Genome-scale integrated analysis to identify prospective molecular mechanisms and therapeutic targets in isocitrate dehydrogenase 2 R140Q-mutated acute myeloid leukemia
- Authors:
- Published online on: March 18, 2019 https://doi.org/10.3892/or.2019.7075
- Pages: 2876-2888
-
Copyright: © Huang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Acute myeloid leukemia (AML) is a cancer of the myeloid lineage hematopoietic cells, and is clonally heterogeneous since all myeloid precursors and progenitors can potentially undergo malignant transformation (1,2). The uncontrolled proliferation and accumulation of immature myeloid cells or blasts impair normal hematopoiesis, thereby increasing the risk of severe infections and hemorrhage (1,2). The genomic landscape of AML is highly diverse, and some mutations can be predictive of prognosis, as well as potential therapeutic targets. Most leukemias are characterized by specific oncoproteins encoded by fusion genes resulting from chromosomal translocations. Therefore, these oncoproteins not only serve as diagnostic markers of specific types of leukemia but also as potential therapeutic targets. In addition, some chromosomal abnormalities are associated with characteristic histopathological and clinical features, which form the cornerstone of the accurate diagnosis and treatment of leukemia (1). However, the heterogeneity of AML has stymied its targeted treatment, with current mortality rates well over 50% (1).
Isocitrate dehydrogenase 2 (IDH2) mutations are frequently observed in AML (3) and are not only potential targets for personalized therapies (4,5), but also indicators of AML prognosis (6,7). R140Q is the most frequent IDH2 mutation in AML, and is correlated to advanced age, normal karyotype, and the French-American-British (FAB) classification M2 at diagnosis (8). Wiseman et al observed that multi-lineage hematopoiesis from IDH2 R140Q clones was frequently reconstituted after chemotherapy in AML patients (9). However, the key genes and pathways related to IDH2 mutations in AML are not completely clear. In a previous study, we used RNA sequencing datasets from The Cancer Genome Atlas (TGCA) database and multiple bioinformatic analyses to identify potential molecular mechanisms in tumor protein p53 (TP53)-mutated AML, as well as potential prognostic biomarkers (10,11). The aim of the present study was to identify the potential molecular mechanisms and genes associated with IDH2 R140Q mutation in adult de novo AML using a similar approach, in addition to potential targeted therapeutic drugs using the Connectivity Map (CMap).
Materials and methods
RNA-seq data of adult de novo AML patients
The RNA-seq dataset of the bone marrow tissues of an adult de novo AML patient cohort collected at diagnosis, as well as the corresponding survival information, were obtained from TCGA (https://gdc-portal.nci.nih.gov/; accessed August 10, 2018) database (12). The corresponding information on IDH2 R140Q mutation status was obtained from the cBioPortal for Cancer Genomics website (http://www.cbioportal.org/index.do; accessed August 10, 2018) (13,14). Since all the data in this study were retrieved from TCGA, the present study did not require the approval of the ethics committee. The RNA-seq data are available on a public domain, and were acquired and analyzed according to the published guidelines of TCGA (https://cancergenome.nih.gov/publications/publicationguidelines).
Identification of differentially expressed genes (DEGs) and their prognostic value in AML
DEGs between the IDH2 R140Q-mutated and wild-type adult de novo AML were identified by edgeR using the following criteria: |log2 fold change (FC) | ≥1, and both P-value and false discovery rate (FDR) <0.05 (15,16). The heat map and volcano plot of the DEGs were generated using the ‘gplots’ package in R platform, and their prognostic value in AML was determined using the ‘survival’ package in the R platform (17,18).
Functional assessment
Functional assessment of the DEGs, in terms of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, was performed by Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 (https://david.ncifcrf.gov/tools.jsp; accessed August 10, 2018) (19,20) and those with P-values <0.05 were considered statistically significant. The directed acyclic graph of GO terms was drawn using the Biological Networks Gene Ontology (BiNGO) tool in Cytoscape_v3.6.1, a plugin used to assess the overrepresentation of GO categories in biological networks (21).
Construction of protein-protein and gene-gene interaction networks
The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (http://string.embl.de/; accessed August 10, 2018) was used to construct the protein-protein interaction (PPI) networks (22–24), and GeneMANIA (http://genemania.org/; accessed August 10, 2018) was used for gene-gene interaction (GGI) networks (25,26).
Connectivity Map analysis
Connectivity Map (CMap, https://portals.broadinstitute.org/cmap/; accessed August 10, 2018) is an online tool and data source for analyzing the mechanism of action and localization of drugs based on transcriptome data (27,28). A positive score indicates an inducement effect of a small-molecule drug on the query signatures, and a negative score reflects a repression effect. CMap was used to screen for putative small-molecule drugs against IDH2 R140Q-mutated AML, and those with a connective score < −0.2 were identified as the potential therapeutic drugs. The chemical structures of these drugs were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/; accessed August 10, 2018) (29,30) and the Search Tool for Interacting Chemicals (STITCH: http://stitch.embl.de/; accessed August 10, 2018) was used to construct protein-chemical interaction networks based on the existing literature and databases (31–33).
Genome-wide co-expression network analysis
To assess the biological relevance of the IDH2 R140Q mutation in AML, the IDH2 co-expressing genes were screened using the Pearson correlation test. The genome-wide co-expression analysis was performed using the ‘cor’ function in the R platform, and the genes with a |Pearson correlation coefficient| >0.75 and P<0.05 were identified as the IDH2 R140Q co-expressing genes. In addition, the prognostic values of these genes was evaluated by the ‘survival’ package in the R platform (17,18).
Statistical analysis
The IDH2 mRNA expression levels in the R140Q-mutated and wild-type AML bone marrow tissues was compared by the Student's t-test. The clinical outcomes of the R140Q-mutated and wild-type AML patients was compared by the Kaplan-Meier method and log-rank test. Hazard ratio (HR) and 95% confidence interval (CI) were calculated using the Cox proportional hazards regression model. FDR in edgeR was calculated by multiple testing with the Benjamini-Hochberg procedure (34–36). P-value <0.05 was regarded as statistically significant. All statistical analyses were conducted using the SPSS version 20.0 (IBM Corp., Armonk, NY, USA) and R 3.5.0 (https://www.r-project.org/; accessed June 21, 2018).
Results
Patient data
RNA-seq datasets from 151 AML patients were downloaded from TCGA (12), and data of 3 patients with a second IDH2 mutation was excluded. Thirteen of the remaining 148 patients had the IDH2 R140Q mutation and 135 patients were IDH2 wild-type. Their corresponding survival information, including overall survival duration and survival status, was also download from TCGA. The survival information for 11 patients (10 IDH2 wild-type and 1 IDH2 R140Q-mutated) was missing. The bioinformatic analysis flowchart for the present study is shown in Fig. 1.
DEG screening
A total of 230 DEGs were identified between the IDH2 wild-type and R140Q-mutated AML samples (Table SI), of which 133 were downregulated and 97 were upregulated in the bone marrow of the IDH2 R140Q-mutated AML patients. The heat map and volcano plot of the DEGs are shown in Fig. S1 and Fig. 2, respectively.
Association of the DEGs with survival
To investigate the prognostic value of the IDH2 R140Q-specific DEGs, we stratified the patients into a high- and low-expression groups according to the median values of each of these DEGs, and compared their survival status. Due to the incomplete survival information, we could not perform a multivariate Cox proportional risk regression model, and therefore assessed their prognostic values through a univariate analysis. Thirty-one DEGs were significantly associated with the OS (Table SII), and the top 10 significant prognostic DEGs were tetraspanin 10 (TSPAN10), pleckstrin homology domain containing A5 (PLEKHA5), coiled-coil domain containing 198 (CCDC198, also known as C14orf105), pleckstrin homology domain containing A6 (PLEKHA6), A-kinase anchoring protein 12 (AKAP12), gamma-aminobutyric acid type A receptor delta subunit (GABRD), fucosyltransferase 1 (FUT1), potassium voltage-gated channel interacting protein 3 (KCNIP3), immunoglobulin superfamily DCC subclass member 4 (IGDCC4) and podocalyxin like 2 (PODXL2) (Fig. 3A-J).
Functional assessment of DEGs
The DEGs were functionally assessed using the GO terms and KEGG pathways. The significantly enriched GO terms were angiogenesis, cell differentiation, cell-matrix adhesion, homophilic cell adhesion via plasma membrane adhesion molecules, cell junction, signal transduction, phosphoinositide phospholipase C activity and integrin binding (Fig. 4A). KEGG analysis showed that the DEGs were significantly enriched in ECM-receptor interaction, focal adhesion and pathways in cancer (Fig. 4B). The BiNGO analysis also demonstrated that these DEGs were significantly enriched in regulation of calcium ion-dependent exocytosis, cell differentiation, signal transmission, signaling process and phosphoinositide phospholipase C activity (Fig. S2).
GGI and PPI interaction network construction of DEGs
The GGI network indicated complex interactions between the DEGs (Fig. 5), and the top 10 degree genes that were identified as the hub genes were collagen type IV α2 chain (COL4A2), lumican (LUM), fibulin 2 (FBLN2), thrombospondin 2 (THBS2), lysyl oxidase like 1 (LOXL1), myosin light chain 9 (MYL9), heparan sulfate proteoglycan 2 (HSPG2), gamma-glutamyltransferase 5 (GGT5), integrin subunit alpha 1 (ITGA1), protein tyrosine phosphatase, receptor type M (PTPRM), and leucine rich repeat containing 17 (LRRC17); the highest degree was that of COL4A2 (degree=127). The PPI network supported the GGI results, and substantiated complex interactions among these DEGs at the protein level as well (Fig. S3). The top 10 degree genes identified as hub genes in the PPI networks were insulin receptor related receptor (INSRR), LUM, AXL receptor tyrosine kinase (AXL), cyclin D1 (CCND1), CDC42 binding protein kinase beta (CDC42BPB), cyclin dependent kinase 15 (CDK15), nitric oxide synthase 2 (NOS2), t-complex-associated-testis-expressed 1 (TCTE1), SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 1 (SMARCA1) and espin (ESPN); the highest degree was that of INSRR (degree=39). The LUM gene was common to both GGI and PPI networks, indicating its critical role in IDH2 R140Q-mutated AML.
CMap analysis
The CMap analysis was conducted to screen for small-molecule drugs targeting the IDH2 R140Q mutation. Thirteen drugs were identified: arachidonyltrifluoromethane, vanoxerine, vancomycin, vinblastine, thioperamide, nystatin, meteneprost, etiocholanolone, finasteride, PF-00539745-00, benzocaine, felbinac and brinzolamide (Table I and Fig. 6). (PF-00539745-00 does not appear in the figure as its chemical structure is not available in PubChem). The protein-chemical interaction networks of 11 drugs were obtained from STITCH (Fig. 7), and cytochrome P450 family 2 subfamily C member 8 (CYP2C8), NOS2 and adrenoceptor α 2A (ADRA2A) were common to the DEGs and protein-chemical interaction networks. In addition, KEGG analysis showed that ADRA2A was involved in the neuroactive ligand-receptor interaction pathway, and NOS2 in pathways in cancer and amoebiasis pathway. Based on these findings, we hypothesized that nystatin is a potential therapeutic agent against IDH2 R140Q-mutated AML that acts through the nystatin-NOS2 pathway to regulate cancer/amoebiasis pathways. Similarly, thioperamide possibly functions through the thioperamide-ADRA2A-neuroactive ligand-receptor interaction pathway axis, and vanoxerine through the vanoxerine-CYP2C8 axis.
 | Table I.Potential targeted therapeutic drugs for IDH2 R140Q-mutated AML based on Connectivity Map analysis. |
Genome-wide co-expression network analysis
Genome-wide co-expression network analysis showed that 542 genes were co-expressed with IDH2 in the IDH2 R140Q-mutated AML bone marrow tissues, of which 455 were positively correlated and 87 were negatively correlated with IDH2 R140Q (Fig. 8 and Table SIII). GO enrichment analysis showed that IDH2 and the co-expressed genes were significantly enriched in DNA replication, DNA repair, cell-cell adhesion, cell division and cell cycle-related biological processes (Table II and Table SIV), while KEGG analysis showed significant enrichment in DNA replication, SNARE interactions in vesicular transport and base excision repair pathway (Table II and Table SV). Furthermore, these results were verified by BiNGO, which indicated significant enrichment in the cell cycle process and DNA replication (Fig. S4). GGI (Fig. 9) and PPI (Fig. S5) interaction networks were respectively constructed using geneMANIA and STRING, and further verified the relationship between IDH2 and the co-expressing genes. The prognostic value of these co-expressing genes in AML was determined using the ‘survival’ package in the R platform, and 56 genes were significantly associated with the OS (Table SVI). The top ten significantly prognostic genes were splicing factor 3a subunit 1 (SF3A1), centromere protein V (CENPV), interferon stimulated exonuclease gene 20 like 2 (ISG20L2), prohibitin (PHB), glycine cleavage system protein H (GCSH), heat shock transcription factor 2 (HSF2), zinc finger protein 19 (ZNF19), EARP complex and GARP complex interacting protein 1 (EIPR1, also known as TSSC1), enoyl-CoA hydratase, short chain 1 (ECHS1), and nucleic acid binding protein 2 (NABP2). The Kaplan-Meier curves of the patients stratified into the low-and high-expressing groups are shown in Fig. 10A-J.
Survival analysis of IDH2
To better understand the role of the IDH2 R140Q mutation in AML, we compared the expression levels of the wild-type and mutated IDH2, and observed no significant differences (Fig. 11A). Furthermore, the OS of the AML patients harboring the wild-type or IDH2 R140Q mutant was also not significantly different (Fig. 11B).
Discussion
Although studies have reported fewer mutations in acute myeloid leukemia (AML) compared to other cancers, those specifically related to AML pathogenesis and prognoses have not been fully elucidated. An integrative analysis based on multi-genomic data is necessary to determine the relationship between specific gene mutations and cancer progression. Isocitrate dehydrogenase (IDH) is a family of enzymes that catalyze the oxidative decarboxylation of isocitrate to α-ketoglutarate and NADPH in the tricarboxylic acid (TCA) cycle. The human genome has five IDH genes that encode three isozymes, IDH1, IDH2 and IDH3 of which IDH2 is mitochondrial and frequently mutated in different cancers, including AML, brain tumors and gliomas (4). The most common mutations in IDH are located at R132 (IDH1), R140 and R172 (IDH2) (3,8,37). In the present study, the RNA-seq dataset from The Cancer Genome Atlas (TCGA) was utilized to identify the key genes and pathways associated with the IDH2 R140Q mutation in adult de novo AML for the first time using multiple bioinformatic methods. In addition, potential small-molecule drugs targeting the mutated IDH2 were identified.
Functional assessment of the IDH2 R140Q-specific differentially expressed genes (DEGs) showed significant association with angiogenesis, cell differentiation, cell-matrix adhesion, homophilic cell adhesion via plasma membrane adhesion molecules and cell junction. Previous studies have shown a prognostic role of marrow angiogenesis-associated factors in AML patients, and a promising therapeutic role of angiogenesis inhibition (38–40). IDH mutations induce tumorigenesis by epigenetic alterations, as well as by disrupting the tricarboxylic acid (TCA) cycle and activating hypoxia-related signaling pathways, which increase anabolic processes and angiogenesis (41,42). Functional assessment of IDH2 co-expressing genes showed enrichment in DNA replication, DNA repair, cell-cell adhesion, cell division, and cell cycle-related processes. Therefore, the biological and clinical manifestations of IDH2 R140Q mutation are distinct from wild-type AML at the cellular level. Further studies are needed to validate the functions of these IDH2 co-expressing genes.
Among the hub differentially expressed genes (DEGs) identified by protein-protein interaction (PPI) and gene-gene interaction (GGI), several have been correlated to AML, especially in patients with numerous mutations. Previous studies have identified MYH9 as a direct target of RUNX1, an important transcription factor that regulates the differentiation of hematopoietic stem cells into mature blood cells, and is frequently mutated in AML (43,44). Zhang et al reported downregulation of CCND1 in an AML cell line treated with amifostine, and may therefore be its direct target (45). AXL is essential for the constitutive Fms-like tyrosine kinase-3 (FLT3) phosphorylation in FLT3-internal tandem duplication AML, which is responsible for the aberrant blast cell proliferation and the clinical outcome of AML, and its inhibition decreases FLT3 phosphorylation (46). Therefore, AXL is a potential target for treating FLT3-mutated AML, and its specific inhibitor BGB324 has been tested against leukemia cell proliferation and therapy resistance (47,48). AKAP12 is a tumor-suppressor gene that is inactivated in childhood myeloid malignancies via epigenetic silencing through promoter DNA methylation (49). SF3A1 is a core spliceosomal gene that plays a fundamental role in the processing of nascent RNA transcripts. Although a somatic mutation of SF3A1 has been reported in AML, its rarity has precluded analysis of its function and molecular mechanism (50,51). In the present study, the prognostic DEGs and IDH2 co-expressing genes were identified based on a single cohort of TCGA. Therefore, our results have to be verified in additional larger cohorts with complete clinical parameters.
Although we identified 13 candidate drugs targeting the IDH2 R140Q mutation, we could not find any evidence of their application against AML that were based on previous studies. Vinblastine is one of the most widely used plant-derived chemotherapeutic agents used to treat cancers, including AML. The combination of cytosine arabinoside, VP 16–213, vincristine and vinblastine (A-triple-V) is used to treat AML relapse, and results in complete remission in most cases (52,53). Finasteride is a 5-a-reductase inhibitor used for the treatment of alopecia and prostate cancer. Chau et al demonstrated that while finasteride may not have a dose-dependent effect on prostate cancer, it may decrease the risk (54). One of the potential mechanisms of finasteride action in prostate cancer is the inhibition of cancer cell invasion and metastasis by downregulation of matrix metalloproteinase (MMP)2 and MMP9 (55). Wiebe et al reported that progesterone-induced stimulation of mammary tumorigenesis can also be inhibited by finasteride (56). In addition, it also decreased melanogenesis in both melanocytes and melanoma cells by inhibiting adenylate cyclase and the melanocortin 1 receptor (57). The potential therapeutic effects of these drugs on AML need to be studied further.
The present study has some limitations that need to be clarified. First, our results are from a single cohort of TCGA and generated by bioinformatic analysis, and thus need to be verified in additional cohorts, as well as in experimental studies, such as in vitro validation. Second, due to the relatively small sample size of our cohort, and only 13 patients with the IDH2 R140Q mutation, the results generated in this study still need to be further verified using a larger sample size in future research. Third, we did not observe any significant differences in the IDH2 mRNA expression levels and OS duration between the IDH2 R140Q mutant and wild-type AML patients, which may be due to the small sample size. Despite these limitations, we identified multiple genes associated with the IDH2 R140Q mutation, and its potential molecular mechanisms in adult de novo AML. In addition, we also identified 13 drugs specific for IDH2 R140Q-mutated AML, which once verified can be used for AML patients harboring this mutation.
In conclusion, we identified several prognostic biomarkers and potential molecular mechanisms, that may play an essential role in IDH2 R140Q-mutated adult de novo AML and improve prognosis, as well as 13 candidate drugs for IDH2 R140Q-mutated adult de novo AML. However, our findings need to be verified by further experimental and clinical studies for future clinical application.
Supplementary Material
Supporting Data
Acknowledgements
The authors thank the contributors of The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and the cBioPortal for Cancer Genomics (http://www.cbioportal.org/index.do) for their contribution to share the AML dataset on open access. In addition, we also would like to acknowledge the helpful comments on this paper received from our reviewers.
Funding
The present study was supported in part by the National Natural Science Foundation of China (no. 81160075), the Natural Science Foundation of Guangxi (no. 0728124) and the Self-raised Scientific Research Fund of the Ministry of Health of Guangxi Province (Z2014035).
Availability of data and materials
The datasets used during the present study are available from the corresponding author upon reasonable request. All raw IDH2 mutation and RNA-seq dataset of AML, which were included in the present study, can be downloaded from TCGA (https://portal.gdc.cancer.gov/) and cBioPortal for Cancer Genomics (http://www.cbioportal.org/index.do).
Authors' contributions
RH, XiL and QL acquired the data and created a draft of the manuscript; RH, XiL, JL, JW, XS, XiaL, BL, FZ, YH and QL conducted and further performed the study, processed and analyzed the data; RH and QL revised and approved the final version of the manuscript. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Ethics approval and consent to participate
This study does not contain any studies with human participants or animals performed by any of the authors. Since all AML dataset included in this manuscript were obtained from The Cancer Genome Atlas and cBioPortal for Cancer Genomics, therefore, additional approval by an Ethics Committee is not necessary. In addition, the procedures of this manuscript were in accordance with the Helsinki declaration of 1964 and its later amendments.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Authors' information
Professor Qiaochuan Li: ORCID: https://orcid.org/0000-0001-6255-8155.
References
|
Short NJ, Rytting ME and Cortes JE: Acute myeloid leukaemia. Lancet. 392:593–606. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Estey E and Döhner H: Acute myeloid leukaemia. Lancet. 368:1894–1907. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Paschka P, Schlenk RF, Gaidzik VI, Habdank M, Krönke J, Bullinger L, Späth D, Kayser S, Zucknick M, Götze K, et al: IDH1 and IDH2 mutations are frequent genetic alterations in acute myeloid leukemia and confer adverse prognosis in cytogenetically normal acute myeloid leukemia with NPM1 mutation without FLT3 internal tandem duplication. J Clin Oncol. 28:3636–3643. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Dang L, Yen K and Attar EC: IDH mutations in cancer and progress toward development of targeted therapeutics. Ann Oncol. 27:599–608. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Shih AH, Meydan C, Shank K, Garrett-Bakelman FE, Ward PS, Intlekofer AM, Nazir A, Stein EM, Knapp K, Glass J, et al: Combination targeted therapy to disrupt aberrant oncogenic signaling and reverse epigenetic dysfunction in IDH2- and TET2-mutant acute myeloid leukemia. Cancer Discov. 7:494–505. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Green CL, Evans CM, Zhao L, Hills RK, Burnett AK, Linch DC and Gale RE: The prognostic significance of IDH2 mutations in AML depends on the location of the mutation. Blood. 118:409–412. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
DiNardo CD, Ravandi F, Agresta S, Konopleva M, Takahashi K, Kadia T, Routbort M, Patel KP, Mark Brandt, Pierce S, et al: Characteristics, clinical outcome, and prognostic significance of IDH mutations in AML. Am J Hematol. 90:732–736. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Chao HY, Jia ZX, Chen T, Lu XZ, Cen L, Xiao R, Jiang NK, Ying JH, Zhou M and Zhang R: IDH2 mutations are frequent in Chinese patients with acute myeloid leukemia and associated with NPM1 mutations and FAB-M2 subtype. Int J Lab Hematol. 34:502–509. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Wiseman DH, Williams EL, Wilks DP, Sun Leong H, Somerville TD, Dennis MW, Struys EA, Bakkali A, Salomons GS and Somervaille TC: Frequent reconstitution of IDH2(R140Q) mutant clonal multilineage hematopoiesis following chemotherapy for acute myeloid leukemia. Leukemia. 30:1946–1950. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Huang R, Liao X and Li Q: Identification of key pathways and genes in TP53 mutation acute myeloid leukemia: Evidence from bioinformatics analysis. OncoTargets Ther. 11:163–173. 2017. View Article : Google Scholar | |
|
Huang R, Liao X and Li Q: Identification and validation of potential prognostic gene biomarkers for predicting survival in patients with acute myeloid leukemia. OncoTargets Ther. 10:5243–5254. 2017. View Article : Google Scholar | |
|
Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A, Hoadley K, Triche TJ Jr, Laird PW, Baty JD, et al Cancer Genome Atlas Research Network, : Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 368:2059–2074. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al: The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2:401–404. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 6:pl12013. View Article : Google Scholar : PubMed/NCBI | |
|
Robinson MD, McCarthy DJ and Smyth GK: edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 26:139–140. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
McCarthy DJ, Chen Y and Smyth GK: Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40:4288–4297. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Liao X, Yang C, Huang R, Han C, Yu T, Huang K, Liu X, Yu L, Zhu G, Su H, et al: Identification of potential prognostic long non-coding rna biomarkers for predicting survival in patients with hepatocellular carcinoma. Cell Physiol Biochem. 48:1854–1869. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Wei HT, Guo EN, Liao XW, Chen LS, Wang JL, Ni M and Liang C: Genome-scale analysis to identify potential prognostic microRNA biomarkers for predicting overall survival in patients with colon adenocarcinoma. Oncol Rep. 40:1947–1958. 2018.PubMed/NCBI | |
|
Huang W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Jiao X, Sherman BT, Huang W, Stephens R, Baseler MW, Lane HC and Lempicki RA: DAVID-WS: A stateful web service to facilitate gene/protein list analysis. Bioinformatics. 28:1805–1806. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Maere S, Heymans K and Kuiper M: BiNGO: A Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 21:3448–3449. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43:D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45(D1): D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, Jouffre N, Huynen MA and Bork P: STRING: Known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 33:D433–D437. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, et al: The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38 (Suppl 2):W214–W220. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Mostafavi S, Ray D, Warde-Farley D, Grouios C and Morris Q: GeneMANIA: A real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9 (Suppl 1):S42008. View Article : Google Scholar : PubMed/NCBI | |
|
Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, et al: The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science. 313:1929–1935. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Lamb J: The Connectivity Map: A new tool for biomedical research. Nat Rev Cancer. 7:54–60. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, et al: PubChem substance and compound databases. Nucleic Acids Res. 44:D1202–D1213. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Wang Y, Bryant SH, Cheng T, Wang J, Gindulyte A, Shoemaker BA, Thiessen PA, He S and Zhang J: PubChem BioAssay: 2017 update. Nucleic Acids Res. 45:D955–D963. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Kuhn M, von Mering C, Campillos M, Jensen LJ and Bork P: STITCH: Interaction networks of chemicals and proteins. Nucleic Acids Res. 36:D684–D688. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Kuhn M, Szklarczyk D, Franceschini A, Campillos M, von Mering C, Jensen LJ, Beyer A and Bork P: STITCH 2: An interaction network database for small molecules and proteins. Nucleic Acids Res. 38 (Suppl 1):D552–D556. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P and Kuhn M: STITCH 5: Augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 44:D380–D384. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Benjamini Y and Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 57:289–300. 1995. | |
|
Reiner A, Yekutieli D and Benjamini Y: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics. 19:368–375. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Benjamini Y, Drai D, Elmer G, Kafkafi N and Golani I: Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 125:279–284. 2001. View Article : Google Scholar : PubMed/NCBI | |
|
Ahmad F, Mohota R, Sanap S, Mandava S and Das BR: Molecular evaluation of DNMT3A and IDH1/2 gene mutation: Frequency, distribution pattern and associations with additional molecular markers in normal karyotype Indian acute myeloid leukemia patients. Asian Pac J Cancer Prev. 15:1247–1253. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Lee CY, Tien HF, Hu CY, Chou WC and Lin LI: Marrow angiogenesis-associated factors as prognostic biomarkers in patients with acute myelogenous leukaemia. Br J Cancer. 97:877–882. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Trujillo A, McGee C and Cogle CR: Angiogenesis in acute myeloid leukemia and opportunities for novel therapies. J Oncol. 2012:1286082012. View Article : Google Scholar : PubMed/NCBI | |
|
Hussong JW, Rodgers GM and Shami PJ: Evidence of increased angiogenesis in patients with acute myeloid leukemia. Blood. 95:309–313. 2000.PubMed/NCBI | |
|
Krell D, Mulholland P, Frampton AE, Krell J, Stebbing J and Bardella C: IDH mutations in tumorigenesis and their potential role as novel therapeutic targets. Future Oncol. 9:1923–1935. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Schaap FG, French PJ and Bovée JV: Mutations in the isocitrate dehydrogenase genes IDH1 and IDH2 in tumors. Adv Anat Pathol. 20:32–38. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Bluteau D, Glembotsky AC, Raimbault A, Balayn N, Gilles L, Rameau P, Nurden P, Alessi MC, Debili N, Vainchenker W, et al: Dysmegakaryopoiesis of FPD/AML pedigrees with constitutional RUNX1 mutations is linked to myosin II deregulated expression. Blood. 120:2708–2718. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Okuda T, Nishimura M, Nakao M and Fujita Y: RUNX1/AML1: A central player in hematopoiesis. Int J Hematol. 74:252–257. 2001. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang F, Yang B, Zhang K, Hou ML, Lu XC and Li YX: CCND1-BCL2 Gene Network: A direct target of Amifostine in human acute megakaryocytic leukemia cells. Chem Biol Drug Des. 89:681–693. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Park IK, Mishra A, Chandler J, Whitman SP, Marcucci G and Caligiuri MA: Inhibition of the receptor tyrosine kinase Axl impedes activation of the FLT3 internal tandem duplication in human acute myeloid leukemia: Implications for Axl as a potential therapeutic target. Blood. 121:2064–2073. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Janning M, Ben-Batalla I and Loges S: Axl inhibition: A potential road to a novel acute myeloid leukemia therapy? Expert Rev Hematol. 8:135–138. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Ben-Batalla I, Schultze A, Wroblewski M, Erdmann R, Heuser M, Waizenegger JS, Riecken K, Binder M, Schewe D, Sawall S, et al: Axl, a prognostic and therapeutic target in acute myeloid leukemia mediates paracrine crosstalk of leukemia cells with bone marrow stroma. Blood. 122:2443–2452. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Flotho C, Paulun A, Batz C and Niemeyer CM: AKAP12, a gene with tumour suppressor properties, is a target of promoter DNA methylation in childhood myeloid malignancies. Br J Haematol. 138:644–650. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Yoshida K, Sanada M, Shiraishi Y, Nowak D, Nagata Y, Yamamoto R, Sato Y, Sato-Otsubo A, Kon A, Nagasaki M, et al: Frequent pathway mutations of splicing machinery in myelodysplasia. Nature. 478:64–69. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Larsson CA, Cote G and Quintás-Cardama A: The changing mutational landscape of acute myeloid leukemia and myelodysplastic syndrome. Mol Cancer Res. 11:815–827. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Sauter C, Fehr J, Frick P, Gmuer J, Honegger H and Martz G: Acute myelogenous leukemia: Successful treatment of relapse with cytosine arabinoside, VP 16–213, vincristine and vinblastine (A-triple-V). Eur J Cancer Clin Oncol. 18:733–737. 1982. View Article : Google Scholar : PubMed/NCBI | |
|
Müller MR, Sauter C, Erni J and Martz G: Influence of a new relapse treatment for acute myeloid leukemia (AML) on in vitro granulopoiesis. Anticancer Res. 3:127–131. 1983.PubMed/NCBI | |
|
Chau CH, Price DK, Till C, Goodman PJ, Chen X, Leach RJ, Johnson-Pais TL, Hsing AW, Hoque A, Tangen CM, et al: Finasteride concentrations and prostate cancer risk: Results from the Prostate Cancer Prevention Trial. PLoS One. 10:e01266722015. View Article : Google Scholar : PubMed/NCBI | |
|
Moroz A, Delella FK, Almeida R, Lacorte LM, Fávaro WJ, Deffune E and Felisbino SL: Finasteride inhibits human prostate cancer cell invasion through MMP2 and MMP9 downregulation. PLoS One. 8:e847572013. View Article : Google Scholar : PubMed/NCBI | |
|
Wiebe JP, Rivas MA, Mercogliano MF, Elizalde PV and Schillaci R: Progesterone-induced stimulation of mammary tumorigenesis is due to the progesterone metabolite, 5α-dihydroprogesterone (5αP) and can be suppressed by the 5α-reductase inhibitor, finasteride. J Steroid Biochem Mol Biol. 149:27–34. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Seo JO, Yumnam S, Jeong KW and Kim SY: Finasteride inhibits melanogenesis through regulation of the adenylate cyclase in melanocytes and melanoma cells. Arch Pharm Res. 41:324–332. 2018. View Article : Google Scholar : PubMed/NCBI |
