Bioinformatics study on genes related to a high-risk postoperative recurrence of lung adenocarcinoma

In this study, we aimed to screen out genes associated with a high risk of postoperative recurrence of lung adenocarcinoma and investigate the possible mechanisms of the involvement of these genes in the recurrence of lung adenocarcinoma. We identify Hub genes and verify the expression levels and prognostic roles of these genes. Datasets of GSE40791, GSE31210, and GSE30219 were obtained from the Gene Expression Omnibus database. Enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the screened candidate genes using the DAVID database. Then, we performed protein–protein interaction (PPI) network analysis through the database STRING. Hub genes were screened out using Cytoscape software, and their expression levels were determined by the GEPIA database. Finally, we assessed the relationships of Hub genes expression levels and the time of survival. Forty-five candidate genes related to a high-risk of lung adenocarcinoma recurrence were screened out. Gene ontology analysis showed that these genes were enriched in the mitotic spindle assembly checkpoint, mitotic sister chromosome segregation, G2/M-phase transition of the mitotic cell cycle, and ATP binding, etc. KEGG analysis showed that these genes were involved predominantly in the cell cycle, p53 signaling pathway, and oocyte meiosis. We screened out the top ten Hub genes related to high expression of lung adenocarcinoma from the PPI network. The high expression levels of eight genes (TOP2A, HMMR, MELK, MAD2L1, BUB1B, BUB1, RRM2, and CCNA2) were related to short recurrence-free survival and they can be used as biomarkers for high risk of lung adenocarcinoma recurrence. This study screened out eight genes associated with a high risk of lung adenocarcinoma recurrence, which might provide novel insights into researching the recurrence mechanisms of lung adenocarcinoma as well as into the selection of targets in the treatment of the disease.

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An Integrated Analysis of Prognostic and Immune Infiltrates for Hub Genes as Potential Survival Indicators in Patients with Lung Adenocarcinoma

10.21203/rs.3.rs-900442/v1 ◽

2021 ◽

Author(s):

Zhiyun Xu ◽

Shi Wang ◽

Zhijian Ren ◽

Xiang Gao ◽

Lin Xu ◽

...

Keyword(s):

Cell Cycle ◽

Gene Ontology ◽

Lung Adenocarcinoma ◽

Immune Cells ◽

Enrichment Analysis ◽

Integrated Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Expression Levels ◽

Pathway Enrichment

Abstract Objective: Lung adenocarcinoma is one of the major subtypes of lung cancer. However, the prognosis of individuals with LUAD is still not promising. Therefore, this research aims to discover useful biomarkers to enhance the treatment and diagnosis of LUAD.Methods: GEO2R was used to identify common up-regulated DEGs in the GSE32863, GSE40791 and GSE75037. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis. Metascape was also utilized to construct the PPI network, and the MCODE plug-in was employed to filter important subnetworks. The prognosis and expression levels of the hub genes were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The Timer database was utilized to confirm the correlation between immune cells infiltration and the expression levels of hub genes in LUAD tissues.Results: This research discovered 307 common up-regulated DEGs, and gene ontology and pathway enrichment analysis indicated that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF and KNTC1. The seven hub genes were highly expressed in LUAD tissues and had a poor prognosis. AURKB, CCNB2, and CDC20 were inversely associated with B and CD4+ T cells. CDCA5, CDCA8, and CENPF have a substantially negative correlation with B Cell, but positive correlation with Neutrophil. Conclusions: This research demonstrates that increased expression of seven hub genes is associated with worse prognosis for LUAD patients. Additionally, immune cells infiltrating LUAD tissues may serve as a regulating mechanism.

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The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

Journal of Biomaterials and Tissue Engineering ◽

10.1166/jbt.2022.2834 ◽

2022 ◽

Vol 12 (3) ◽

pp. 523-532

Author(s):

Xin Yan ◽

Chunfeng Liang ◽

Xinghuan Liang ◽

Li Li ◽

Zhenxing Huang ◽

...

Keyword(s):

Cell Cycle ◽

Gene Ontology ◽

Protein Interaction ◽

Adrenocortical Carcinoma ◽

Gene Expression Omnibus ◽

Heparin Binding ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

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Identification of genes with therapeutic and prognostic values in lung adenocarcinoma microenvironment

10.21203/rs.3.rs-16969/v1 ◽

2020 ◽

Author(s):

Xueping Jiang ◽

Yanping Gao ◽

Nannan Zhang ◽

Cheng Yuan ◽

Yuan Luo ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Immune Cell ◽

The Cancer Genome Atlas ◽

Venn Diagram ◽

Ppi Network ◽

Survival Curves ◽

Hub Genes ◽

Expression Levels ◽

Kegg Analysis

Abstract Background As the most diagnosed malignancy, lung cancer is also the primary cause of cancer death in the entire world. The therapy of lung adenocarcinoma (LUAD), which is the most prevalent subtype of lung cancer, draw researchers’ increasing attentions. This research aimed to investigate the tumor microenvironment (TME)-related hub genes which might be novel targets for treatment. Materials and methods LUAD-associated data packages, including RNA-Seq information and clinical data of 522 patients, were obtained from The Cancer Genome Atlas (TCGA). For better evaluation of stromal and immune cell components, immune scores, stromal scores and estimate scores were obtained with ESTIMATE algorithm based on gene expression levels in tumors. The R package heatmap and clustering analysis were used to explore interested genes. Differentially expressed genes (DEGs) were identified by Venn diagram. Protein-protein interaction (PPI) network was applied to explore intrinsic connections of DEGs. Kaplan-Meier (K-M) survival curves, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to investigate the prognostic values and intricate biological functions of DEGs. The relationships between 4 survival-related hub genes and 6 types of immune cells were examined using TIMER database. The LinkedOmics database was applied to look for kinase targets of hub genes. Results The immune/stromal/estimate scores were significantly correlated with clinical features, including the grades and sizes of LUAD, distant metastasis and outcomes. A total of 702 DEGs, 589 up-regulated and 113 down-regulated, were identified. GO and KEGG analysis showed that the DEGs had significant correlations with tumor immunology. PPI network suggested that the top 8 nodes were FPR2, C3AR1, MCHR1, CCR5, FPR1, CCL19, CCR2 and CXCL10. K-M survival curves indicated that FPR2, C3AR1, MCHR1 and CCR5, as hub genes, were significantly correlated with the overall survival (OS) of LUAD patients. The expression levels of C3AR1 and CCR5 were positively correlated with immune cell infiltration. LYN, LCK and SYK were the targeted kinases of these hub genes. Conclusion FPR2, C3AR1, MCHR1 and CCR5 were TME-related genes and potential biomarkers for the therapy and prognosis of LUAD.

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Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy

European Journal of Medical Research ◽

10.1186/s40001-020-00441-2 ◽

2020 ◽

Vol 25 (1) ◽

Author(s):

Xue Jiang ◽

Zhijie Xu ◽

Yuanyuan Du ◽

Hongyu Chen

Keyword(s):

Gene Ontology ◽

Inflammatory Response ◽

Molecular Mechanism ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Control Group ◽

Integrin Subunit ◽

Ppi Network ◽

Hub Genes ◽

Ppi Networks

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN. Results In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN. Conclusions We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

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Integrated Bioinformatics Analysis of Master Regulators in Anaplastic Thyroid Carcinoma

BioMed Research International ◽

10.1155/2019/9734576 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Zongfu Pan ◽

Lu Li ◽

Qilu Fang ◽

Yangyang Qian ◽

Yiwen Zhang ◽

...

Keyword(s):

Cell Cycle ◽

Thyroid Carcinoma ◽

Signaling Pathway ◽

Regulatory Network ◽

Anaplastic Thyroid Carcinoma ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes ◽

Normal Thyroid ◽

Master Regulators

Anaplastic thyroid carcinoma (ATC) is one of the most aggressive and rapidly lethal tumors. However, limited advances have been made to prolong the survival and to reduce the mortality over the last decades. Therefore, identifying the master regulators underlying ATC progression is desperately needed. In our present study, three datasets including GSE33630, GSE29265, and GSE65144 were retrieved from Gene Expression Omnibus with a total of 32 ATC samples and 78 normal thyroid tissues. A total of 1804 consistently changed differentially expressed genes (DEGs) were identified from three datasets. KEGG pathways enrichment suggested that upregulated DEGs were mainly enriched in ECM-receptor interaction, cell cycle, PI3K-Akt signaling pathway, focal adhesion, and p53 signaling pathway. Furthermore, key gene modules in PPI network were identified by Cytoscape plugin MCODE and they were mainly associated with DNA replication, cell cycle process, collagen fibril organization, and regulation of leukocyte migration. Additionally, TOP2A, CDK1, CCNB1, VEGFA, BIRC5, MAPK1, CCNA2, MAD2L1, CDC20, and BUB1 were identified as hub genes of the PPI network. Interestingly, module analysis showed that 8 out of 10 hub genes participated in Module 1 network and more than 70% genes of Module 2 consisted of collagen family members. Notably, transcription factors (TFs) regulatory network analysis indicated that E2F7, FOXM1, and NFYB were master regulators of Module 1, while CREB3L1 was the master regulator of Module 2. Experimental validation showed that CREB3L1, E2F7, and FOXM1 were significantly upregulated in ATC tissue and cell line when compared with normal thyroid group. In conclusion, the TFs regulatory network provided a more detail molecular mechanism underlying ATC occurrence and progression. TFs including E2F7, FOXM1, CREB3L1, and NFYB were likely to be master regulators of ATC progression, suggesting their potential role as molecular therapeutic targets in ATC treatment.

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Protein Expression Patterns of Candidate Genes Involved in Apoptosis and Cell Cycle Control in Genetic Subgroups of Chronic Lymphocytic Leukemia.

Blood ◽

10.1182/blood.v104.11.2796.2796 ◽

2004 ◽

Vol 104 (11) ◽

pp. 2796-2796

Author(s):

Christof Schneider ◽

Dirk Winkler ◽

Meike Loddenkemper ◽

Alexander Krober ◽

Peter Lichter ◽

...

Keyword(s):

Cell Cycle ◽

Protein Expression ◽

Cyclin D1 ◽

Candidate Genes ◽

Cell Lines ◽

Normal Karyotype ◽

Lymphocytic Leukemia ◽

Expression Levels ◽

Protein Levels ◽

Atm Protein

Abstract Chronic lymphocytic leukemia (CLL) is a heterogeneous disease with a highly variable clinical course. Genomic aberrations (such as 13q−, 11q−, +12q, 17p−) can be found in about 80% of CLL cases and define pathogenic as well as clinical subgroups. Similarly, the mutational status of the variable region of the immunoglobulin heavy-chain gene (VH) identifies subgroups with different maturation stage and clinical outcome. In this study protein expression levels of candidate genes involved in cell cycle and apoptosis control (p53, ATM, Akt1, PI3-K, p21, p27, cdk4, Cyclin-D1, D2, D3, Bax, Bcl-2, Apaf-1, Smac, XIAP, cIAP2, survivin) were examined by Western Blotting. A total of 87 CLL cases derived from the subgroups with 11q- (n=22), 17p-/p53 mutation (n=18), +12q (n=24), 13q- (n=8) or a normal karyotype (n=15) were studied and compared to the cell lines EHEB and JVM-2. VH-mutation status was available for 65 cases (unmutated n=48, mutated n=17). Due to limitations in sample availability not all proteins could be examined in all cases. A highly homogenous expression pattern for all the proteins studied was observed in the CLL subgroup with a normal karyotype. This pattern was independent of the VH-status. CLL samples with normal karyotype, +12q and 13q deletion showed equal levels of ATM as compared to EHEB and JVM-2. As compared to cases with a normal karyotype the ATM level within the 11q- subgroup was reduced in 5 cases and absent in 1 case among 11 evaluable 11q- cases. The 17p- subgroup was comprised of 3 cases with concomitant 17p- and 11q- and 15 cases with 17p- but no 11q-. The latter group showed ATM protein levels comparable to the levels of the normal karyotype group. In the group with 17p- and 11q- there was an ATM expression level similar to the groups with 17p- and normal karyotype in two cases while one case had a reduced ATM protein level comparable to the 11q- subgroup. All cases with 17p- exhibited a stronger expression of p53 as compared to the cell lines and all other cases, except for one case with normal karyotype and one with an 11q-. No p53 mutations could be detected in exons 5–9 by sequencing in these two cases. High levels of survivin protein were found in all cases with 17p- and/or 11q-, 13q-, +12q while the subgroup with a normal karyotype showed lower levels. High levels of cdk4 protein were expressed in cases with 17p-, 11q- and 13q- while cdk4 protein levels were low in the subgroup with +12q and normal karyotype. Regarding p21, p27, Bcl2, Bax, Smac, Apaf-1, Cyclin D1–D3, cIAP2, XIAP, Akt1 and PI3K no variation in the expression levels were observed across the genetic CLL subgroups. Comparing the CLL cases to the cell lines the differences in expression levels were found for the cell cycle regulators Cyclin D1, D2, D3, p21 and p27. While the cell lines showed strong protein levels for Cyclin D1, D2, D3 and p21, they were nearly absent in the CLL cases. Expression of p27 was higher in all CLL cases as compared to JVM-2 and EHEB. In conclusion, the 17q- subgroup was the only group with a high level of p53 protein expression indicating that p53 is the affected gene in this subgroup. In contrast, the ATM protein levels are reduced only in a part of the 11q- cases indicating a possible role of additional candidate genes. Cases with +12q and normal karyotype showed weak expression of cdk4 pointing out a possible function in these subgroups.

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Bioinformatics Approach to Identify Common Gene Signatures of Patients With Coronavirus 2019 and Lung Adenocarcinoma

10.21203/rs.3.rs-880952/v1 ◽

2021 ◽

Author(s):

Xiao Liang ◽

Yali Chen ◽

Yuchao Fan

Keyword(s):

Lung Adenocarcinoma ◽

Severe Disease ◽

Enrichment Analysis ◽

Immune Checkpoints ◽

Specific Gene ◽

Ppi Network ◽

Hub Genes ◽

Immune Infiltration ◽

Protein Protein Interaction ◽

Tf Gene

Abstract Coronavirus disease 2019 (COVID-19) continues as a global pandemic. Patients with lung cancer infected with COVID-19 may develop severe disease or die. Treating such patients severely burdens overwhelmed healthcare systems. Here we identified potential pathological mechanisms shared between patients with COVID-19 and lung adenocarcinoma (LUAD). Co-expressed, differentially expressed genes (DEGs) in patients with COVID-19 and LUAD were identified and used to construct a protein-protein interaction (PPI) network and to perform enrichment analysis. We used the NetworkAnalyst platform to establish a co-regulatory of the co-expressed DEGs, and we used Spearman’s correlation to evaluate the significance of associations of hub genes with immune infiltration and immune checkpoints. Analysis of three datasets identified 112 shared DEGs, which were used to construct a protein-PPI network. Subsequent enrichment analysis revealed co-expressed genes related to biological process (BP), molecular function (MF), cellular component (CC) as well as to pathways, specific organs, cells and diseases. Ten co-expressed hub genes were employed to construct a gene-miRNA, transcription factor (TF)-gene and TF-miRNA network. Hub genes were significantly associated with immune infiltration and immune checkpoints. Finally methylation level of hub genes in LUAD was obtained via UALCAN database. The present multi-dimensional study reveals commonality in specific gene expression by patients with COVID-19 and LUAD. These findings provide insights into developing strategies for optimising the management and treatment of patients with LUAD with COVID-19.

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Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis

PeerJ ◽

10.7717/peerj.10594 ◽

2021 ◽

Vol 9 ◽

pp. e10594

Author(s):

Qian Zhao ◽

Yan Zhang ◽

Shichun Shao ◽

Yeqing Sun ◽

Zhengkui Lin

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Correlation Analysis ◽

Bioinformatics Analysis ◽

Biological Pathways ◽

Gene Module ◽

Ppi Network ◽

Hub Genes ◽

Data Set ◽

Gene Modules

Background Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis. Methods A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003). Results Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05). Conclusion MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.

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Expression of RUNX1 and RUNX1T1 Correlates with Cytogenetic Subgroups in Pediatric AML: A Report From the Children's Oncology Group

Blood ◽

10.1182/blood.v120.21.1425.1425 ◽

2012 ◽

Vol 120 (21) ◽

pp. 1425-1425

Author(s):

Matthew A. Kutny ◽

Todd A. Alonzo ◽

Robert B. Gerbing ◽

Janet Franklin ◽

Susana C. Raimondi ◽

...

Keyword(s):

High Risk ◽

Mrna Expression ◽

High Expression ◽

Monosomy 7 ◽

Expression Levels ◽

Wide Range ◽

Risk Patients ◽

Race Ethnicity ◽

Standard Risk ◽

Runx1 Expression

Abstract Abstract 1425 Background: The t(8;21) translocation results in the fusion oncogene RUNX1-RUNX1T1 (also called AML1-ETO) and confers a favorable prognosis in both pediatric and adult AML. Little is known about the expression of RUNX1 and its translocation partner RUNX1T1 in myeloid leukemia. In this study we examined the mRNA expression of both genes in children with newly diagnosed AML and correlated patient clinical features with gene expression. Methods: We isolated mRNA from diagnostic specimens of 206 patients enrolled on COG AAML03P1. RUNX1 and RUNX1T1 mRNA expression was measured using qRT-PCR. GUSB gene served as control for mRNA quality and to standardize expression. Expression was correlated with patient and disease characteristics and clinical outcomes. Results: Relative RUNX1 expression ranged from 0.09 to 11.32 with a median of 1.45. The patient population was divided into quartiles (n=51, 52, 51, 52) with quartile 1 (Q1) having the lowest expression and quartile 4 (Q4) the highest expression. There was no significant difference in sex, age, race, ethnicity, hematologic parameters at diagnosis, or CNS status. Cytogenetic groups t(8;21), inv(16), abnormal 11q23, and monosomy 7 differed significantly in RUNX1 expression. The percentage of patients with t(8;21) decreased for increasing quartiles of RUNX1 expression with 28% of Q1 having t(8;21) compared to 12% of Q2, 8% of Q3 and 0% of Q4 (p=<0.001). Conversely, the percentage of patients with inv(16) increased for increasing quartiles of RUNX1 expression with only 2% of Q1 having inv(16) compared to 12% of Q2, 27% of Q3 and 20% of Q4 (p=0.006). Percentage of patients with abnormal 11q23 decreased for increasing quartiles of RUNX1 expression (Q1=36%, Q2=22%, Q3=15% and Q4=7%, p=0.003). All 4 patients with monosomy 7 had RUNX1 expression levels in Q4 (p=0.005). RUNX1 expression did not differ by FLT3-ITD, CEBPa mutant and NPM mutant. On recent COG studies, low risk includes core binding factor (CBF) patients and CEBPa or NPM mutant, high risk -5/5q-, −7 or FLT3-ITD high AR, and standard risk being all others. There was no difference in distribution of low or standard risk patients. The high risk group had higher RUNX1 expression attributable to the high expression in monosomy 7 (p=0.027). There was no difference across quartiles for rates of CR, 5 yr EFS or OS. Relative RUNX1T1 expression had a wide range from 0 to 139,786 with 69% of patients having no detectable expression. Due to the number of patients with no expression and the logarithmic distribution of expression, we divided the cohort into no expression (“exp=0”, n=143), expression levels >0 to 1 (“exp>0–1”, n=23) and expression >1 (“exp>1”, n=40). Among these 3 groups, there was no difference in sex, age, race, ethnicity, or CNS status. WBC at diagnosis was higher (median 38,000/μL) for exp=0 compared to exp>0–1 (median 14,100/μL) and exp>1 (median 15,800/μL) (p=0.045). RUNX1T1 expression differed significantly in patients with t(8;21) and monosomy 7. Patients with t(8;21) made up a larger proportion of the high RUNX1T1 expression group including 35% (13/37) having exp>1 compared to 0% of patients with exp>0–1 and 7% (10/134) with exp=0 (p=<0.001). Inv(16) patients showed trend toward the lower RUNX1T1 expression group but not significantly (p=0.11). Monosomy 7 patients clustered in the higher expression group accounting for 8% (3/37) of patients with exp>1 compared to 0% with exp>0–1 and 1% (1/134) with exp=0 (p=0.017). Expression did not differ in the molecular groups or risk groupings. Rates for CR, OS and EFS did not differ significantly by RUNX1T1 expression. However, there was a trend toward higher relapse risk among those with higher RUNX1T1 expression in standard risk patients (exp=0, RR=34±14%; exp>0–1, RR=28±33%; exp >1, RR=68%±33; p=0.138). Conclusion: Both RUNX1 and RUNX1T1 expression correlate with cytogenetic subgrouping, particularly in CBF and monosomy 7. Patients with inv(16) and monosomy 7 make up a larger percentage of the high RUNX1 expression patients but t(8;21) patients tend to be in the low expression groups. For RUNX1T1, t(8;21) and monosomy 7 patients had higher percentages in the high expression group, while inv(16) patients showed a trend toward the lower expression group. RUNX1 or RUNX1T1 expression levels alone did not predict OS or EFS, but further study is warranted to understand the role of high RUNX1 and RUNX1T1 expression in the rare high risk pediatric patients with monosomy 7. Disclosures: No relevant conflicts of interest to declare.

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Targeting BCL6-Mediated Drug-Resistance in High-Risk Childhood ALL

Blood ◽

10.1182/blood.v120.21.776.776 ◽

2012 ◽

Vol 120 (21) ◽

pp. 776-776

Author(s):

Christian Hurtz ◽

Parham Ramezani-Rad ◽

Huimin Geng ◽

Behzad Kharabi ◽

William L. Carroll ◽

...

Keyword(s):

Drug Resistance ◽

High Risk ◽

B Cell ◽

B Cell Lymphoma ◽

Direct Consequence ◽

High Expression ◽

Aberrant Expression ◽

Childhood All ◽

Expression Levels ◽

Bcl6 Expression

Abstract Abstract 776 Background: BCL6 is known as a protooncogene and transcriptional repressor in Diffuse Large B cell lymphoma (DLBCL), where BCL6 is frequently involved in chromosomal rearrangements. We recently found that BCL6 mediates a novel form of drug resistance to tyrosine kinase inhibitors (TKI) in Ph+ ALL and CML (Duy et al., Nature 2011; Hurtz et al., J Exp Med 2011). This was based on the finding that Ph+ ALL and CML cells respond to TKI-mediated inhibition of BCR-ABL1 kinase activity by upregulation of BCL6, which protects from p53-mediated apoptosis. Our current study was prompted by the finding that high expression levels of BCL6 represent a predictor of poor clinical outcome in children with Non-Ph ALL. We therefore performed detailed studies of expression and function of BCL6 in various subtypes of Non-Ph childhood ALL. Results: In a gene expression analysis of 207 children with high-risk B cell precursor ALL (COG P9906 trial), we found that high expression levels of BCL6 at the time of diagnosis represents a strong predictor of poor overall (OS; p=0.007) and relapse-free (RFS; p=0.02) survival. Also, high expression levels of BCL6 are predictive of positive minimal residual disease (MRD+) status on day 29 after the onset of chemotherapy. For 49 patients in this clinical trial, matched sample pairs at diagnosis and relapse were available. In these patients, BCL6 expression was significantly higher at relapse compared to diagnosis (p=0.003). We next studied BCL6 mRNA (n=122) and protein (n=21) levels in a larger cohort of primary childhood ALL patient samples and found that BCL6 expression levels are particularly high in MLL-AF4 infant ALL. This finding was unexpected and we tested if the MLL-AF4 oncogene drives aberrant BCL6 expression in these cells. First, ChIP-analysis revealed that the chimeric oncoprotein MLL-AF4 directly binds to the BCL6 promoter, suggesting that MLL-AF4 may indeed drive BCL6 expression. In support of this hypothesis, BCL6 Western blot analyses of inducible MLL-AF4-transgenic pro-B cells demonstrated that activation of the MLL-AF4 transgene is sufficient to induce ∼10-fold upregulation of BCL6 protein levels. We conclude that aberrant expression of BCL6 in childhood ALL can be the direct consequence of MLL-AF4 activity. To investigate the potential function of BCL6, we used a genetic mouse model of childhood ALL based on bone marrow precursor cells from BCL6−/− mice. Since mutations in the RAS pathway are found in about 30% of childhood ALL cases, we transformed B cell progenitor cells from BCL6−/− and wildtype mice with oncogenic NRASG12D, which represents the most frequent RAS lesion in B cell lineage leukemia. Surprisingly, BCL6-deficiency results in a failure of NRASG12D ALL cells to initiate leukemia, while NOD/SCID mice that were transplanted with BCL6 wildtype NRASG12DALL succumbed to the disease. Clinical relevance: To verify if these observations are relevant to human disease, we transduced primary human childhood ALL xenografts with MLL-AF4 gene rearrangement with a dominant-negative BCL6-mutant (BCL6-DN). Expression of BCL6-DN rapidly induced cell cycle arrest and cell death. To test if BCL6 inhibition is of potential use for children with MLL-AF4 leukemia, we used a recently developed retro-inverso BCL6 peptide inhibitor (RI-BPI, Cerchietti et al., Blood 2009). RI-BPI is able to inhibit BCL6 function and is currently under clinical development for the treatment of BCL6-dependent DLBCL. Treatment of the primary human MLL-AF4 ALL xenograft cells with RI-BPI compromised colony formation in methylcellulose and leukemia-initiation in transplant recipient mice and had a strong synergistic effect when combined with Vincristine, which represents the backbone for most high risk regimen in pediatric ALL. Conclusions: These findings identify BCL6 as a therapeutic target in high-risk childhood leukemia and its pharmacological inhibition as a novel strategy as therapeutic adjuvant. Interestingly, MLL-AF4 ALL cells exhibit constitutively high expression levels of BCL6. Aberrant expression of BCL6 in MLL-AF4 ALL is the direct consequence of MLL-AF4 activity in these cells. Based on these findings, we propose combinations of BCL6 inhibitors (e.g. RI-BPI) with standard chemotherapy as potential approach to reduce the risk of ALL relapse and improve overall outcome. Disclosures: No relevant conflicts of interest to declare.

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