Bin Chen

Keyword(s):

Gene Expression ◽

Intervertebral Disc ◽

Differentially Expressed Genes ◽

Disc Degeneration ◽

Pathway Analysis ◽

Annulus Fibrosus ◽

Intervertebral Disc Degeneration ◽

Differentially Expressed ◽

Clinical Samples ◽

Hub Genes

Background: Intervertebral disc degeneration impairs the quality of patients lives. Even though there has been development of many therapeutic strategies, most of them remain unsatisfactory due to the limited understanding of the mechanisms that underlie the intervertebral disc degeneration.Questions/purposes: This study is meant to identify the key modules and hub genes related to the annulus fibrosus in intervertebral disc degeneration (IDD) through: (1) constructing a weighted gene co-expression network; (2) identifying key modules and hub genes; (3) verifying the relationships of key modules and hub genes with IDD; and (4) confirming the expression pattern of hub genes in clinical samples.Methods: The Gene Expression Omnibus provided 24 sets of annulus fibrosus microarray data. Differentially expressed genes between the annulus fibrosus of degenerative and non-degenerative intervertebral disc samples have gone through the Gene Ontology (GO) and pathway analysis. The construction of a gene network and classification of genes into different modules were conducted through performing Weighted Gene Co-expression Network Analysis. The identification of modules and hub genes that were most related to intervertebral disc degeneration was proceeded. In order to verify the relationships of the module and hub genes with intervertebral disc degeneration, Ingenuity Pathway Analysis was operated. Clinical samples were adopted to help verify the hub gene expression profile.Results: One thousand one hundred ninety differentially expressed genes were identified. Terms and pathways associated with intervertebral disc degeneration were presented by GO and pathway analysis. The construction of a Weighted Gene Coexpression Network was completed and clustering differentially expressed genes into four modules was also achieved. The module with the lowest P-value and the highest absolute correlation coefficient was selected and its relationship with intervertebral disc degeneration was confirmed by Ingenuity Pathway Analysis. The identification of hub genes and the confirmation of their expression profile were also realized.Conclusions: This study generated a comprehensive overview of the gene networks underlying annulus fibrosus in intervertebral disc degeneration.Clinical Relevance: Modules and hub genes identified in this study are highly associated with intervertebral disc degeneration, and may serve as potential therapeutic targets for intervertebral disc degeneration.

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